explorers: 77
This data as json
rowid | slug | config | createdAt | updatedAt | lastEditedByUserId | lastEditedAt | commitMessage | tsv | isPublished |
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77 | poverty-lis | {"blocks": [{"args": [], "type": "graphers", "block": [{"tab": "map", "note": "This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices.", "title": "Poverty: Share of population living on less than $1 a day (after tax)", "ySlugs": "headcount_ratio_dhi_eq_100", "subtitle": "This data is adjusted for inflation and for differences in living costs between countries. Income here is measured after taxes and benefits. 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It captures the depth of poverty of those living on less than $10 a day. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.", "yAxisMin": "0", "hasMapTab": "true", "tableSlug": "lis_vars", "Indicator Dropdown": "Average shortfall (% of poverty line)", "Poverty line Dropdown": "$10 per day", "Income measure Dropdown": "After tax", "Adjust for cost sharing within households (equivalized income) Checkbox": "true"}, {"tab": "map", "note": "This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries.", "title": "Average shortfall from a poverty line of $20 a day (as a share of the poverty line) (after tax)", "ySlugs": "income_gap_ratio_dhi_eq_2000", "subtitle": "This is the average shortfall expressed as a share of the poverty line, sometimes called the 'income gap ratio'. It captures the depth of poverty of those living on less than $20 a day. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.", "yAxisMin": "0", "hasMapTab": "true", "tableSlug": "lis_vars", "Indicator Dropdown": "Average shortfall (% of poverty line)", "Poverty line Dropdown": "$20 per day", "Income measure Dropdown": "After tax", "Adjust for cost sharing within households (equivalized income) Checkbox": "true"}, {"tab": "map", "note": "This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries.", "title": "Average shortfall from a poverty line of $30 a day (as a share of the poverty line) (after tax)", "ySlugs": "income_gap_ratio_dhi_eq_3000", "subtitle": "This is the average shortfall expressed as a share of the poverty line, sometimes called the 'income gap ratio'. It captures the depth of poverty of those living on less than $30 a day. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.", "yAxisMin": "0", "hasMapTab": "true", "tableSlug": "lis_vars", "Indicator Dropdown": "Average shortfall (% of poverty line)", "Poverty line Dropdown": "$30 per day", "Income measure Dropdown": "After tax", "Adjust for cost sharing within households (equivalized income) Checkbox": "true"}, {"tab": "map", "note": "This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries.", "title": "Average shortfall from a poverty line of $40 a day (as a share of the poverty line) (after tax)", "ySlugs": "income_gap_ratio_dhi_eq_4000", "subtitle": "This is the average shortfall expressed as a share of the poverty line, sometimes called the 'income gap ratio'. It captures the depth of poverty of those living on less than $40 a day. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.", "yAxisMin": "0", "hasMapTab": "true", "tableSlug": "lis_vars", "Indicator Dropdown": "Average shortfall (% of poverty line)", "Poverty line Dropdown": "$40 per day", "Income measure Dropdown": "After tax", "Adjust for cost sharing within households (equivalized income) Checkbox": "true"}, {"tab": "map", "note": "This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries.", "title": "Poverty gap index at $1 a day (after tax)", "ySlugs": "poverty_gap_index_dhi_eq_100", "subtitle": "The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.", "yAxisMin": "0", "hasMapTab": "true", "tableSlug": "lis_vars", "Indicator Dropdown": "Poverty gap index", "Poverty line Dropdown": "$1 per day", "Income measure Dropdown": "After tax", "Adjust for cost sharing within households (equivalized income) Checkbox": "true"}, {"tab": "map", "note": "This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries.", "title": "Poverty gap index at $2 a day (after tax)", "ySlugs": "poverty_gap_index_dhi_eq_200", "subtitle": "The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.", "yAxisMin": "0", "hasMapTab": "true", "tableSlug": "lis_vars", "Indicator Dropdown": "Poverty gap index", "Poverty line Dropdown": "$2 per day", "Income measure Dropdown": "After tax", "Adjust for cost sharing within households (equivalized income) Checkbox": "true"}, {"tab": "map", "note": "This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries.", "title": "Poverty gap index at $5 a day (after tax)", "ySlugs": "poverty_gap_index_dhi_eq_500", "subtitle": "The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.", "yAxisMin": "0", "hasMapTab": "true", "tableSlug": "lis_vars", "Indicator Dropdown": "Poverty gap index", "Poverty line Dropdown": "$5 per day", "Income measure Dropdown": "After tax", "Adjust for cost sharing within households (equivalized income) Checkbox": "true"}, {"tab": "map", "note": "This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries.", "title": "Poverty gap index at $10 a day (after tax)", "ySlugs": "poverty_gap_index_dhi_eq_1000", "subtitle": "The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.", "yAxisMin": "0", "hasMapTab": "true", "tableSlug": "lis_vars", "Indicator Dropdown": "Poverty gap index", "Poverty line Dropdown": "$10 per day", "Income measure Dropdown": "After tax", "Adjust for cost sharing within households (equivalized income) Checkbox": "true"}, {"tab": "map", "note": "This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries.", "title": "Poverty gap index at $20 a day (after tax)", "ySlugs": "poverty_gap_index_dhi_eq_2000", "subtitle": "The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.", "yAxisMin": "0", "hasMapTab": "true", "tableSlug": "lis_vars", "Indicator Dropdown": "Poverty gap index", "Poverty line Dropdown": "$20 per day", "Income measure Dropdown": "After tax", "Adjust for cost sharing within households (equivalized income) Checkbox": "true"}, {"tab": "map", "note": "This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries.", "title": "Poverty gap index at $30 a day (after tax)", "ySlugs": "poverty_gap_index_dhi_eq_3000", "subtitle": "The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.", "yAxisMin": "0", "hasMapTab": "true", "tableSlug": "lis_vars", "Indicator Dropdown": "Poverty gap index", "Poverty line Dropdown": "$30 per day", "Income measure Dropdown": "After tax", "Adjust for cost sharing within households (equivalized income) Checkbox": "true"}, {"tab": "map", "note": "This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries.", "title": "Poverty gap index at $40 a day (after tax)", "ySlugs": "poverty_gap_index_dhi_eq_4000", "subtitle": "The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.", "yAxisMin": "0", "hasMapTab": "true", "tableSlug": "lis_vars", "Indicator Dropdown": "Poverty gap index", "Poverty line Dropdown": "$40 per day", "Income measure Dropdown": "After tax", "Adjust for cost sharing within households (equivalized income) Checkbox": "true"}, {"tab": "chart", "note": "This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices.", "title": "Share of population living below a range of poverty lines (after tax)", "ySlugs": "headcount_ratio_dhi_eq_100 headcount_ratio_dhi_eq_200 headcount_ratio_dhi_eq_500 headcount_ratio_dhi_eq_1000 headcount_ratio_dhi_eq_2000 headcount_ratio_dhi_eq_3000 headcount_ratio_dhi_eq_4000", "subtitle": "This data is adjusted for inflation and for differences in living costs between countries. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.", "yAxisMin": "0", "hasMapTab": "false", "tableSlug": "lis_vars", "Indicator Dropdown": "Share in poverty", "Poverty line Dropdown": "Multiple lines", "selectedFacetStrategy": "entity", "Income measure Dropdown": "After tax", "Adjust for cost sharing within households (equivalized income) Checkbox": "true"}, {"tab": "chart", "note": "This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices.", "title": "Number of people living below a range of poverty lines (after tax)", "ySlugs": "headcount_dhi_eq_100 headcount_dhi_eq_200 headcount_dhi_eq_500 headcount_dhi_eq_1000 headcount_dhi_eq_2000 headcount_dhi_eq_3000 headcount_dhi_eq_4000", "subtitle": "This data is adjusted for inflation and for differences in living costs between countries. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.", "yAxisMin": "0", "hasMapTab": "false", "tableSlug": "lis_vars", "Indicator Dropdown": "Number in poverty", "Poverty line Dropdown": "Multiple lines", "selectedFacetStrategy": "entity", "Income measure Dropdown": "After tax", "Adjust for cost sharing within households (equivalized income) Checkbox": "true"}, {"tab": "chart", "note": "This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. The cost of closing the poverty gap does not take into account costs and inefficiencies from making the necessary transfers.", "title": "Total shortfall from a range of poverty lines (after tax)", "ySlugs": "total_shortfall_dhi_eq_100 total_shortfall_dhi_eq_200 total_shortfall_dhi_eq_500 total_shortfall_dhi_eq_1000 total_shortfall_dhi_eq_2000 total_shortfall_dhi_eq_3000 total_shortfall_dhi_eq_4000", "subtitle": "This data is adjusted for inflation and for differences in living costs between countries. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.", "yAxisMin": "0", "hasMapTab": "false", "tableSlug": "lis_vars", "Indicator Dropdown": "Total shortfall from poverty line", "Poverty line Dropdown": "Multiple lines", "selectedFacetStrategy": "entity", "Income measure Dropdown": "After tax", "Adjust for cost sharing within households (equivalized income) Checkbox": "true"}, {"tab": "chart", "note": "This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices.", "title": "Average shortfall from a range of poverty lines (after tax)", "ySlugs": "avg_shortfall_dhi_eq_100_day avg_shortfall_dhi_eq_200_day avg_shortfall_dhi_eq_500_day avg_shortfall_dhi_eq_1000_day avg_shortfall_dhi_eq_2000_day avg_shortfall_dhi_eq_3000_day avg_shortfall_dhi_eq_4000_day", "subtitle": "This data is adjusted for inflation and for differences in living costs between countries. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.", "yAxisMin": "0", "hasMapTab": "false", "tableSlug": "lis_vars", "Indicator Dropdown": "Average shortfall ($)", "Poverty line Dropdown": "Multiple lines", "selectedFacetStrategy": "entity", "Income measure Dropdown": "After tax", "Adjust for cost sharing within hou |
2024-02-02 18:43:22 | 2025-04-04 04:21:34 | 62 | 2025-02-05 15:19:00 | :bug: keep single quotes | explorerTitle Poverty - Luxembourg Income Study selection Chile Brazil South Africa United States France China explorerSubtitle Explore Luxembourg Income Study data on poverty. isPublished true googleSheet https://docs.google.com/spreadsheets/d/1UFdwB1iBpP2tEP6GtxCHvW1GGhjsFflh42FWR80rYIg wpBlockId 57755 entityType country or region pickerColumnSlugs headcount_ratio_mi_pc_3000 headcount_ratio_dhi_pc_3000 headcount_mi_pc_3000 headcount_dhi_pc_3000 total_shortfall_mi_pc_3000 total_shortfall_dhi_pc_3000 avg_shortfall_mi_pc_3000 avg_shortfall_dhi_pc_3000 income_gap_ratio_mi_pc_3000 income_gap_ratio_dhi_pc_3000 poverty_gap_index_mi_pc_3000 poverty_gap_index_dhi_pc_3000 headcount_ratio_50_median_mi_pc headcount_50_median_mi_pc total_shortfall_50_median_mi_pc avg_shortfall_50_median_mi_pc income_gap_ratio_50_median_mi_pc poverty_gap_index_50_median_mi_pc graphers title ySlugs Indicator Dropdown Poverty line Dropdown Income measure Dropdown Adjust for cost sharing within households (equivalized income) Checkbox subtitle note type selectedFacetStrategy hasMapTab tab tableSlug relatedQuestionText relatedQuestionUrl yAxisMin defaultView Poverty: Share of population living on less than $1 a day (after tax) headcount_ratio_dhi_eq_100 Share in poverty $1 per day After tax true This data is adjusted for inflation and for differences in living costs between countries. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. true map lis_vars 0 Poverty: Share of population living on less than $2 a day (after tax) headcount_ratio_dhi_eq_200 Share in poverty $2 per day After tax true This data is adjusted for inflation and for differences in living costs between countries. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. true map lis_vars 0 Poverty: Share of population living on less than $5 a day (after tax) headcount_ratio_dhi_eq_500 Share in poverty $5 per day After tax true This data is adjusted for inflation and for differences in living costs between countries. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. true map lis_vars 0 Poverty: Share of population living on less than $10 a day (after tax) headcount_ratio_dhi_eq_1000 Share in poverty $10 per day After tax true This data is adjusted for inflation and for differences in living costs between countries. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. true map lis_vars 0 Poverty: Share of population living on less than $20 a day (after tax) headcount_ratio_dhi_eq_2000 Share in poverty $20 per day After tax true This data is adjusted for inflation and for differences in living costs between countries. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. true map lis_vars 0 Poverty: Share of population living on less than $30 a day (after tax) headcount_ratio_dhi_eq_3000 Share in poverty $30 per day After tax true This data is adjusted for inflation and for differences in living costs between countries. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. true map lis_vars 0 Poverty: Share of population living on less than $40 a day (after tax) headcount_ratio_dhi_eq_4000 Share in poverty $40 per day After tax true This data is adjusted for inflation and for differences in living costs between countries. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. true map lis_vars 0 Poverty: Number of people living on less than $1 a day (after tax) headcount_dhi_eq_100 Number in poverty $1 per day After tax true This data is adjusted for inflation and for differences in living costs between countries. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. true map lis_vars 0 Poverty: Number of people living on less than $2 a day (after tax) headcount_dhi_eq_200 Number in poverty $2 per day After tax true This data is adjusted for inflation and for differences in living costs between countries. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. true map lis_vars 0 Poverty: Number of people living on less than $5 a day (after tax) headcount_dhi_eq_500 Number in poverty $5 per day After tax true This data is adjusted for inflation and for differences in living costs between countries. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. true map lis_vars 0 Poverty: Number of people living on less than $10 a day (after tax) headcount_dhi_eq_1000 Number in poverty $10 per day After tax true This data is adjusted for inflation and for differences in living costs between countries. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. true map lis_vars 0 Poverty: Number of people living on less than $20 a day (after tax) headcount_dhi_eq_2000 Number in poverty $20 per day After tax true This data is adjusted for inflation and for differences in living costs between countries. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. true map lis_vars 0 Poverty: Number of people living on less than $30 a day (after tax) headcount_dhi_eq_3000 Number in poverty $30 per day After tax true This data is adjusted for inflation and for differences in living costs between countries. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. true map lis_vars 0 Poverty: Number of people living on less than $40 a day (after tax) headcount_dhi_eq_4000 Number in poverty $40 per day After tax true This data is adjusted for inflation and for differences in living costs between countries. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. true map lis_vars 0 Total shortfall from a poverty line of $1 a day (after tax) total_shortfall_dhi_eq_100 Total shortfall from poverty line $1 per day After tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $1 a day. This data is adjusted for inflation and for differences in living costs between countries. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. The cost of closing the poverty gap does not take into account costs and inefficiencies from making the necessary transfers. true map lis_vars 0 Total shortfall from a poverty line of $2 a day (after tax) total_shortfall_dhi_eq_200 Total shortfall from poverty line $2 per day After tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $2 a day. This data is adjusted for inflation and for differences in living costs between countries. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. The cost of closing the poverty gap does not take into account costs and inefficiencies from making the necessary transfers. true map lis_vars 0 Total shortfall from a poverty line of $5 a day (after tax) total_shortfall_dhi_eq_500 Total shortfall from poverty line $5 per day After tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $5 a day. This data is adjusted for inflation and for differences in living costs between countries. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. The cost of closing the poverty gap does not take into account costs and inefficiencies from making the necessary transfers. true map lis_vars 0 Total shortfall from a poverty line of $10 a day (after tax) total_shortfall_dhi_eq_1000 Total shortfall from poverty line $10 per day After tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $10 a day. This data is adjusted for inflation and for differences in living costs between countries. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. The cost of closing the poverty gap does not take into account costs and inefficiencies from making the necessary transfers. true map lis_vars 0 Total shortfall from a poverty line of $20 a day (after tax) total_shortfall_dhi_eq_2000 Total shortfall from poverty line $20 per day After tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $20 a day. This data is adjusted for inflation and for differences in living costs between countries. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. The cost of closing the poverty gap does not take into account costs and inefficiencies from making the necessary transfers. true map lis_vars 0 Total shortfall from a poverty line of $30 a day (after tax) total_shortfall_dhi_eq_3000 Total shortfall from poverty line $30 per day After tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $30 a day. This data is adjusted for inflation and for differences in living costs between countries. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. The cost of closing the poverty gap does not take into account costs and inefficiencies from making the necessary transfers. true map lis_vars 0 Total shortfall from a poverty line of $40 a day (after tax) total_shortfall_dhi_eq_4000 Total shortfall from poverty line $40 per day After tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $40 a day. This data is adjusted for inflation and for differences in living costs between countries. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. The cost of closing the poverty gap does not take into account costs and inefficiencies from making the necessary transfers. true map lis_vars 0 Average shortfall from a poverty line of $1 a day (after tax) avg_shortfall_dhi_eq_100_day Average shortfall ($) $1 per day After tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $1 a day, averaged across the population in poverty. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of $2 a day (after tax) avg_shortfall_dhi_eq_200_day Average shortfall ($) $2 per day After tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $2 a day, averaged across the population in poverty. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of $5 a day (after tax) avg_shortfall_dhi_eq_500_day Average shortfall ($) $5 per day After tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $5 a day, averaged across the population in poverty. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of $10 a day (after tax) avg_shortfall_dhi_eq_1000_day Average shortfall ($) $10 per day After tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $10 a day, averaged across the population in poverty. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of $20 a day (after tax) avg_shortfall_dhi_eq_2000_day Average shortfall ($) $20 per day After tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $20 a day, averaged across the population in poverty. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of $30 a day (after tax) avg_shortfall_dhi_eq_3000_day Average shortfall ($) $30 per day After tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $30 a day, averaged across the population in poverty. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of $40 a day (after tax) avg_shortfall_dhi_eq_4000_day Average shortfall ($) $40 per day After tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $40 a day, averaged across the population in poverty. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of $1 a day (as a share of the poverty line) (after tax) income_gap_ratio_dhi_eq_100 Average shortfall (% of poverty line) $1 per day After tax true This is the average shortfall expressed as a share of the poverty line, sometimes called the 'income gap ratio'. It captures the depth of poverty of those living on less than $1 a day. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of $2 a day (as a share of the poverty line) (after tax) income_gap_ratio_dhi_eq_200 Average shortfall (% of poverty line) $2 per day After tax true This is the average shortfall expressed as a share of the poverty line, sometimes called the 'income gap ratio'. It captures the depth of poverty of those living on less than $2 a day. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of $5 a day (as a share of the poverty line) (after tax) income_gap_ratio_dhi_eq_500 Average shortfall (% of poverty line) $5 per day After tax true This is the average shortfall expressed as a share of the poverty line, sometimes called the 'income gap ratio'. It captures the depth of poverty of those living on less than $5 a day. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of $10 a day (as a share of the poverty line) (after tax) income_gap_ratio_dhi_eq_1000 Average shortfall (% of poverty line) $10 per day After tax true This is the average shortfall expressed as a share of the poverty line, sometimes called the 'income gap ratio'. It captures the depth of poverty of those living on less than $10 a day. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of $20 a day (as a share of the poverty line) (after tax) income_gap_ratio_dhi_eq_2000 Average shortfall (% of poverty line) $20 per day After tax true This is the average shortfall expressed as a share of the poverty line, sometimes called the 'income gap ratio'. It captures the depth of poverty of those living on less than $20 a day. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of $30 a day (as a share of the poverty line) (after tax) income_gap_ratio_dhi_eq_3000 Average shortfall (% of poverty line) $30 per day After tax true This is the average shortfall expressed as a share of the poverty line, sometimes called the 'income gap ratio'. It captures the depth of poverty of those living on less than $30 a day. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of $40 a day (as a share of the poverty line) (after tax) income_gap_ratio_dhi_eq_4000 Average shortfall (% of poverty line) $40 per day After tax true This is the average shortfall expressed as a share of the poverty line, sometimes called the 'income gap ratio'. It captures the depth of poverty of those living on less than $40 a day. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Poverty gap index at $1 a day (after tax) poverty_gap_index_dhi_eq_100 Poverty gap index $1 per day After tax true The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Poverty gap index at $2 a day (after tax) poverty_gap_index_dhi_eq_200 Poverty gap index $2 per day After tax true The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Poverty gap index at $5 a day (after tax) poverty_gap_index_dhi_eq_500 Poverty gap index $5 per day After tax true The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Poverty gap index at $10 a day (after tax) poverty_gap_index_dhi_eq_1000 Poverty gap index $10 per day After tax true The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Poverty gap index at $20 a day (after tax) poverty_gap_index_dhi_eq_2000 Poverty gap index $20 per day After tax true The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Poverty gap index at $30 a day (after tax) poverty_gap_index_dhi_eq_3000 Poverty gap index $30 per day After tax true The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Poverty gap index at $40 a day (after tax) poverty_gap_index_dhi_eq_4000 Poverty gap index $40 per day After tax true The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Share of population living below a range of poverty lines (after tax) headcount_ratio_dhi_eq_100 headcount_ratio_dhi_eq_200 headcount_ratio_dhi_eq_500 headcount_ratio_dhi_eq_1000 headcount_ratio_dhi_eq_2000 headcount_ratio_dhi_eq_3000 headcount_ratio_dhi_eq_4000 Share in poverty Multiple lines After tax true This data is adjusted for inflation and for differences in living costs between countries. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. entity false chart lis_vars 0 Number of people living below a range of poverty lines (after tax) headcount_dhi_eq_100 headcount_dhi_eq_200 headcount_dhi_eq_500 headcount_dhi_eq_1000 headcount_dhi_eq_2000 headcount_dhi_eq_3000 headcount_dhi_eq_4000 Number in poverty Multiple lines After tax true This data is adjusted for inflation and for differences in living costs between countries. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. entity false chart lis_vars 0 Total shortfall from a range of poverty lines (after tax) total_shortfall_dhi_eq_100 total_shortfall_dhi_eq_200 total_shortfall_dhi_eq_500 total_shortfall_dhi_eq_1000 total_shortfall_dhi_eq_2000 total_shortfall_dhi_eq_3000 total_shortfall_dhi_eq_4000 Total shortfall from poverty line Multiple lines After tax true This data is adjusted for inflation and for differences in living costs between countries. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. The cost of closing the poverty gap does not take into account costs and inefficiencies from making the necessary transfers. entity false chart lis_vars 0 Average shortfall from a range of poverty lines (after tax) avg_shortfall_dhi_eq_100_day avg_shortfall_dhi_eq_200_day avg_shortfall_dhi_eq_500_day avg_shortfall_dhi_eq_1000_day avg_shortfall_dhi_eq_2000_day avg_shortfall_dhi_eq_3000_day avg_shortfall_dhi_eq_4000_day Average shortfall ($) Multiple lines After tax true This data is adjusted for inflation and for differences in living costs between countries. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. entity false chart lis_vars 0 Average shortfall from a range of poverty lines (as a share of the poverty line) (after tax) income_gap_ratio_dhi_eq_100 income_gap_ratio_dhi_eq_200 income_gap_ratio_dhi_eq_500 income_gap_ratio_dhi_eq_1000 income_gap_ratio_dhi_eq_2000 income_gap_ratio_dhi_eq_3000 income_gap_ratio_dhi_eq_4000 Average shortfall (% of poverty line) Multiple lines After tax true Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Poverty gap index at a range of poverty lines (after tax) poverty_gap_index_dhi_eq_100 poverty_gap_index_dhi_eq_200 poverty_gap_index_dhi_eq_500 poverty_gap_index_dhi_eq_1000 poverty_gap_index_dhi_eq_2000 poverty_gap_index_dhi_eq_3000 poverty_gap_index_dhi_eq_4000 Poverty gap index Multiple lines After tax true Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Relative poverty: Share of people below 40% of the median income (after tax) headcount_ratio_40_median_dhi_eq Share in poverty Relative poverty: 40% of median After tax true Relative poverty is measured in terms of a poverty line that rises and falls over time with average incomes – in this case set at 40% of the median income. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. true map lis_vars 0 Relative poverty: Share of people below 50% of the median income (after tax) headcount_ratio_50_median_dhi_eq Share in poverty Relative poverty: 50% of median After tax true Relative poverty is measured in terms of a poverty line that rises and falls over time with average incomes – in this case set at 50% of the median income. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. true map lis_vars 0 Relative poverty: Share of people below 60% of the median income (after tax) headcount_ratio_60_median_dhi_eq Share in poverty Relative poverty: 60% of median After tax true Relative poverty is measured in terms of a poverty line that rises and falls over time with average incomes – in this case set at 60% of the median income. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. true map lis_vars 0 Relative poverty: Number of people below 40% of the median income (after tax) headcount_40_median_dhi_eq Number in poverty Relative poverty: 40% of median After tax true Relative poverty is measured in terms of a poverty line that rises and falls over time with average incomes – in this case set at 40% of the median income. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. true map lis_vars 0 Relative poverty: Number of people below 50% of the median income (after tax) headcount_50_median_dhi_eq Number in poverty Relative poverty: 50% of median After tax true Relative poverty is measured in terms of a poverty line that rises and falls over time with average incomes – in this case set at 50% of the median income. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. true map lis_vars 0 Relative poverty: Number of people below 60% of the median income (after tax) headcount_60_median_dhi_eq Number in poverty Relative poverty: 60% of median After tax true Relative poverty is measured in terms of a poverty line that rises and falls over time with average incomes – in this case set at 60% of the median income. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. true map lis_vars 0 Total shortfall from a poverty line of 40% of the median income (after tax) total_shortfall_40_median_dhi_eq Total shortfall from poverty line Relative poverty: 40% of median After tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to 40% of the median income. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Total shortfall from a poverty line of 50% of the median income (after tax) total_shortfall_50_median_dhi_eq Total shortfall from poverty line Relative poverty: 50% of median After tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to 50% of the median income. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Total shortfall from a poverty line of 60% of the median income (after tax) total_shortfall_60_median_dhi_eq Total shortfall from poverty line Relative poverty: 60% of median After tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to 60% of the median income. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of 40% of the median income (after tax) avg_shortfall_40_median_dhi_eq_day Average shortfall ($) Relative poverty: 40% of median After tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to 40% of the median income, averaged across the population in poverty. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of 50% of the median income (after tax) avg_shortfall_50_median_dhi_eq_day Average shortfall ($) Relative poverty: 50% of median After tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to 50% of the median income, averaged across the population in poverty. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of 60% of the median income (after tax) avg_shortfall_60_median_dhi_eq_day Average shortfall ($) Relative poverty: 60% of median After tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to 60% of the median income, averaged across the population in poverty. Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of 40% of the median income (as a share of the poverty line) (after tax) income_gap_ratio_40_median_dhi_eq Average shortfall (% of poverty line) Relative poverty: 40% of median After tax true "This is the average shortfall expressed as a share of the poverty line, sometimes called the ""income gap ratio"". It captures the depth of poverty of those living on less than 40% of the median income. Income here is measured after taxes and benefits. Income has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating." This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of 50% of the median income (as a share of the poverty line) (after tax) income_gap_ratio_50_median_dhi_eq Average shortfall (% of poverty line) Relative poverty: 50% of median After tax true "This is the average shortfall expressed as a share of the poverty line, sometimes called the ""income gap ratio"". It captures the depth of poverty of those living on less than 50% of the median income. Income here is measured after taxes and benefits. Income has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating." This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of 60% of the median income (as a share of the poverty line) (after tax) income_gap_ratio_60_median_dhi_eq Average shortfall (% of poverty line) Relative poverty: 60% of median After tax true "This is the average shortfall expressed as a share of the poverty line, sometimes called the ""income gap ratio"". It captures the depth of poverty of those living on less than 60% of the median income. Income here is measured after taxes and benefits. Income has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating." This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Poverty gap index at 40% of the median income (after tax) poverty_gap_index_40_median_dhi_eq Poverty gap index Relative poverty: 40% of median After tax true The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Poverty gap index at 50% of the median income (after tax) poverty_gap_index_50_median_dhi_eq Poverty gap index Relative poverty: 50% of median After tax true The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Poverty gap index at 60% of the median income (after tax) poverty_gap_index_60_median_dhi_eq Poverty gap index Relative poverty: 60% of median After tax true The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). Income here is measured after taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Poverty: Share of population living on less than $1 a day (before tax) headcount_ratio_mi_eq_100 Share in poverty $1 per day Before tax true This data is adjusted for inflation and for differences in living costs between countries. Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. true map lis_vars 0 Poverty: Share of population living on less than $2 a day (before tax) headcount_ratio_mi_eq_200 Share in poverty $2 per day Before tax true This data is adjusted for inflation and for differences in living costs between countries. Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. true map lis_vars 0 Poverty: Share of population living on less than $5 a day (before tax) headcount_ratio_mi_eq_500 Share in poverty $5 per day Before tax true This data is adjusted for inflation and for differences in living costs between countries. Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. true map lis_vars 0 Poverty: Share of population living on less than $10 a day (before tax) headcount_ratio_mi_eq_1000 Share in poverty $10 per day Before tax true This data is adjusted for inflation and for differences in living costs between countries. Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. true map lis_vars 0 Poverty: Share of population living on less than $20 a day (before tax) headcount_ratio_mi_eq_2000 Share in poverty $20 per day Before tax true This data is adjusted for inflation and for differences in living costs between countries. Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. true map lis_vars 0 Poverty: Share of population living on less than $30 a day (before tax) headcount_ratio_mi_eq_3000 Share in poverty $30 per day Before tax true This data is adjusted for inflation and for differences in living costs between countries. Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. true map lis_vars 0 Poverty: Share of population living on less than $40 a day (before tax) headcount_ratio_mi_eq_4000 Share in poverty $40 per day Before tax true This data is adjusted for inflation and for differences in living costs between countries. Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. true map lis_vars 0 Poverty: Number of people living on less than $1 a day (before tax) headcount_mi_eq_100 Number in poverty $1 per day Before tax true This data is adjusted for inflation and for differences in living costs between countries. Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. true map lis_vars 0 Poverty: Number of people living on less than $2 a day (before tax) headcount_mi_eq_200 Number in poverty $2 per day Before tax true This data is adjusted for inflation and for differences in living costs between countries. Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. true map lis_vars 0 Poverty: Number of people living on less than $5 a day (before tax) headcount_mi_eq_500 Number in poverty $5 per day Before tax true This data is adjusted for inflation and for differences in living costs between countries. Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. true map lis_vars 0 Poverty: Number of people living on less than $10 a day (before tax) headcount_mi_eq_1000 Number in poverty $10 per day Before tax true This data is adjusted for inflation and for differences in living costs between countries. Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. true map lis_vars 0 Poverty: Number of people living on less than $20 a day (before tax) headcount_mi_eq_2000 Number in poverty $20 per day Before tax true This data is adjusted for inflation and for differences in living costs between countries. Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. true map lis_vars 0 Poverty: Number of people living on less than $30 a day (before tax) headcount_mi_eq_3000 Number in poverty $30 per day Before tax true This data is adjusted for inflation and for differences in living costs between countries. Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. true map lis_vars 0 Poverty: Number of people living on less than $40 a day (before tax) headcount_mi_eq_4000 Number in poverty $40 per day Before tax true This data is adjusted for inflation and for differences in living costs between countries. Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. true map lis_vars 0 Total shortfall from a poverty line of $1 a day (before tax) total_shortfall_mi_eq_100 Total shortfall from poverty line $1 per day Before tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $1 a day. This data is adjusted for inflation and for differences in living costs between countries. Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. The cost of closing the poverty gap does not take into account costs and inefficiencies from making the necessary transfers. true map lis_vars 0 Total shortfall from a poverty line of $2 a day (before tax) total_shortfall_mi_eq_200 Total shortfall from poverty line $2 per day Before tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $2 a day. This data is adjusted for inflation and for differences in living costs between countries. Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. The cost of closing the poverty gap does not take into account costs and inefficiencies from making the necessary transfers. true map lis_vars 0 Total shortfall from a poverty line of $5 a day (before tax) total_shortfall_mi_eq_500 Total shortfall from poverty line $5 per day Before tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $5 a day. This data is adjusted for inflation and for differences in living costs between countries. Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. The cost of closing the poverty gap does not take into account costs and inefficiencies from making the necessary transfers. true map lis_vars 0 Total shortfall from a poverty line of $10 a day (before tax) total_shortfall_mi_eq_1000 Total shortfall from poverty line $10 per day Before tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $10 a day. This data is adjusted for inflation and for differences in living costs between countries. Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. The cost of closing the poverty gap does not take into account costs and inefficiencies from making the necessary transfers. true map lis_vars 0 Total shortfall from a poverty line of $20 a day (before tax) total_shortfall_mi_eq_2000 Total shortfall from poverty line $20 per day Before tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $20 a day. This data is adjusted for inflation and for differences in living costs between countries. Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. The cost of closing the poverty gap does not take into account costs and inefficiencies from making the necessary transfers. true map lis_vars 0 Total shortfall from a poverty line of $30 a day (before tax) total_shortfall_mi_eq_3000 Total shortfall from poverty line $30 per day Before tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $30 a day. This data is adjusted for inflation and for differences in living costs between countries. Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. The cost of closing the poverty gap does not take into account costs and inefficiencies from making the necessary transfers. true map lis_vars 0 Total shortfall from a poverty line of $40 a day (before tax) total_shortfall_mi_eq_4000 Total shortfall from poverty line $40 per day Before tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $40 a day. This data is adjusted for inflation and for differences in living costs between countries. Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. The cost of closing the poverty gap does not take into account costs and inefficiencies from making the necessary transfers. true map lis_vars 0 Average shortfall from a poverty line of $1 a day (before tax) avg_shortfall_mi_eq_100_day Average shortfall ($) $1 per day Before tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $1 a day, averaged across the population in poverty. Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of $2 a day (before tax) avg_shortfall_mi_eq_200_day Average shortfall ($) $2 per day Before tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $2 a day, averaged across the population in poverty. Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of $5 a day (before tax) avg_shortfall_mi_eq_500_day Average shortfall ($) $5 per day Before tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $5 a day, averaged across the population in poverty. Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of $10 a day (before tax) avg_shortfall_mi_eq_1000_day Average shortfall ($) $10 per day Before tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $10 a day, averaged across the population in poverty. Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of $20 a day (before tax) avg_shortfall_mi_eq_2000_day Average shortfall ($) $20 per day Before tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $20 a day, averaged across the population in poverty. Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of $30 a day (before tax) avg_shortfall_mi_eq_3000_day Average shortfall ($) $30 per day Before tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $30 a day, averaged across the population in poverty. Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of $40 a day (before tax) avg_shortfall_mi_eq_4000_day Average shortfall ($) $40 per day Before tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $40 a day, averaged across the population in poverty. Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of $1 a day (as a share of the poverty line) (before tax) income_gap_ratio_mi_eq_100 Average shortfall (% of poverty line) $1 per day Before tax true This is the average shortfall expressed as a share of the poverty line, sometimes called the 'income gap ratio'. It captures the depth of poverty of those living on less than $1 a day. Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of $2 a day (as a share of the poverty line) (before tax) income_gap_ratio_mi_eq_200 Average shortfall (% of poverty line) $2 per day Before tax true This is the average shortfall expressed as a share of the poverty line, sometimes called the 'income gap ratio'. It captures the depth of poverty of those living on less than $2 a day. Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of $5 a day (as a share of the poverty line) (before tax) income_gap_ratio_mi_eq_500 Average shortfall (% of poverty line) $5 per day Before tax true This is the average shortfall expressed as a share of the poverty line, sometimes called the 'income gap ratio'. It captures the depth of poverty of those living on less than $5 a day. Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of $10 a day (as a share of the poverty line) (before tax) income_gap_ratio_mi_eq_1000 Average shortfall (% of poverty line) $10 per day Before tax true This is the average shortfall expressed as a share of the poverty line, sometimes called the 'income gap ratio'. It captures the depth of poverty of those living on less than $10 a day. Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of $20 a day (as a share of the poverty line) (before tax) income_gap_ratio_mi_eq_2000 Average shortfall (% of poverty line) $20 per day Before tax true This is the average shortfall expressed as a share of the poverty line, sometimes called the 'income gap ratio'. It captures the depth of poverty of those living on less than $20 a day. Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of $30 a day (as a share of the poverty line) (before tax) income_gap_ratio_mi_eq_3000 Average shortfall (% of poverty line) $30 per day Before tax true This is the average shortfall expressed as a share of the poverty line, sometimes called the 'income gap ratio'. It captures the depth of poverty of those living on less than $30 a day. Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of $40 a day (as a share of the poverty line) (before tax) income_gap_ratio_mi_eq_4000 Average shortfall (% of poverty line) $40 per day Before tax true This is the average shortfall expressed as a share of the poverty line, sometimes called the 'income gap ratio'. It captures the depth of poverty of those living on less than $40 a day. Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Poverty gap index at $1 a day (before tax) poverty_gap_index_mi_eq_100 Poverty gap index $1 per day Before tax true The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Poverty gap index at $2 a day (before tax) poverty_gap_index_mi_eq_200 Poverty gap index $2 per day Before tax true The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Poverty gap index at $5 a day (before tax) poverty_gap_index_mi_eq_500 Poverty gap index $5 per day Before tax true The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Poverty gap index at $10 a day (before tax) poverty_gap_index_mi_eq_1000 Poverty gap index $10 per day Before tax true The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Poverty gap index at $20 a day (before tax) poverty_gap_index_mi_eq_2000 Poverty gap index $20 per day Before tax true The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Poverty gap index at $30 a day (before tax) poverty_gap_index_mi_eq_3000 Poverty gap index $30 per day Before tax true The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Poverty gap index at $40 a day (before tax) poverty_gap_index_mi_eq_4000 Poverty gap index $40 per day Before tax true The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Share of population living below a range of poverty lines (before tax) headcount_ratio_mi_eq_100 headcount_ratio_mi_eq_200 headcount_ratio_mi_eq_500 headcount_ratio_mi_eq_1000 headcount_ratio_mi_eq_2000 headcount_ratio_mi_eq_3000 headcount_ratio_mi_eq_4000 Share in poverty Multiple lines Before tax true This data is adjusted for inflation and for differences in living costs between countries. Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. entity false chart lis_vars 0 Number of people living below a range of poverty lines (before tax) headcount_mi_eq_100 headcount_mi_eq_200 headcount_mi_eq_500 headcount_mi_eq_1000 headcount_mi_eq_2000 headcount_mi_eq_3000 headcount_mi_eq_4000 Number in poverty Multiple lines Before tax true This data is adjusted for inflation and for differences in living costs between countries. Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. entity false chart lis_vars 0 Total shortfall from a range of poverty lines (before tax) total_shortfall_mi_eq_100 total_shortfall_mi_eq_200 total_shortfall_mi_eq_500 total_shortfall_mi_eq_1000 total_shortfall_mi_eq_2000 total_shortfall_mi_eq_3000 total_shortfall_mi_eq_4000 Total shortfall from poverty line Multiple lines Before tax true This data is adjusted for inflation and for differences in living costs between countries. Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. The cost of closing the poverty gap does not take into account costs and inefficiencies from making the necessary transfers. entity false chart lis_vars 0 Average shortfall from a range of poverty lines (before tax) avg_shortfall_mi_eq_100_day avg_shortfall_mi_eq_200_day avg_shortfall_mi_eq_500_day avg_shortfall_mi_eq_1000_day avg_shortfall_mi_eq_2000_day avg_shortfall_mi_eq_3000_day avg_shortfall_mi_eq_4000_day Average shortfall ($) Multiple lines Before tax true This data is adjusted for inflation and for differences in living costs between countries. Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. entity false chart lis_vars 0 Average shortfall from a range of poverty lines (as a share of the poverty line) (before tax) income_gap_ratio_mi_eq_100 income_gap_ratio_mi_eq_200 income_gap_ratio_mi_eq_500 income_gap_ratio_mi_eq_1000 income_gap_ratio_mi_eq_2000 income_gap_ratio_mi_eq_3000 income_gap_ratio_mi_eq_4000 Average shortfall (% of poverty line) Multiple lines Before tax true Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Poverty gap index at a range of poverty lines (before tax) poverty_gap_index_mi_eq_100 poverty_gap_index_mi_eq_200 poverty_gap_index_mi_eq_500 poverty_gap_index_mi_eq_1000 poverty_gap_index_mi_eq_2000 poverty_gap_index_mi_eq_3000 poverty_gap_index_mi_eq_4000 Poverty gap index Multiple lines Before tax true Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Relative poverty: Share of people below 40% of the median income (before tax) headcount_ratio_40_median_mi_eq Share in poverty Relative poverty: 40% of median Before tax true Relative poverty is measured in terms of a poverty line that rises and falls over time with average incomes – in this case set at 40% of the median income. Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. true map lis_vars 0 Relative poverty: Share of people below 50% of the median income (before tax) headcount_ratio_50_median_mi_eq Share in poverty Relative poverty: 50% of median Before tax true Relative poverty is measured in terms of a poverty line that rises and falls over time with average incomes – in this case set at 50% of the median income. Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. true map lis_vars 0 Relative poverty: Share of people below 60% of the median income (before tax) headcount_ratio_60_median_mi_eq Share in poverty Relative poverty: 60% of median Before tax true Relative poverty is measured in terms of a poverty line that rises and falls over time with average incomes – in this case set at 60% of the median income. Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. true map lis_vars 0 Relative poverty: Number of people below 40% of the median income (before tax) headcount_40_median_mi_eq Number in poverty Relative poverty: 40% of median Before tax true Relative poverty is measured in terms of a poverty line that rises and falls over time with average incomes – in this case set at 40% of the median income. Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. true map lis_vars 0 Relative poverty: Number of people below 50% of the median income (before tax) headcount_50_median_mi_eq Number in poverty Relative poverty: 50% of median Before tax true Relative poverty is measured in terms of a poverty line that rises and falls over time with average incomes – in this case set at 50% of the median income. Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. true map lis_vars 0 Relative poverty: Number of people below 60% of the median income (before tax) headcount_60_median_mi_eq Number in poverty Relative poverty: 60% of median Before tax true Relative poverty is measured in terms of a poverty line that rises and falls over time with average incomes – in this case set at 60% of the median income. Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. true map lis_vars 0 Total shortfall from a poverty line of 40% of the median income (before tax) total_shortfall_40_median_mi_eq Total shortfall from poverty line Relative poverty: 40% of median Before tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to 40% of the median income. Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Total shortfall from a poverty line of 50% of the median income (before tax) total_shortfall_50_median_mi_eq Total shortfall from poverty line Relative poverty: 50% of median Before tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to 50% of the median income. Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Total shortfall from a poverty line of 60% of the median income (before tax) total_shortfall_60_median_mi_eq Total shortfall from poverty line Relative poverty: 60% of median Before tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to 60% of the median income. Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of 40% of the median income (before tax) avg_shortfall_40_median_mi_eq_day Average shortfall ($) Relative poverty: 40% of median Before tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to 40% of the median income, averaged across the population in poverty. Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of 50% of the median income (before tax) avg_shortfall_50_median_mi_eq_day Average shortfall ($) Relative poverty: 50% of median Before tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to 50% of the median income, averaged across the population in poverty. Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of 60% of the median income (before tax) avg_shortfall_60_median_mi_eq_day Average shortfall ($) Relative poverty: 60% of median Before tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to 60% of the median income, averaged across the population in poverty. Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of 40% of the median income (as a share of the poverty line) (before tax) income_gap_ratio_40_median_mi_eq Average shortfall (% of poverty line) Relative poverty: 40% of median Before tax true "This is the average shortfall expressed as a share of the poverty line, sometimes called the ""income gap ratio"". It captures the depth of poverty of those living on less than 40% of the median income. Income here is measured before taxes and benefits. Income has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating." This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of 50% of the median income (as a share of the poverty line) (before tax) income_gap_ratio_50_median_mi_eq Average shortfall (% of poverty line) Relative poverty: 50% of median Before tax true "This is the average shortfall expressed as a share of the poverty line, sometimes called the ""income gap ratio"". It captures the depth of poverty of those living on less than 50% of the median income. Income here is measured before taxes and benefits. Income has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating." This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of 60% of the median income (as a share of the poverty line) (before tax) income_gap_ratio_60_median_mi_eq Average shortfall (% of poverty line) Relative poverty: 60% of median Before tax true "This is the average shortfall expressed as a share of the poverty line, sometimes called the ""income gap ratio"". It captures the depth of poverty of those living on less than 60% of the median income. Income here is measured before taxes and benefits. Income has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating." This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Poverty gap index at 40% of the median income (before tax) poverty_gap_index_40_median_mi_eq Poverty gap index Relative poverty: 40% of median Before tax true The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Poverty gap index at 50% of the median income (before tax) poverty_gap_index_50_median_mi_eq Poverty gap index Relative poverty: 50% of median Before tax true The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Poverty gap index at 60% of the median income (before tax) poverty_gap_index_60_median_mi_eq Poverty gap index Relative poverty: 60% of median Before tax true The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). Income here is measured before taxes and benefits. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Poverty: Share of population living on less than $1 a day (After vs. before tax) headcount_ratio_mi_eq_100 headcount_ratio_dhi_eq_100 Share in poverty $1 per day After tax vs. before tax true This data is adjusted for inflation and for differences in living costs between countries. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. entity false chart lis_vars 0 Poverty: Share of population living on less than $2 a day (After vs. before tax) headcount_ratio_mi_eq_200 headcount_ratio_dhi_eq_200 Share in poverty $2 per day After tax vs. before tax true This data is adjusted for inflation and for differences in living costs between countries. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. entity false chart lis_vars 0 Poverty: Share of population living on less than $5 a day (After vs. before tax) headcount_ratio_mi_eq_500 headcount_ratio_dhi_eq_500 Share in poverty $5 per day After tax vs. before tax true This data is adjusted for inflation and for differences in living costs between countries. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. entity false chart lis_vars 0 Poverty: Share of population living on less than $10 a day (After vs. before tax) headcount_ratio_mi_eq_1000 headcount_ratio_dhi_eq_1000 Share in poverty $10 per day After tax vs. before tax true This data is adjusted for inflation and for differences in living costs between countries. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. entity false chart lis_vars 0 Poverty: Share of population living on less than $20 a day (After vs. before tax) headcount_ratio_mi_eq_2000 headcount_ratio_dhi_eq_2000 Share in poverty $20 per day After tax vs. before tax true This data is adjusted for inflation and for differences in living costs between countries. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. entity false chart lis_vars 0 Poverty: Share of population living on less than $30 a day (After vs. before tax) headcount_ratio_mi_eq_3000 headcount_ratio_dhi_eq_3000 Share in poverty $30 per day After tax vs. before tax true This data is adjusted for inflation and for differences in living costs between countries. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. entity false chart lis_vars 0 Poverty: Share of population living on less than $40 a day (After vs. before tax) headcount_ratio_mi_eq_4000 headcount_ratio_dhi_eq_4000 Share in poverty $40 per day After tax vs. before tax true This data is adjusted for inflation and for differences in living costs between countries. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. entity false chart lis_vars 0 Poverty: Number of people living on less than $1 a day (After vs. before tax) headcount_mi_eq_100 headcount_dhi_eq_100 Number in poverty $1 per day After tax vs. before tax true This data is adjusted for inflation and for differences in living costs between countries. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. entity false chart lis_vars 0 Poverty: Number of people living on less than $2 a day (After vs. before tax) headcount_mi_eq_200 headcount_dhi_eq_200 Number in poverty $2 per day After tax vs. before tax true This data is adjusted for inflation and for differences in living costs between countries. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. entity false chart lis_vars 0 Poverty: Number of people living on less than $5 a day (After vs. before tax) headcount_mi_eq_500 headcount_dhi_eq_500 Number in poverty $5 per day After tax vs. before tax true This data is adjusted for inflation and for differences in living costs between countries. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. entity false chart lis_vars 0 Poverty: Number of people living on less than $10 a day (After vs. before tax) headcount_mi_eq_1000 headcount_dhi_eq_1000 Number in poverty $10 per day After tax vs. before tax true This data is adjusted for inflation and for differences in living costs between countries. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. entity false chart lis_vars 0 Poverty: Number of people living on less than $20 a day (After vs. before tax) headcount_mi_eq_2000 headcount_dhi_eq_2000 Number in poverty $20 per day After tax vs. before tax true This data is adjusted for inflation and for differences in living costs between countries. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. entity false chart lis_vars 0 Poverty: Number of people living on less than $30 a day (After vs. before tax) headcount_mi_eq_3000 headcount_dhi_eq_3000 Number in poverty $30 per day After tax vs. before tax true This data is adjusted for inflation and for differences in living costs between countries. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. entity false chart lis_vars 0 Poverty: Number of people living on less than $40 a day (After vs. before tax) headcount_mi_eq_4000 headcount_dhi_eq_4000 Number in poverty $40 per day After tax vs. before tax true This data is adjusted for inflation and for differences in living costs between countries. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. entity false chart lis_vars 0 Total shortfall from a poverty line of $1 a day (After vs. before tax) total_shortfall_mi_eq_100 total_shortfall_dhi_eq_100 Total shortfall from poverty line $1 per day After tax vs. before tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $1 a day. This data is adjusted for inflation and for differences in living costs between countries. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. The cost of closing the poverty gap does not take into account costs and inefficiencies from making the necessary transfers. entity false chart lis_vars 0 Total shortfall from a poverty line of $2 a day (After vs. before tax) total_shortfall_mi_eq_200 total_shortfall_dhi_eq_200 Total shortfall from poverty line $2 per day After tax vs. before tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $2 a day. This data is adjusted for inflation and for differences in living costs between countries. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. The cost of closing the poverty gap does not take into account costs and inefficiencies from making the necessary transfers. entity false chart lis_vars 0 Total shortfall from a poverty line of $5 a day (After vs. before tax) total_shortfall_mi_eq_500 total_shortfall_dhi_eq_500 Total shortfall from poverty line $5 per day After tax vs. before tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $5 a day. This data is adjusted for inflation and for differences in living costs between countries. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. The cost of closing the poverty gap does not take into account costs and inefficiencies from making the necessary transfers. entity false chart lis_vars 0 Total shortfall from a poverty line of $10 a day (After vs. before tax) total_shortfall_mi_eq_1000 total_shortfall_dhi_eq_1000 Total shortfall from poverty line $10 per day After tax vs. before tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $10 a day. This data is adjusted for inflation and for differences in living costs between countries. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. The cost of closing the poverty gap does not take into account costs and inefficiencies from making the necessary transfers. entity false chart lis_vars 0 Total shortfall from a poverty line of $20 a day (After vs. before tax) total_shortfall_mi_eq_2000 total_shortfall_dhi_eq_2000 Total shortfall from poverty line $20 per day After tax vs. before tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $20 a day. This data is adjusted for inflation and for differences in living costs between countries. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. The cost of closing the poverty gap does not take into account costs and inefficiencies from making the necessary transfers. entity false chart lis_vars 0 Total shortfall from a poverty line of $30 a day (After vs. before tax) total_shortfall_mi_eq_3000 total_shortfall_dhi_eq_3000 Total shortfall from poverty line $30 per day After tax vs. before tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $30 a day. This data is adjusted for inflation and for differences in living costs between countries. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. The cost of closing the poverty gap does not take into account costs and inefficiencies from making the necessary transfers. entity false chart lis_vars 0 Total shortfall from a poverty line of $40 a day (After vs. before tax) total_shortfall_mi_eq_4000 total_shortfall_dhi_eq_4000 Total shortfall from poverty line $40 per day After tax vs. before tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $40 a day. This data is adjusted for inflation and for differences in living costs between countries. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. The cost of closing the poverty gap does not take into account costs and inefficiencies from making the necessary transfers. entity false chart lis_vars 0 Average shortfall from a poverty line of $1 a day (After vs. before tax) avg_shortfall_mi_eq_100_day avg_shortfall_dhi_eq_100_day Average shortfall ($) $1 per day After tax vs. before tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $1 a day, averaged across the population in poverty. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Average shortfall from a poverty line of $2 a day (After vs. before tax) avg_shortfall_mi_eq_200_day avg_shortfall_dhi_eq_200_day Average shortfall ($) $2 per day After tax vs. before tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $2 a day, averaged across the population in poverty. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Average shortfall from a poverty line of $5 a day (After vs. before tax) avg_shortfall_mi_eq_500_day avg_shortfall_dhi_eq_500_day Average shortfall ($) $5 per day After tax vs. before tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $5 a day, averaged across the population in poverty. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Average shortfall from a poverty line of $10 a day (After vs. before tax) avg_shortfall_mi_eq_1000_day avg_shortfall_dhi_eq_1000_day Average shortfall ($) $10 per day After tax vs. before tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $10 a day, averaged across the population in poverty. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Average shortfall from a poverty line of $20 a day (After vs. before tax) avg_shortfall_mi_eq_2000_day avg_shortfall_dhi_eq_2000_day Average shortfall ($) $20 per day After tax vs. before tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $20 a day, averaged across the population in poverty. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Average shortfall from a poverty line of $30 a day (After vs. before tax) avg_shortfall_mi_eq_3000_day avg_shortfall_dhi_eq_3000_day Average shortfall ($) $30 per day After tax vs. before tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $30 a day, averaged across the population in poverty. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Average shortfall from a poverty line of $40 a day (After vs. before tax) avg_shortfall_mi_eq_4000_day avg_shortfall_dhi_eq_4000_day Average shortfall ($) $40 per day After tax vs. before tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $40 a day, averaged across the population in poverty. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Average shortfall from a poverty line of $1 a day (as a share of the poverty line) (After vs. before tax) income_gap_ratio_mi_eq_100 income_gap_ratio_dhi_eq_100 Average shortfall (% of poverty line) $1 per day After tax vs. before tax true This is the average shortfall expressed as a share of the poverty line, sometimes called the 'income gap ratio'. It captures the depth of poverty of those living on less than $1 a day. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Average shortfall from a poverty line of $2 a day (as a share of the poverty line) (After vs. before tax) income_gap_ratio_mi_eq_200 income_gap_ratio_dhi_eq_200 Average shortfall (% of poverty line) $2 per day After tax vs. before tax true This is the average shortfall expressed as a share of the poverty line, sometimes called the 'income gap ratio'. It captures the depth of poverty of those living on less than $2 a day. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Average shortfall from a poverty line of $5 a day (as a share of the poverty line) (After vs. before tax) income_gap_ratio_mi_eq_500 income_gap_ratio_dhi_eq_500 Average shortfall (% of poverty line) $5 per day After tax vs. before tax true This is the average shortfall expressed as a share of the poverty line, sometimes called the 'income gap ratio'. It captures the depth of poverty of those living on less than $5 a day. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Average shortfall from a poverty line of $10 a day (as a share of the poverty line) (After vs. before tax) income_gap_ratio_mi_eq_1000 income_gap_ratio_dhi_eq_1000 Average shortfall (% of poverty line) $10 per day After tax vs. before tax true This is the average shortfall expressed as a share of the poverty line, sometimes called the 'income gap ratio'. It captures the depth of poverty of those living on less than $10 a day. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Average shortfall from a poverty line of $20 a day (as a share of the poverty line) (After vs. before tax) income_gap_ratio_mi_eq_2000 income_gap_ratio_dhi_eq_2000 Average shortfall (% of poverty line) $20 per day After tax vs. before tax true This is the average shortfall expressed as a share of the poverty line, sometimes called the 'income gap ratio'. It captures the depth of poverty of those living on less than $20 a day. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Average shortfall from a poverty line of $30 a day (as a share of the poverty line) (After vs. before tax) income_gap_ratio_mi_eq_3000 income_gap_ratio_dhi_eq_3000 Average shortfall (% of poverty line) $30 per day After tax vs. before tax true This is the average shortfall expressed as a share of the poverty line, sometimes called the 'income gap ratio'. It captures the depth of poverty of those living on less than $30 a day. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Average shortfall from a poverty line of $40 a day (as a share of the poverty line) (After vs. before tax) income_gap_ratio_mi_eq_4000 income_gap_ratio_dhi_eq_4000 Average shortfall (% of poverty line) $40 per day After tax vs. before tax true This is the average shortfall expressed as a share of the poverty line, sometimes called the 'income gap ratio'. It captures the depth of poverty of those living on less than $40 a day. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Poverty gap index at $1 a day (After vs. before tax) poverty_gap_index_mi_eq_100 poverty_gap_index_dhi_eq_100 Poverty gap index $1 per day After tax vs. before tax true The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Poverty gap index at $2 a day (After vs. before tax) poverty_gap_index_mi_eq_200 poverty_gap_index_dhi_eq_200 Poverty gap index $2 per day After tax vs. before tax true The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Poverty gap index at $5 a day (After vs. before tax) poverty_gap_index_mi_eq_500 poverty_gap_index_dhi_eq_500 Poverty gap index $5 per day After tax vs. before tax true The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Poverty gap index at $10 a day (After vs. before tax) poverty_gap_index_mi_eq_1000 poverty_gap_index_dhi_eq_1000 Poverty gap index $10 per day After tax vs. before tax true The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Poverty gap index at $20 a day (After vs. before tax) poverty_gap_index_mi_eq_2000 poverty_gap_index_dhi_eq_2000 Poverty gap index $20 per day After tax vs. before tax true The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Poverty gap index at $30 a day (After vs. before tax) poverty_gap_index_mi_eq_3000 poverty_gap_index_dhi_eq_3000 Poverty gap index $30 per day After tax vs. before tax true The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Poverty gap index at $40 a day (After vs. before tax) poverty_gap_index_mi_eq_4000 poverty_gap_index_dhi_eq_4000 Poverty gap index $40 per day After tax vs. before tax true The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Relative poverty: Share of people below 40% of the median income (After vs. before tax) headcount_ratio_40_median_mi_eq headcount_ratio_40_median_dhi_eq Share in poverty Relative poverty: 40% of median After tax vs. before tax true Relative poverty is measured in terms of a poverty line that rises and falls over time with average incomes – in this case set at 40% of the median income. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. entity false chart lis_vars 0 Relative poverty: Share of people below 50% of the median income (After vs. before tax) headcount_ratio_50_median_mi_eq headcount_ratio_50_median_dhi_eq Share in poverty Relative poverty: 50% of median After tax vs. before tax true Relative poverty is measured in terms of a poverty line that rises and falls over time with average incomes – in this case set at 50% of the median income. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. entity false chart lis_vars 0 Relative poverty: Share of people below 60% of the median income (After vs. before tax) headcount_ratio_60_median_mi_eq headcount_ratio_60_median_dhi_eq Share in poverty Relative poverty: 60% of median After tax vs. before tax true Relative poverty is measured in terms of a poverty line that rises and falls over time with average incomes – in this case set at 60% of the median income. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. entity false chart lis_vars 0 Relative poverty: Number of people below 40% of the median income (After vs. before tax) headcount_40_median_mi_eq headcount_40_median_dhi_eq Number in poverty Relative poverty: 40% of median After tax vs. before tax true Relative poverty is measured in terms of a poverty line that rises and falls over time with average incomes – in this case set at 40% of the median income. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. entity false chart lis_vars 0 Relative poverty: Number of people below 50% of the median income (After vs. before tax) headcount_50_median_mi_eq headcount_50_median_dhi_eq Number in poverty Relative poverty: 50% of median After tax vs. before tax true Relative poverty is measured in terms of a poverty line that rises and falls over time with average incomes – in this case set at 50% of the median income. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. entity false chart lis_vars 0 Relative poverty: Number of people below 60% of the median income (After vs. before tax) headcount_60_median_mi_eq headcount_60_median_dhi_eq Number in poverty Relative poverty: 60% of median After tax vs. before tax true Relative poverty is measured in terms of a poverty line that rises and falls over time with average incomes – in this case set at 60% of the median income. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. entity false chart lis_vars 0 Total shortfall from a poverty line of 40% of the median income (After vs. before tax) total_shortfall_40_median_mi_eq total_shortfall_40_median_dhi_eq Total shortfall from poverty line Relative poverty: 40% of median After tax vs. before tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to 40% of the median income. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Total shortfall from a poverty line of 50% of the median income (After vs. before tax) total_shortfall_50_median_mi_eq total_shortfall_50_median_dhi_eq Total shortfall from poverty line Relative poverty: 50% of median After tax vs. before tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to 50% of the median income. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Total shortfall from a poverty line of 60% of the median income (After vs. before tax) total_shortfall_60_median_mi_eq total_shortfall_60_median_dhi_eq Total shortfall from poverty line Relative poverty: 60% of median After tax vs. before tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to 60% of the median income. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Average shortfall from a poverty line of 40% of the median income (After vs. before tax) avg_shortfall_40_median_mi_eq_day avg_shortfall_40_median_dhi_eq_day Average shortfall ($) Relative poverty: 40% of median After tax vs. before tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to 40% of the median income, averaged across the population in poverty. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Average shortfall from a poverty line of 50% of the median income (After vs. before tax) avg_shortfall_50_median_mi_eq_day avg_shortfall_50_median_dhi_eq_day Average shortfall ($) Relative poverty: 50% of median After tax vs. before tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to 50% of the median income, averaged across the population in poverty. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Average shortfall from a poverty line of 60% of the median income (After vs. before tax) avg_shortfall_60_median_mi_eq_day avg_shortfall_60_median_dhi_eq_day Average shortfall ($) Relative poverty: 60% of median After tax vs. before tax true This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to 60% of the median income, averaged across the population in poverty. Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Average shortfall from a poverty line of 40% of the median income (as a share of the poverty line) (After vs. before tax) income_gap_ratio_40_median_mi_eq income_gap_ratio_40_median_dhi_eq Average shortfall (% of poverty line) Relative poverty: 40% of median After tax vs. before tax true "This is the average shortfall expressed as a share of the poverty line, sometimes called the ""income gap ratio"". It captures the depth of poverty of those living on less than 40% of the median income. Income has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating." This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Average shortfall from a poverty line of 50% of the median income (as a share of the poverty line) (After vs. before tax) income_gap_ratio_50_median_mi_eq income_gap_ratio_50_median_dhi_eq Average shortfall (% of poverty line) Relative poverty: 50% of median After tax vs. before tax true "This is the average shortfall expressed as a share of the poverty line, sometimes called the ""income gap ratio"". It captures the depth of poverty of those living on less than 50% of the median income. Income has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating." This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Average shortfall from a poverty line of 60% of the median income (as a share of the poverty line) (After vs. before tax) income_gap_ratio_60_median_mi_eq income_gap_ratio_60_median_dhi_eq Average shortfall (% of poverty line) Relative poverty: 60% of median After tax vs. before tax true "This is the average shortfall expressed as a share of the poverty line, sometimes called the ""income gap ratio"". It captures the depth of poverty of those living on less than 60% of the median income. Income has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating." This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Poverty gap index at 40% of the median income (After vs. before tax) poverty_gap_index_40_median_mi_eq poverty_gap_index_40_median_dhi_eq Poverty gap index Relative poverty: 40% of median After tax vs. before tax true The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Poverty gap index at 50% of the median income (After vs. before tax) poverty_gap_index_50_median_mi_eq poverty_gap_index_50_median_dhi_eq Poverty gap index Relative poverty: 50% of median After tax vs. before tax true The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Poverty gap index at 60% of the median income (After vs. before tax) poverty_gap_index_60_median_mi_eq poverty_gap_index_60_median_dhi_eq Poverty gap index Relative poverty: 60% of median After tax vs. before tax true The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). Income has also been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Poverty: Share of population living on less than $1 a day (after tax) headcount_ratio_dhi_pc_100 Share in poverty $1 per day After tax false This data is adjusted for inflation and for differences in living costs between countries. Income here is measured after taxes and benefits. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. true map lis_vars 0 Poverty: Share of population living on less than $2 a day (after tax) headcount_ratio_dhi_pc_200 Share in poverty $2 per day After tax false This data is adjusted for inflation and for differences in living costs between countries. Income here is measured after taxes and benefits. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. true map lis_vars 0 Poverty: Share of population living on less than $5 a day (after tax) headcount_ratio_dhi_pc_500 Share in poverty $5 per day After tax false This data is adjusted for inflation and for differences in living costs between countries. Income here is measured after taxes and benefits. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. true map lis_vars 0 Poverty: Share of population living on less than $10 a day (after tax) headcount_ratio_dhi_pc_1000 Share in poverty $10 per day After tax false This data is adjusted for inflation and for differences in living costs between countries. Income here is measured after taxes and benefits. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. true map lis_vars 0 Poverty: Share of population living on less than $20 a day (after tax) headcount_ratio_dhi_pc_2000 Share in poverty $20 per day After tax false This data is adjusted for inflation and for differences in living costs between countries. Income here is measured after taxes and benefits. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. true map lis_vars 0 Poverty: Share of population living on less than $30 a day (after tax) headcount_ratio_dhi_pc_3000 Share in poverty $30 per day After tax false This data is adjusted for inflation and for differences in living costs between countries. Income here is measured after taxes and benefits. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. true map lis_vars 0 true Poverty: Share of population living on less than $40 a day (after tax) headcount_ratio_dhi_pc_4000 Share in poverty $40 per day After tax false This data is adjusted for inflation and for differences in living costs between countries. Income here is measured after taxes and benefits. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. true map lis_vars 0 Poverty: Number of people living on less than $1 a day (after tax) headcount_dhi_pc_100 Number in poverty $1 per day After tax false This data is adjusted for inflation and for differences in living costs between countries. Income here is measured after taxes and benefits. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. true map lis_vars 0 Poverty: Number of people living on less than $2 a day (after tax) headcount_dhi_pc_200 Number in poverty $2 per day After tax false This data is adjusted for inflation and for differences in living costs between countries. Income here is measured after taxes and benefits. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. true map lis_vars 0 Poverty: Number of people living on less than $5 a day (after tax) headcount_dhi_pc_500 Number in poverty $5 per day After tax false This data is adjusted for inflation and for differences in living costs between countries. Income here is measured after taxes and benefits. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. true map lis_vars 0 Poverty: Number of people living on less than $10 a day (after tax) headcount_dhi_pc_1000 Number in poverty $10 per day After tax false This data is adjusted for inflation and for differences in living costs between countries. Income here is measured after taxes and benefits. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. true map lis_vars 0 Poverty: Number of people living on less than $20 a day (after tax) headcount_dhi_pc_2000 Number in poverty $20 per day After tax false This data is adjusted for inflation and for differences in living costs between countries. Income here is measured after taxes and benefits. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. true map lis_vars 0 Poverty: Number of people living on less than $30 a day (after tax) headcount_dhi_pc_3000 Number in poverty $30 per day After tax false This data is adjusted for inflation and for differences in living costs between countries. Income here is measured after taxes and benefits. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. true map lis_vars 0 Poverty: Number of people living on less than $40 a day (after tax) headcount_dhi_pc_4000 Number in poverty $40 per day After tax false This data is adjusted for inflation and for differences in living costs between countries. Income here is measured after taxes and benefits. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. true map lis_vars 0 Total shortfall from a poverty line of $1 a day (after tax) total_shortfall_dhi_pc_100 Total shortfall from poverty line $1 per day After tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $1 a day. This data is adjusted for inflation and for differences in living costs between countries. Income here is measured after taxes and benefits. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. The cost of closing the poverty gap does not take into account costs and inefficiencies from making the necessary transfers. true map lis_vars 0 Total shortfall from a poverty line of $2 a day (after tax) total_shortfall_dhi_pc_200 Total shortfall from poverty line $2 per day After tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $2 a day. This data is adjusted for inflation and for differences in living costs between countries. Income here is measured after taxes and benefits. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. The cost of closing the poverty gap does not take into account costs and inefficiencies from making the necessary transfers. true map lis_vars 0 Total shortfall from a poverty line of $5 a day (after tax) total_shortfall_dhi_pc_500 Total shortfall from poverty line $5 per day After tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $5 a day. This data is adjusted for inflation and for differences in living costs between countries. Income here is measured after taxes and benefits. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. The cost of closing the poverty gap does not take into account costs and inefficiencies from making the necessary transfers. true map lis_vars 0 Total shortfall from a poverty line of $10 a day (after tax) total_shortfall_dhi_pc_1000 Total shortfall from poverty line $10 per day After tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $10 a day. This data is adjusted for inflation and for differences in living costs between countries. Income here is measured after taxes and benefits. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. The cost of closing the poverty gap does not take into account costs and inefficiencies from making the necessary transfers. true map lis_vars 0 Total shortfall from a poverty line of $20 a day (after tax) total_shortfall_dhi_pc_2000 Total shortfall from poverty line $20 per day After tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $20 a day. This data is adjusted for inflation and for differences in living costs between countries. Income here is measured after taxes and benefits. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. The cost of closing the poverty gap does not take into account costs and inefficiencies from making the necessary transfers. true map lis_vars 0 Total shortfall from a poverty line of $30 a day (after tax) total_shortfall_dhi_pc_3000 Total shortfall from poverty line $30 per day After tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $30 a day. This data is adjusted for inflation and for differences in living costs between countries. Income here is measured after taxes and benefits. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. The cost of closing the poverty gap does not take into account costs and inefficiencies from making the necessary transfers. true map lis_vars 0 Total shortfall from a poverty line of $40 a day (after tax) total_shortfall_dhi_pc_4000 Total shortfall from poverty line $40 per day After tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $40 a day. This data is adjusted for inflation and for differences in living costs between countries. Income here is measured after taxes and benefits. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. The cost of closing the poverty gap does not take into account costs and inefficiencies from making the necessary transfers. true map lis_vars 0 Average shortfall from a poverty line of $1 a day (after tax) avg_shortfall_dhi_pc_100_day Average shortfall ($) $1 per day After tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $1 a day, averaged across the population in poverty. Income here is measured after taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of $2 a day (after tax) avg_shortfall_dhi_pc_200_day Average shortfall ($) $2 per day After tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $2 a day, averaged across the population in poverty. Income here is measured after taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of $5 a day (after tax) avg_shortfall_dhi_pc_500_day Average shortfall ($) $5 per day After tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $5 a day, averaged across the population in poverty. Income here is measured after taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of $10 a day (after tax) avg_shortfall_dhi_pc_1000_day Average shortfall ($) $10 per day After tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $10 a day, averaged across the population in poverty. Income here is measured after taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of $20 a day (after tax) avg_shortfall_dhi_pc_2000_day Average shortfall ($) $20 per day After tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $20 a day, averaged across the population in poverty. Income here is measured after taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of $30 a day (after tax) avg_shortfall_dhi_pc_3000_day Average shortfall ($) $30 per day After tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $30 a day, averaged across the population in poverty. Income here is measured after taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of $40 a day (after tax) avg_shortfall_dhi_pc_4000_day Average shortfall ($) $40 per day After tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $40 a day, averaged across the population in poverty. Income here is measured after taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of $1 a day (as a share of the poverty line) (after tax) income_gap_ratio_dhi_pc_100 Average shortfall (% of poverty line) $1 per day After tax false This is the average shortfall expressed as a share of the poverty line, sometimes called the 'income gap ratio'. It captures the depth of poverty of those living on less than $1 a day. Income here is measured after taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of $2 a day (as a share of the poverty line) (after tax) income_gap_ratio_dhi_pc_200 Average shortfall (% of poverty line) $2 per day After tax false This is the average shortfall expressed as a share of the poverty line, sometimes called the 'income gap ratio'. It captures the depth of poverty of those living on less than $2 a day. Income here is measured after taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of $5 a day (as a share of the poverty line) (after tax) income_gap_ratio_dhi_pc_500 Average shortfall (% of poverty line) $5 per day After tax false This is the average shortfall expressed as a share of the poverty line, sometimes called the 'income gap ratio'. It captures the depth of poverty of those living on less than $5 a day. Income here is measured after taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of $10 a day (as a share of the poverty line) (after tax) income_gap_ratio_dhi_pc_1000 Average shortfall (% of poverty line) $10 per day After tax false This is the average shortfall expressed as a share of the poverty line, sometimes called the 'income gap ratio'. It captures the depth of poverty of those living on less than $10 a day. Income here is measured after taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of $20 a day (as a share of the poverty line) (after tax) income_gap_ratio_dhi_pc_2000 Average shortfall (% of poverty line) $20 per day After tax false This is the average shortfall expressed as a share of the poverty line, sometimes called the 'income gap ratio'. It captures the depth of poverty of those living on less than $20 a day. Income here is measured after taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of $30 a day (as a share of the poverty line) (after tax) income_gap_ratio_dhi_pc_3000 Average shortfall (% of poverty line) $30 per day After tax false This is the average shortfall expressed as a share of the poverty line, sometimes called the 'income gap ratio'. It captures the depth of poverty of those living on less than $30 a day. Income here is measured after taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of $40 a day (as a share of the poverty line) (after tax) income_gap_ratio_dhi_pc_4000 Average shortfall (% of poverty line) $40 per day After tax false This is the average shortfall expressed as a share of the poverty line, sometimes called the 'income gap ratio'. It captures the depth of poverty of those living on less than $40 a day. Income here is measured after taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Poverty gap index at $1 a day (after tax) poverty_gap_index_dhi_pc_100 Poverty gap index $1 per day After tax false The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). Income here is measured after taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Poverty gap index at $2 a day (after tax) poverty_gap_index_dhi_pc_200 Poverty gap index $2 per day After tax false The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). Income here is measured after taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Poverty gap index at $5 a day (after tax) poverty_gap_index_dhi_pc_500 Poverty gap index $5 per day After tax false The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). Income here is measured after taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Poverty gap index at $10 a day (after tax) poverty_gap_index_dhi_pc_1000 Poverty gap index $10 per day After tax false The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). Income here is measured after taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Poverty gap index at $20 a day (after tax) poverty_gap_index_dhi_pc_2000 Poverty gap index $20 per day After tax false The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). Income here is measured after taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Poverty gap index at $30 a day (after tax) poverty_gap_index_dhi_pc_3000 Poverty gap index $30 per day After tax false The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). Income here is measured after taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Poverty gap index at $40 a day (after tax) poverty_gap_index_dhi_pc_4000 Poverty gap index $40 per day After tax false The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). Income here is measured after taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Share of population living below a range of poverty lines (after tax) headcount_ratio_dhi_pc_100 headcount_ratio_dhi_pc_200 headcount_ratio_dhi_pc_500 headcount_ratio_dhi_pc_1000 headcount_ratio_dhi_pc_2000 headcount_ratio_dhi_pc_3000 headcount_ratio_dhi_pc_4000 Share in poverty Multiple lines After tax false This data is adjusted for inflation and for differences in living costs between countries. Income here is measured after taxes and benefits. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. entity false chart lis_vars 0 Number of people living below a range of poverty lines (after tax) headcount_dhi_pc_100 headcount_dhi_pc_200 headcount_dhi_pc_500 headcount_dhi_pc_1000 headcount_dhi_pc_2000 headcount_dhi_pc_3000 headcount_dhi_pc_4000 Number in poverty Multiple lines After tax false This data is adjusted for inflation and for differences in living costs between countries. Income here is measured after taxes and benefits. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. entity false chart lis_vars 0 Total shortfall from a range of poverty lines (after tax) total_shortfall_dhi_pc_100 total_shortfall_dhi_pc_200 total_shortfall_dhi_pc_500 total_shortfall_dhi_pc_1000 total_shortfall_dhi_pc_2000 total_shortfall_dhi_pc_3000 total_shortfall_dhi_pc_4000 Total shortfall from poverty line Multiple lines After tax false This data is adjusted for inflation and for differences in living costs between countries. Income here is measured after taxes and benefits. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. The cost of closing the poverty gap does not take into account costs and inefficiencies from making the necessary transfers. entity false chart lis_vars 0 Average shortfall from a range of poverty lines (after tax) avg_shortfall_dhi_pc_100_day avg_shortfall_dhi_pc_200_day avg_shortfall_dhi_pc_500_day avg_shortfall_dhi_pc_1000_day avg_shortfall_dhi_pc_2000_day avg_shortfall_dhi_pc_3000_day avg_shortfall_dhi_pc_4000_day Average shortfall ($) Multiple lines After tax false This data is adjusted for inflation and for differences in living costs between countries. Income here is measured after taxes and benefits. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. entity false chart lis_vars 0 Average shortfall from a range of poverty lines (as a share of the poverty line) (after tax) income_gap_ratio_dhi_pc_100 income_gap_ratio_dhi_pc_200 income_gap_ratio_dhi_pc_500 income_gap_ratio_dhi_pc_1000 income_gap_ratio_dhi_pc_2000 income_gap_ratio_dhi_pc_3000 income_gap_ratio_dhi_pc_4000 Average shortfall (% of poverty line) Multiple lines After tax false Income here is measured after taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Poverty gap index at a range of poverty lines (after tax) poverty_gap_index_dhi_pc_100 poverty_gap_index_dhi_pc_200 poverty_gap_index_dhi_pc_500 poverty_gap_index_dhi_pc_1000 poverty_gap_index_dhi_pc_2000 poverty_gap_index_dhi_pc_3000 poverty_gap_index_dhi_pc_4000 Poverty gap index Multiple lines After tax false Income here is measured after taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Relative poverty: Share of people below 40% of the median income (after tax) headcount_ratio_40_median_dhi_pc Share in poverty Relative poverty: 40% of median After tax false Relative poverty is measured in terms of a poverty line that rises and falls over time with average incomes – in this case set at 40% of the median income. Income here is measured after taxes and benefits. true map lis_vars 0 Relative poverty: Share of people below 50% of the median income (after tax) headcount_ratio_50_median_dhi_pc Share in poverty Relative poverty: 50% of median After tax false Relative poverty is measured in terms of a poverty line that rises and falls over time with average incomes – in this case set at 50% of the median income. Income here is measured after taxes and benefits. true map lis_vars 0 Relative poverty: Share of people below 60% of the median income (after tax) headcount_ratio_60_median_dhi_pc Share in poverty Relative poverty: 60% of median After tax false Relative poverty is measured in terms of a poverty line that rises and falls over time with average incomes – in this case set at 60% of the median income. Income here is measured after taxes and benefits. true map lis_vars 0 Relative poverty: Number of people below 40% of the median income (after tax) headcount_40_median_dhi_pc Number in poverty Relative poverty: 40% of median After tax false Relative poverty is measured in terms of a poverty line that rises and falls over time with average incomes – in this case set at 40% of the median income. Income here is measured after taxes and benefits. true map lis_vars 0 Relative poverty: Number of people below 50% of the median income (after tax) headcount_50_median_dhi_pc Number in poverty Relative poverty: 50% of median After tax false Relative poverty is measured in terms of a poverty line that rises and falls over time with average incomes – in this case set at 50% of the median income. Income here is measured after taxes and benefits. true map lis_vars 0 Relative poverty: Number of people below 60% of the median income (after tax) headcount_60_median_dhi_pc Number in poverty Relative poverty: 60% of median After tax false Relative poverty is measured in terms of a poverty line that rises and falls over time with average incomes – in this case set at 60% of the median income. Income here is measured after taxes and benefits. true map lis_vars 0 Total shortfall from a poverty line of 40% of the median income (after tax) total_shortfall_40_median_dhi_pc Total shortfall from poverty line Relative poverty: 40% of median After tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to 40% of the median income. Income here is measured after taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Total shortfall from a poverty line of 50% of the median income (after tax) total_shortfall_50_median_dhi_pc Total shortfall from poverty line Relative poverty: 50% of median After tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to 50% of the median income. Income here is measured after taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Total shortfall from a poverty line of 60% of the median income (after tax) total_shortfall_60_median_dhi_pc Total shortfall from poverty line Relative poverty: 60% of median After tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to 60% of the median income. Income here is measured after taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of 40% of the median income (after tax) avg_shortfall_40_median_dhi_pc_day Average shortfall ($) Relative poverty: 40% of median After tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to 40% of the median income, averaged across the population in poverty. Income here is measured after taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of 50% of the median income (after tax) avg_shortfall_50_median_dhi_pc_day Average shortfall ($) Relative poverty: 50% of median After tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to 50% of the median income, averaged across the population in poverty. Income here is measured after taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of 60% of the median income (after tax) avg_shortfall_60_median_dhi_pc_day Average shortfall ($) Relative poverty: 60% of median After tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to 60% of the median income, averaged across the population in poverty. Income here is measured after taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of 40% of the median income (as a share of the poverty line) (after tax) income_gap_ratio_40_median_dhi_pc Average shortfall (% of poverty line) Relative poverty: 40% of median After tax false "This is the average shortfall expressed as a share of the poverty line, sometimes called the ""income gap ratio"". It captures the depth of poverty of those living on less than 40% of the median income. Income here is measured after taxes and benefits. " This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of 50% of the median income (as a share of the poverty line) (after tax) income_gap_ratio_50_median_dhi_pc Average shortfall (% of poverty line) Relative poverty: 50% of median After tax false "This is the average shortfall expressed as a share of the poverty line, sometimes called the ""income gap ratio"". It captures the depth of poverty of those living on less than 50% of the median income. Income here is measured after taxes and benefits. " This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of 60% of the median income (as a share of the poverty line) (after tax) income_gap_ratio_60_median_dhi_pc Average shortfall (% of poverty line) Relative poverty: 60% of median After tax false "This is the average shortfall expressed as a share of the poverty line, sometimes called the ""income gap ratio"". It captures the depth of poverty of those living on less than 60% of the median income. Income here is measured after taxes and benefits. " This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Poverty gap index at 40% of the median income (after tax) poverty_gap_index_40_median_dhi_pc Poverty gap index Relative poverty: 40% of median After tax false The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). Income here is measured after taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Poverty gap index at 50% of the median income (after tax) poverty_gap_index_50_median_dhi_pc Poverty gap index Relative poverty: 50% of median After tax false The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). Income here is measured after taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Poverty gap index at 60% of the median income (after tax) poverty_gap_index_60_median_dhi_pc Poverty gap index Relative poverty: 60% of median After tax false The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). Income here is measured after taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Poverty: Share of population living on less than $1 a day (before tax) headcount_ratio_mi_pc_100 Share in poverty $1 per day Before tax false This data is adjusted for inflation and for differences in living costs between countries. Income here is measured before taxes and benefits. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. true map lis_vars 0 Poverty: Share of population living on less than $2 a day (before tax) headcount_ratio_mi_pc_200 Share in poverty $2 per day Before tax false This data is adjusted for inflation and for differences in living costs between countries. Income here is measured before taxes and benefits. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. true map lis_vars 0 Poverty: Share of population living on less than $5 a day (before tax) headcount_ratio_mi_pc_500 Share in poverty $5 per day Before tax false This data is adjusted for inflation and for differences in living costs between countries. Income here is measured before taxes and benefits. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. true map lis_vars 0 Poverty: Share of population living on less than $10 a day (before tax) headcount_ratio_mi_pc_1000 Share in poverty $10 per day Before tax false This data is adjusted for inflation and for differences in living costs between countries. Income here is measured before taxes and benefits. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. true map lis_vars 0 Poverty: Share of population living on less than $20 a day (before tax) headcount_ratio_mi_pc_2000 Share in poverty $20 per day Before tax false This data is adjusted for inflation and for differences in living costs between countries. Income here is measured before taxes and benefits. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. true map lis_vars 0 Poverty: Share of population living on less than $30 a day (before tax) headcount_ratio_mi_pc_3000 Share in poverty $30 per day Before tax false This data is adjusted for inflation and for differences in living costs between countries. Income here is measured before taxes and benefits. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. true map lis_vars 0 Poverty: Share of population living on less than $40 a day (before tax) headcount_ratio_mi_pc_4000 Share in poverty $40 per day Before tax false This data is adjusted for inflation and for differences in living costs between countries. Income here is measured before taxes and benefits. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. true map lis_vars 0 Poverty: Number of people living on less than $1 a day (before tax) headcount_mi_pc_100 Number in poverty $1 per day Before tax false This data is adjusted for inflation and for differences in living costs between countries. Income here is measured before taxes and benefits. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. true map lis_vars 0 Poverty: Number of people living on less than $2 a day (before tax) headcount_mi_pc_200 Number in poverty $2 per day Before tax false This data is adjusted for inflation and for differences in living costs between countries. Income here is measured before taxes and benefits. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. true map lis_vars 0 Poverty: Number of people living on less than $5 a day (before tax) headcount_mi_pc_500 Number in poverty $5 per day Before tax false This data is adjusted for inflation and for differences in living costs between countries. Income here is measured before taxes and benefits. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. true map lis_vars 0 Poverty: Number of people living on less than $10 a day (before tax) headcount_mi_pc_1000 Number in poverty $10 per day Before tax false This data is adjusted for inflation and for differences in living costs between countries. Income here is measured before taxes and benefits. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. true map lis_vars 0 Poverty: Number of people living on less than $20 a day (before tax) headcount_mi_pc_2000 Number in poverty $20 per day Before tax false This data is adjusted for inflation and for differences in living costs between countries. Income here is measured before taxes and benefits. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. true map lis_vars 0 Poverty: Number of people living on less than $30 a day (before tax) headcount_mi_pc_3000 Number in poverty $30 per day Before tax false This data is adjusted for inflation and for differences in living costs between countries. Income here is measured before taxes and benefits. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. true map lis_vars 0 Poverty: Number of people living on less than $40 a day (before tax) headcount_mi_pc_4000 Number in poverty $40 per day Before tax false This data is adjusted for inflation and for differences in living costs between countries. Income here is measured before taxes and benefits. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. true map lis_vars 0 Total shortfall from a poverty line of $1 a day (before tax) total_shortfall_mi_pc_100 Total shortfall from poverty line $1 per day Before tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $1 a day. This data is adjusted for inflation and for differences in living costs between countries. Income here is measured before taxes and benefits. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. The cost of closing the poverty gap does not take into account costs and inefficiencies from making the necessary transfers. true map lis_vars 0 Total shortfall from a poverty line of $2 a day (before tax) total_shortfall_mi_pc_200 Total shortfall from poverty line $2 per day Before tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $2 a day. This data is adjusted for inflation and for differences in living costs between countries. Income here is measured before taxes and benefits. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. The cost of closing the poverty gap does not take into account costs and inefficiencies from making the necessary transfers. true map lis_vars 0 Total shortfall from a poverty line of $5 a day (before tax) total_shortfall_mi_pc_500 Total shortfall from poverty line $5 per day Before tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $5 a day. This data is adjusted for inflation and for differences in living costs between countries. Income here is measured before taxes and benefits. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. The cost of closing the poverty gap does not take into account costs and inefficiencies from making the necessary transfers. true map lis_vars 0 Total shortfall from a poverty line of $10 a day (before tax) total_shortfall_mi_pc_1000 Total shortfall from poverty line $10 per day Before tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $10 a day. This data is adjusted for inflation and for differences in living costs between countries. Income here is measured before taxes and benefits. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. The cost of closing the poverty gap does not take into account costs and inefficiencies from making the necessary transfers. true map lis_vars 0 Total shortfall from a poverty line of $20 a day (before tax) total_shortfall_mi_pc_2000 Total shortfall from poverty line $20 per day Before tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $20 a day. This data is adjusted for inflation and for differences in living costs between countries. Income here is measured before taxes and benefits. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. The cost of closing the poverty gap does not take into account costs and inefficiencies from making the necessary transfers. true map lis_vars 0 Total shortfall from a poverty line of $30 a day (before tax) total_shortfall_mi_pc_3000 Total shortfall from poverty line $30 per day Before tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $30 a day. This data is adjusted for inflation and for differences in living costs between countries. Income here is measured before taxes and benefits. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. The cost of closing the poverty gap does not take into account costs and inefficiencies from making the necessary transfers. true map lis_vars 0 Total shortfall from a poverty line of $40 a day (before tax) total_shortfall_mi_pc_4000 Total shortfall from poverty line $40 per day Before tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $40 a day. This data is adjusted for inflation and for differences in living costs between countries. Income here is measured before taxes and benefits. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. The cost of closing the poverty gap does not take into account costs and inefficiencies from making the necessary transfers. true map lis_vars 0 Average shortfall from a poverty line of $1 a day (before tax) avg_shortfall_mi_pc_100_day Average shortfall ($) $1 per day Before tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $1 a day, averaged across the population in poverty. Income here is measured before taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of $2 a day (before tax) avg_shortfall_mi_pc_200_day Average shortfall ($) $2 per day Before tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $2 a day, averaged across the population in poverty. Income here is measured before taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of $5 a day (before tax) avg_shortfall_mi_pc_500_day Average shortfall ($) $5 per day Before tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $5 a day, averaged across the population in poverty. Income here is measured before taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of $10 a day (before tax) avg_shortfall_mi_pc_1000_day Average shortfall ($) $10 per day Before tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $10 a day, averaged across the population in poverty. Income here is measured before taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of $20 a day (before tax) avg_shortfall_mi_pc_2000_day Average shortfall ($) $20 per day Before tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $20 a day, averaged across the population in poverty. Income here is measured before taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of $30 a day (before tax) avg_shortfall_mi_pc_3000_day Average shortfall ($) $30 per day Before tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $30 a day, averaged across the population in poverty. Income here is measured before taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of $40 a day (before tax) avg_shortfall_mi_pc_4000_day Average shortfall ($) $40 per day Before tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $40 a day, averaged across the population in poverty. Income here is measured before taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of $1 a day (as a share of the poverty line) (before tax) income_gap_ratio_mi_pc_100 Average shortfall (% of poverty line) $1 per day Before tax false This is the average shortfall expressed as a share of the poverty line, sometimes called the 'income gap ratio'. It captures the depth of poverty of those living on less than $1 a day. Income here is measured before taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of $2 a day (as a share of the poverty line) (before tax) income_gap_ratio_mi_pc_200 Average shortfall (% of poverty line) $2 per day Before tax false This is the average shortfall expressed as a share of the poverty line, sometimes called the 'income gap ratio'. It captures the depth of poverty of those living on less than $2 a day. Income here is measured before taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of $5 a day (as a share of the poverty line) (before tax) income_gap_ratio_mi_pc_500 Average shortfall (% of poverty line) $5 per day Before tax false This is the average shortfall expressed as a share of the poverty line, sometimes called the 'income gap ratio'. It captures the depth of poverty of those living on less than $5 a day. Income here is measured before taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of $10 a day (as a share of the poverty line) (before tax) income_gap_ratio_mi_pc_1000 Average shortfall (% of poverty line) $10 per day Before tax false This is the average shortfall expressed as a share of the poverty line, sometimes called the 'income gap ratio'. It captures the depth of poverty of those living on less than $10 a day. Income here is measured before taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of $20 a day (as a share of the poverty line) (before tax) income_gap_ratio_mi_pc_2000 Average shortfall (% of poverty line) $20 per day Before tax false This is the average shortfall expressed as a share of the poverty line, sometimes called the 'income gap ratio'. It captures the depth of poverty of those living on less than $20 a day. Income here is measured before taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of $30 a day (as a share of the poverty line) (before tax) income_gap_ratio_mi_pc_3000 Average shortfall (% of poverty line) $30 per day Before tax false This is the average shortfall expressed as a share of the poverty line, sometimes called the 'income gap ratio'. It captures the depth of poverty of those living on less than $30 a day. Income here is measured before taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of $40 a day (as a share of the poverty line) (before tax) income_gap_ratio_mi_pc_4000 Average shortfall (% of poverty line) $40 per day Before tax false This is the average shortfall expressed as a share of the poverty line, sometimes called the 'income gap ratio'. It captures the depth of poverty of those living on less than $40 a day. Income here is measured before taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Poverty gap index at $1 a day (before tax) poverty_gap_index_mi_pc_100 Poverty gap index $1 per day Before tax false The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). Income here is measured before taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Poverty gap index at $2 a day (before tax) poverty_gap_index_mi_pc_200 Poverty gap index $2 per day Before tax false The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). Income here is measured before taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Poverty gap index at $5 a day (before tax) poverty_gap_index_mi_pc_500 Poverty gap index $5 per day Before tax false The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). Income here is measured before taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Poverty gap index at $10 a day (before tax) poverty_gap_index_mi_pc_1000 Poverty gap index $10 per day Before tax false The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). Income here is measured before taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Poverty gap index at $20 a day (before tax) poverty_gap_index_mi_pc_2000 Poverty gap index $20 per day Before tax false The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). Income here is measured before taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Poverty gap index at $30 a day (before tax) poverty_gap_index_mi_pc_3000 Poverty gap index $30 per day Before tax false The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). Income here is measured before taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Poverty gap index at $40 a day (before tax) poverty_gap_index_mi_pc_4000 Poverty gap index $40 per day Before tax false The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). Income here is measured before taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Share of population living below a range of poverty lines (before tax) headcount_ratio_mi_pc_100 headcount_ratio_mi_pc_200 headcount_ratio_mi_pc_500 headcount_ratio_mi_pc_1000 headcount_ratio_mi_pc_2000 headcount_ratio_mi_pc_3000 headcount_ratio_mi_pc_4000 Share in poverty Multiple lines Before tax false This data is adjusted for inflation and for differences in living costs between countries. Income here is measured before taxes and benefits. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. entity false chart lis_vars 0 Number of people living below a range of poverty lines (before tax) headcount_mi_pc_100 headcount_mi_pc_200 headcount_mi_pc_500 headcount_mi_pc_1000 headcount_mi_pc_2000 headcount_mi_pc_3000 headcount_mi_pc_4000 Number in poverty Multiple lines Before tax false This data is adjusted for inflation and for differences in living costs between countries. Income here is measured before taxes and benefits. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. entity false chart lis_vars 0 Total shortfall from a range of poverty lines (before tax) total_shortfall_mi_pc_100 total_shortfall_mi_pc_200 total_shortfall_mi_pc_500 total_shortfall_mi_pc_1000 total_shortfall_mi_pc_2000 total_shortfall_mi_pc_3000 total_shortfall_mi_pc_4000 Total shortfall from poverty line Multiple lines Before tax false This data is adjusted for inflation and for differences in living costs between countries. Income here is measured before taxes and benefits. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. The cost of closing the poverty gap does not take into account costs and inefficiencies from making the necessary transfers. entity false chart lis_vars 0 Average shortfall from a range of poverty lines (before tax) avg_shortfall_mi_pc_100_day avg_shortfall_mi_pc_200_day avg_shortfall_mi_pc_500_day avg_shortfall_mi_pc_1000_day avg_shortfall_mi_pc_2000_day avg_shortfall_mi_pc_3000_day avg_shortfall_mi_pc_4000_day Average shortfall ($) Multiple lines Before tax false This data is adjusted for inflation and for differences in living costs between countries. Income here is measured before taxes and benefits. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. entity false chart lis_vars 0 Average shortfall from a range of poverty lines (as a share of the poverty line) (before tax) income_gap_ratio_mi_pc_100 income_gap_ratio_mi_pc_200 income_gap_ratio_mi_pc_500 income_gap_ratio_mi_pc_1000 income_gap_ratio_mi_pc_2000 income_gap_ratio_mi_pc_3000 income_gap_ratio_mi_pc_4000 Average shortfall (% of poverty line) Multiple lines Before tax false Income here is measured before taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Poverty gap index at a range of poverty lines (before tax) poverty_gap_index_mi_pc_100 poverty_gap_index_mi_pc_200 poverty_gap_index_mi_pc_500 poverty_gap_index_mi_pc_1000 poverty_gap_index_mi_pc_2000 poverty_gap_index_mi_pc_3000 poverty_gap_index_mi_pc_4000 Poverty gap index Multiple lines Before tax false Income here is measured before taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Relative poverty: Share of people below 40% of the median income (before tax) headcount_ratio_40_median_mi_pc Share in poverty Relative poverty: 40% of median Before tax false Relative poverty is measured in terms of a poverty line that rises and falls over time with average incomes – in this case set at 40% of the median income. Income here is measured before taxes and benefits. true map lis_vars 0 Relative poverty: Share of people below 50% of the median income (before tax) headcount_ratio_50_median_mi_pc Share in poverty Relative poverty: 50% of median Before tax false Relative poverty is measured in terms of a poverty line that rises and falls over time with average incomes – in this case set at 50% of the median income. Income here is measured before taxes and benefits. true map lis_vars 0 Relative poverty: Share of people below 60% of the median income (before tax) headcount_ratio_60_median_mi_pc Share in poverty Relative poverty: 60% of median Before tax false Relative poverty is measured in terms of a poverty line that rises and falls over time with average incomes – in this case set at 60% of the median income. Income here is measured before taxes and benefits. true map lis_vars 0 Relative poverty: Number of people below 40% of the median income (before tax) headcount_40_median_mi_pc Number in poverty Relative poverty: 40% of median Before tax false Relative poverty is measured in terms of a poverty line that rises and falls over time with average incomes – in this case set at 40% of the median income. Income here is measured before taxes and benefits. true map lis_vars 0 Relative poverty: Number of people below 50% of the median income (before tax) headcount_50_median_mi_pc Number in poverty Relative poverty: 50% of median Before tax false Relative poverty is measured in terms of a poverty line that rises and falls over time with average incomes – in this case set at 50% of the median income. Income here is measured before taxes and benefits. true map lis_vars 0 Relative poverty: Number of people below 60% of the median income (before tax) headcount_60_median_mi_pc Number in poverty Relative poverty: 60% of median Before tax false Relative poverty is measured in terms of a poverty line that rises and falls over time with average incomes – in this case set at 60% of the median income. Income here is measured before taxes and benefits. true map lis_vars 0 Total shortfall from a poverty line of 40% of the median income (before tax) total_shortfall_40_median_mi_pc Total shortfall from poverty line Relative poverty: 40% of median Before tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to 40% of the median income. Income here is measured before taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Total shortfall from a poverty line of 50% of the median income (before tax) total_shortfall_50_median_mi_pc Total shortfall from poverty line Relative poverty: 50% of median Before tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to 50% of the median income. Income here is measured before taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Total shortfall from a poverty line of 60% of the median income (before tax) total_shortfall_60_median_mi_pc Total shortfall from poverty line Relative poverty: 60% of median Before tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to 60% of the median income. Income here is measured before taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of 40% of the median income (before tax) avg_shortfall_40_median_mi_pc_day Average shortfall ($) Relative poverty: 40% of median Before tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to 40% of the median income, averaged across the population in poverty. Income here is measured before taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of 50% of the median income (before tax) avg_shortfall_50_median_mi_pc_day Average shortfall ($) Relative poverty: 50% of median Before tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to 50% of the median income, averaged across the population in poverty. Income here is measured before taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of 60% of the median income (before tax) avg_shortfall_60_median_mi_pc_day Average shortfall ($) Relative poverty: 60% of median Before tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to 60% of the median income, averaged across the population in poverty. Income here is measured before taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of 40% of the median income (as a share of the poverty line) (before tax) income_gap_ratio_40_median_mi_pc Average shortfall (% of poverty line) Relative poverty: 40% of median Before tax false "This is the average shortfall expressed as a share of the poverty line, sometimes called the ""income gap ratio"". It captures the depth of poverty of those living on less than 40% of the median income. Income here is measured before taxes and benefits. " This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of 50% of the median income (as a share of the poverty line) (before tax) income_gap_ratio_50_median_mi_pc Average shortfall (% of poverty line) Relative poverty: 50% of median Before tax false "This is the average shortfall expressed as a share of the poverty line, sometimes called the ""income gap ratio"". It captures the depth of poverty of those living on less than 50% of the median income. Income here is measured before taxes and benefits. " This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Average shortfall from a poverty line of 60% of the median income (as a share of the poverty line) (before tax) income_gap_ratio_60_median_mi_pc Average shortfall (% of poverty line) Relative poverty: 60% of median Before tax false "This is the average shortfall expressed as a share of the poverty line, sometimes called the ""income gap ratio"". It captures the depth of poverty of those living on less than 60% of the median income. Income here is measured before taxes and benefits. " This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Poverty gap index at 40% of the median income (before tax) poverty_gap_index_40_median_mi_pc Poverty gap index Relative poverty: 40% of median Before tax false The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). Income here is measured before taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Poverty gap index at 50% of the median income (before tax) poverty_gap_index_50_median_mi_pc Poverty gap index Relative poverty: 50% of median Before tax false The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). Income here is measured before taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Poverty gap index at 60% of the median income (before tax) poverty_gap_index_60_median_mi_pc Poverty gap index Relative poverty: 60% of median Before tax false The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). Income here is measured before taxes and benefits. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. true map lis_vars 0 Poverty: Share of population living on less than $1 a day (After vs. before tax) headcount_ratio_mi_pc_100 headcount_ratio_dhi_pc_100 Share in poverty $1 per day After tax vs. before tax false This data is adjusted for inflation and for differences in living costs between countries. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. entity false chart lis_vars 0 Poverty: Share of population living on less than $2 a day (After vs. before tax) headcount_ratio_mi_pc_200 headcount_ratio_dhi_pc_200 Share in poverty $2 per day After tax vs. before tax false This data is adjusted for inflation and for differences in living costs between countries. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. entity false chart lis_vars 0 Poverty: Share of population living on less than $5 a day (After vs. before tax) headcount_ratio_mi_pc_500 headcount_ratio_dhi_pc_500 Share in poverty $5 per day After tax vs. before tax false This data is adjusted for inflation and for differences in living costs between countries. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. entity false chart lis_vars 0 Poverty: Share of population living on less than $10 a day (After vs. before tax) headcount_ratio_mi_pc_1000 headcount_ratio_dhi_pc_1000 Share in poverty $10 per day After tax vs. before tax false This data is adjusted for inflation and for differences in living costs between countries. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. entity false chart lis_vars 0 Poverty: Share of population living on less than $20 a day (After vs. before tax) headcount_ratio_mi_pc_2000 headcount_ratio_dhi_pc_2000 Share in poverty $20 per day After tax vs. before tax false This data is adjusted for inflation and for differences in living costs between countries. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. entity false chart lis_vars 0 Poverty: Share of population living on less than $30 a day (After vs. before tax) headcount_ratio_mi_pc_3000 headcount_ratio_dhi_pc_3000 Share in poverty $30 per day After tax vs. before tax false This data is adjusted for inflation and for differences in living costs between countries. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. entity false chart lis_vars 0 Poverty: Share of population living on less than $40 a day (After vs. before tax) headcount_ratio_mi_pc_4000 headcount_ratio_dhi_pc_4000 Share in poverty $40 per day After tax vs. before tax false This data is adjusted for inflation and for differences in living costs between countries. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. entity false chart lis_vars 0 Poverty: Number of people living on less than $1 a day (After vs. before tax) headcount_mi_pc_100 headcount_dhi_pc_100 Number in poverty $1 per day After tax vs. before tax false This data is adjusted for inflation and for differences in living costs between countries. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. entity false chart lis_vars 0 Poverty: Number of people living on less than $2 a day (After vs. before tax) headcount_mi_pc_200 headcount_dhi_pc_200 Number in poverty $2 per day After tax vs. before tax false This data is adjusted for inflation and for differences in living costs between countries. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. entity false chart lis_vars 0 Poverty: Number of people living on less than $5 a day (After vs. before tax) headcount_mi_pc_500 headcount_dhi_pc_500 Number in poverty $5 per day After tax vs. before tax false This data is adjusted for inflation and for differences in living costs between countries. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. entity false chart lis_vars 0 Poverty: Number of people living on less than $10 a day (After vs. before tax) headcount_mi_pc_1000 headcount_dhi_pc_1000 Number in poverty $10 per day After tax vs. before tax false This data is adjusted for inflation and for differences in living costs between countries. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. entity false chart lis_vars 0 Poverty: Number of people living on less than $20 a day (After vs. before tax) headcount_mi_pc_2000 headcount_dhi_pc_2000 Number in poverty $20 per day After tax vs. before tax false This data is adjusted for inflation and for differences in living costs between countries. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. entity false chart lis_vars 0 Poverty: Number of people living on less than $30 a day (After vs. before tax) headcount_mi_pc_3000 headcount_dhi_pc_3000 Number in poverty $30 per day After tax vs. before tax false This data is adjusted for inflation and for differences in living costs between countries. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. entity false chart lis_vars 0 Poverty: Number of people living on less than $40 a day (After vs. before tax) headcount_mi_pc_4000 headcount_dhi_pc_4000 Number in poverty $40 per day After tax vs. before tax false This data is adjusted for inflation and for differences in living costs between countries. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. entity false chart lis_vars 0 Total shortfall from a poverty line of $1 a day (After vs. before tax) total_shortfall_mi_pc_100 total_shortfall_dhi_pc_100 Total shortfall from poverty line $1 per day After tax vs. before tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $1 a day. This data is adjusted for inflation and for differences in living costs between countries. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. The cost of closing the poverty gap does not take into account costs and inefficiencies from making the necessary transfers. entity false chart lis_vars 0 Total shortfall from a poverty line of $2 a day (After vs. before tax) total_shortfall_mi_pc_200 total_shortfall_dhi_pc_200 Total shortfall from poverty line $2 per day After tax vs. before tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $2 a day. This data is adjusted for inflation and for differences in living costs between countries. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. The cost of closing the poverty gap does not take into account costs and inefficiencies from making the necessary transfers. entity false chart lis_vars 0 Total shortfall from a poverty line of $5 a day (After vs. before tax) total_shortfall_mi_pc_500 total_shortfall_dhi_pc_500 Total shortfall from poverty line $5 per day After tax vs. before tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $5 a day. This data is adjusted for inflation and for differences in living costs between countries. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. The cost of closing the poverty gap does not take into account costs and inefficiencies from making the necessary transfers. entity false chart lis_vars 0 Total shortfall from a poverty line of $10 a day (After vs. before tax) total_shortfall_mi_pc_1000 total_shortfall_dhi_pc_1000 Total shortfall from poverty line $10 per day After tax vs. before tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $10 a day. This data is adjusted for inflation and for differences in living costs between countries. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. The cost of closing the poverty gap does not take into account costs and inefficiencies from making the necessary transfers. entity false chart lis_vars 0 Total shortfall from a poverty line of $20 a day (After vs. before tax) total_shortfall_mi_pc_2000 total_shortfall_dhi_pc_2000 Total shortfall from poverty line $20 per day After tax vs. before tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $20 a day. This data is adjusted for inflation and for differences in living costs between countries. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. The cost of closing the poverty gap does not take into account costs and inefficiencies from making the necessary transfers. entity false chart lis_vars 0 Total shortfall from a poverty line of $30 a day (After vs. before tax) total_shortfall_mi_pc_3000 total_shortfall_dhi_pc_3000 Total shortfall from poverty line $30 per day After tax vs. before tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $30 a day. This data is adjusted for inflation and for differences in living costs between countries. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. The cost of closing the poverty gap does not take into account costs and inefficiencies from making the necessary transfers. entity false chart lis_vars 0 Total shortfall from a poverty line of $40 a day (After vs. before tax) total_shortfall_mi_pc_4000 total_shortfall_dhi_pc_4000 Total shortfall from poverty line $40 per day After tax vs. before tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $40 a day. This data is adjusted for inflation and for differences in living costs between countries. This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2017 prices. The cost of closing the poverty gap does not take into account costs and inefficiencies from making the necessary transfers. entity false chart lis_vars 0 Average shortfall from a poverty line of $1 a day (After vs. before tax) avg_shortfall_mi_pc_100_day avg_shortfall_dhi_pc_100_day Average shortfall ($) $1 per day After tax vs. before tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $1 a day, averaged across the population in poverty. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Average shortfall from a poverty line of $2 a day (After vs. before tax) avg_shortfall_mi_pc_200_day avg_shortfall_dhi_pc_200_day Average shortfall ($) $2 per day After tax vs. before tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $2 a day, averaged across the population in poverty. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Average shortfall from a poverty line of $5 a day (After vs. before tax) avg_shortfall_mi_pc_500_day avg_shortfall_dhi_pc_500_day Average shortfall ($) $5 per day After tax vs. before tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $5 a day, averaged across the population in poverty. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Average shortfall from a poverty line of $10 a day (After vs. before tax) avg_shortfall_mi_pc_1000_day avg_shortfall_dhi_pc_1000_day Average shortfall ($) $10 per day After tax vs. before tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $10 a day, averaged across the population in poverty. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Average shortfall from a poverty line of $20 a day (After vs. before tax) avg_shortfall_mi_pc_2000_day avg_shortfall_dhi_pc_2000_day Average shortfall ($) $20 per day After tax vs. before tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $20 a day, averaged across the population in poverty. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Average shortfall from a poverty line of $30 a day (After vs. before tax) avg_shortfall_mi_pc_3000_day avg_shortfall_dhi_pc_3000_day Average shortfall ($) $30 per day After tax vs. before tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $30 a day, averaged across the population in poverty. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Average shortfall from a poverty line of $40 a day (After vs. before tax) avg_shortfall_mi_pc_4000_day avg_shortfall_dhi_pc_4000_day Average shortfall ($) $40 per day After tax vs. before tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to $40 a day, averaged across the population in poverty. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Average shortfall from a poverty line of $1 a day (as a share of the poverty line) (After vs. before tax) income_gap_ratio_mi_pc_100 income_gap_ratio_dhi_pc_100 Average shortfall (% of poverty line) $1 per day After tax vs. before tax false This is the average shortfall expressed as a share of the poverty line, sometimes called the 'income gap ratio'. It captures the depth of poverty of those living on less than $1 a day. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Average shortfall from a poverty line of $2 a day (as a share of the poverty line) (After vs. before tax) income_gap_ratio_mi_pc_200 income_gap_ratio_dhi_pc_200 Average shortfall (% of poverty line) $2 per day After tax vs. before tax false This is the average shortfall expressed as a share of the poverty line, sometimes called the 'income gap ratio'. It captures the depth of poverty of those living on less than $2 a day. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Average shortfall from a poverty line of $5 a day (as a share of the poverty line) (After vs. before tax) income_gap_ratio_mi_pc_500 income_gap_ratio_dhi_pc_500 Average shortfall (% of poverty line) $5 per day After tax vs. before tax false This is the average shortfall expressed as a share of the poverty line, sometimes called the 'income gap ratio'. It captures the depth of poverty of those living on less than $5 a day. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Average shortfall from a poverty line of $10 a day (as a share of the poverty line) (After vs. before tax) income_gap_ratio_mi_pc_1000 income_gap_ratio_dhi_pc_1000 Average shortfall (% of poverty line) $10 per day After tax vs. before tax false This is the average shortfall expressed as a share of the poverty line, sometimes called the 'income gap ratio'. It captures the depth of poverty of those living on less than $10 a day. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Average shortfall from a poverty line of $20 a day (as a share of the poverty line) (After vs. before tax) income_gap_ratio_mi_pc_2000 income_gap_ratio_dhi_pc_2000 Average shortfall (% of poverty line) $20 per day After tax vs. before tax false This is the average shortfall expressed as a share of the poverty line, sometimes called the 'income gap ratio'. It captures the depth of poverty of those living on less than $20 a day. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Average shortfall from a poverty line of $30 a day (as a share of the poverty line) (After vs. before tax) income_gap_ratio_mi_pc_3000 income_gap_ratio_dhi_pc_3000 Average shortfall (% of poverty line) $30 per day After tax vs. before tax false This is the average shortfall expressed as a share of the poverty line, sometimes called the 'income gap ratio'. It captures the depth of poverty of those living on less than $30 a day. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Average shortfall from a poverty line of $40 a day (as a share of the poverty line) (After vs. before tax) income_gap_ratio_mi_pc_4000 income_gap_ratio_dhi_pc_4000 Average shortfall (% of poverty line) $40 per day After tax vs. before tax false This is the average shortfall expressed as a share of the poverty line, sometimes called the 'income gap ratio'. It captures the depth of poverty of those living on less than $40 a day. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Poverty gap index at $1 a day (After vs. before tax) poverty_gap_index_mi_pc_100 poverty_gap_index_dhi_pc_100 Poverty gap index $1 per day After tax vs. before tax false The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Poverty gap index at $2 a day (After vs. before tax) poverty_gap_index_mi_pc_200 poverty_gap_index_dhi_pc_200 Poverty gap index $2 per day After tax vs. before tax false The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Poverty gap index at $5 a day (After vs. before tax) poverty_gap_index_mi_pc_500 poverty_gap_index_dhi_pc_500 Poverty gap index $5 per day After tax vs. before tax false The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Poverty gap index at $10 a day (After vs. before tax) poverty_gap_index_mi_pc_1000 poverty_gap_index_dhi_pc_1000 Poverty gap index $10 per day After tax vs. before tax false The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Poverty gap index at $20 a day (After vs. before tax) poverty_gap_index_mi_pc_2000 poverty_gap_index_dhi_pc_2000 Poverty gap index $20 per day After tax vs. before tax false The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Poverty gap index at $30 a day (After vs. before tax) poverty_gap_index_mi_pc_3000 poverty_gap_index_dhi_pc_3000 Poverty gap index $30 per day After tax vs. before tax false The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Poverty gap index at $40 a day (After vs. before tax) poverty_gap_index_mi_pc_4000 poverty_gap_index_dhi_pc_4000 Poverty gap index $40 per day After tax vs. before tax false The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Relative poverty: Share of people below 40% of the median income (After vs. before tax) headcount_ratio_40_median_mi_pc headcount_ratio_40_median_dhi_pc Share in poverty Relative poverty: 40% of median After tax vs. before tax false Relative poverty is measured in terms of a poverty line that rises and falls over time with average incomes – in this case set at 40% of the median income. entity false chart lis_vars 0 Relative poverty: Share of people below 50% of the median income (After vs. before tax) headcount_ratio_50_median_mi_pc headcount_ratio_50_median_dhi_pc Share in poverty Relative poverty: 50% of median After tax vs. before tax false Relative poverty is measured in terms of a poverty line that rises and falls over time with average incomes – in this case set at 50% of the median income. entity false chart lis_vars 0 Relative poverty: Share of people below 60% of the median income (After vs. before tax) headcount_ratio_60_median_mi_pc headcount_ratio_60_median_dhi_pc Share in poverty Relative poverty: 60% of median After tax vs. before tax false Relative poverty is measured in terms of a poverty line that rises and falls over time with average incomes – in this case set at 60% of the median income. entity false chart lis_vars 0 Relative poverty: Number of people below 40% of the median income (After vs. before tax) headcount_40_median_mi_pc headcount_40_median_dhi_pc Number in poverty Relative poverty: 40% of median After tax vs. before tax false Relative poverty is measured in terms of a poverty line that rises and falls over time with average incomes – in this case set at 40% of the median income. entity false chart lis_vars 0 Relative poverty: Number of people below 50% of the median income (After vs. before tax) headcount_50_median_mi_pc headcount_50_median_dhi_pc Number in poverty Relative poverty: 50% of median After tax vs. before tax false Relative poverty is measured in terms of a poverty line that rises and falls over time with average incomes – in this case set at 50% of the median income. entity false chart lis_vars 0 Relative poverty: Number of people below 60% of the median income (After vs. before tax) headcount_60_median_mi_pc headcount_60_median_dhi_pc Number in poverty Relative poverty: 60% of median After tax vs. before tax false Relative poverty is measured in terms of a poverty line that rises and falls over time with average incomes – in this case set at 60% of the median income. entity false chart lis_vars 0 Total shortfall from a poverty line of 40% of the median income (After vs. before tax) total_shortfall_40_median_mi_pc total_shortfall_40_median_dhi_pc Total shortfall from poverty line Relative poverty: 40% of median After tax vs. before tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to 40% of the median income. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Total shortfall from a poverty line of 50% of the median income (After vs. before tax) total_shortfall_50_median_mi_pc total_shortfall_50_median_dhi_pc Total shortfall from poverty line Relative poverty: 50% of median After tax vs. before tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to 50% of the median income. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Total shortfall from a poverty line of 60% of the median income (After vs. before tax) total_shortfall_60_median_mi_pc total_shortfall_60_median_dhi_pc Total shortfall from poverty line Relative poverty: 60% of median After tax vs. before tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to 60% of the median income. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Average shortfall from a poverty line of 40% of the median income (After vs. before tax) avg_shortfall_40_median_mi_pc_day avg_shortfall_40_median_dhi_pc_day Average shortfall ($) Relative poverty: 40% of median After tax vs. before tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to 40% of the median income, averaged across the population in poverty. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Average shortfall from a poverty line of 50% of the median income (After vs. before tax) avg_shortfall_50_median_mi_pc_day avg_shortfall_50_median_dhi_pc_day Average shortfall ($) Relative poverty: 50% of median After tax vs. before tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to 50% of the median income, averaged across the population in poverty. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Average shortfall from a poverty line of 60% of the median income (After vs. before tax) avg_shortfall_60_median_mi_pc_day avg_shortfall_60_median_dhi_pc_day Average shortfall ($) Relative poverty: 60% of median After tax vs. before tax false This is the amount of money that would be theoretically needed to lift the incomes of all people in poverty up to 60% of the median income, averaged across the population in poverty. This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Average shortfall from a poverty line of 40% of the median income (as a share of the poverty line) (After vs. before tax) income_gap_ratio_40_median_mi_pc income_gap_ratio_40_median_dhi_pc Average shortfall (% of poverty line) Relative poverty: 40% of median After tax vs. before tax false "This is the average shortfall expressed as a share of the poverty line, sometimes called the ""income gap ratio"". It captures the depth of poverty of those living on less than 40% of the median income. " This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Average shortfall from a poverty line of 50% of the median income (as a share of the poverty line) (After vs. before tax) income_gap_ratio_50_median_mi_pc income_gap_ratio_50_median_dhi_pc Average shortfall (% of poverty line) Relative poverty: 50% of median After tax vs. before tax false "This is the average shortfall expressed as a share of the poverty line, sometimes called the ""income gap ratio"". It captures the depth of poverty of those living on less than 50% of the median income. " This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Average shortfall from a poverty line of 60% of the median income (as a share of the poverty line) (After vs. before tax) income_gap_ratio_60_median_mi_pc income_gap_ratio_60_median_dhi_pc Average shortfall (% of poverty line) Relative poverty: 60% of median After tax vs. before tax false "This is the average shortfall expressed as a share of the poverty line, sometimes called the ""income gap ratio"". It captures the depth of poverty of those living on less than 60% of the median income. " This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Poverty gap index at 40% of the median income (After vs. before tax) poverty_gap_index_40_median_mi_pc poverty_gap_index_40_median_dhi_pc Poverty gap index Relative poverty: 40% of median After tax vs. before tax false The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Poverty gap index at 50% of the median income (After vs. before tax) poverty_gap_index_50_median_mi_pc poverty_gap_index_50_median_dhi_pc Poverty gap index Relative poverty: 50% of median After tax vs. before tax false The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 Poverty gap index at 60% of the median income (After vs. before tax) poverty_gap_index_60_median_mi_pc poverty_gap_index_60_median_dhi_pc Poverty gap index Relative poverty: 60% of median After tax vs. before tax false The poverty gap index is a poverty measure that reflects both the prevalence and the depth of poverty. It is calculated as the share of population in poverty multiplied by the average shortfall from the poverty line (expressed as a % of the poverty line). This data is measured in [international-$](#dod:int_dollar_abbreviation) at 2017 prices to account for inflation and differences in living costs between countries. entity false chart lis_vars 0 table https://catalog.ourworldindata.org/explorers/lis/latest/luxembourg_income_study/luxembourg_income_study.csv lis_vars columns lis_vars name slug type description unit shortUnit colorScaleNumericBins colorScaleScheme transform sourceName dataPublishedBy sourceLink colorScaleNumericMinValue tolerance colorScaleEqualSizeBins Country country EntityName Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Year year Year Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Share below $1 a day (after tax) headcount_ratio_dhi_eq_100 Numeric % of population living in households with income below $1 a day.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 3;10;20;30;40;50;60;70;80;90;100 OrRd Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Share below $2 a day (after tax) headcount_ratio_dhi_eq_200 Numeric % of population living in households with income below $2 a day.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 3;10;20;30;40;50;60;70;80;90;100 OrRd Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Share below $5 a day (after tax) headcount_ratio_dhi_eq_500 Numeric % of population living in households with income below $5 a day.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 3;10;20;30;40;50;60;70;80;90;100 OrRd Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Share below $10 a day (after tax) headcount_ratio_dhi_eq_1000 Numeric % of population living in households with income below $10 a day.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 3;10;20;30;40;50;60;70;80;90;100 OrRd Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Share below $20 a day (after tax) headcount_ratio_dhi_eq_2000 Numeric % of population living in households with income below $20 a day.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 3;10;20;30;40;50;60;70;80;90;100 OrRd Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Share below $30 a day (after tax) headcount_ratio_dhi_eq_3000 Numeric % of population living in households with income below $30 a day.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 3;10;20;30;40;50;60;70;80;90;100 OrRd Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Share below $40 a day (after tax) headcount_ratio_dhi_eq_4000 Numeric % of population living in households with income below $40 a day.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 3;10;20;30;40;50;60;70;80;90;100 OrRd Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Number below $1 a day (after tax) headcount_dhi_eq_100 Numeric Number of people living in households with income below $1 a day.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. 100000;300000;1000000;3000000;10000000;30000000;100000000;300000000;1000000000 Reds Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Number below $2 a day (after tax) headcount_dhi_eq_200 Numeric Number of people living in households with income below $2 a day.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. 100000;300000;1000000;3000000;10000000;30000000;100000000;300000000;1000000000 Reds Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Number below $5 a day (after tax) headcount_dhi_eq_500 Numeric Number of people living in households with income below $5 a day.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. 100000;300000;1000000;3000000;10000000;30000000;100000000;300000000;1000000000 Reds Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Number below $10 a day (after tax) headcount_dhi_eq_1000 Numeric Number of people living in households with income below $10 a day.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. 100000;300000;1000000;3000000;10000000;30000000;100000000;300000000;1000000000 Reds Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Number below $20 a day (after tax) headcount_dhi_eq_2000 Numeric Number of people living in households with income below $20 a day.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. 100000;300000;1000000;3000000;10000000;30000000;100000000;300000000;1000000000 Reds Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Number below $30 a day (after tax) headcount_dhi_eq_3000 Numeric Number of people living in households with income below $30 a day.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. 100000;300000;1000000;3000000;10000000;30000000;100000000;300000000;1000000000 Reds Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Number below $40 a day (after tax) headcount_dhi_eq_4000 Numeric Number of people living in households with income below $40 a day.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. 100000;300000;1000000;3000000;10000000;30000000;100000000;300000000;1000000000 Reds Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Total shortfall - $1 a day (after tax) total_shortfall_dhi_eq_100 Numeric The total shortfall from a poverty line of $1 a day. This is the amount of money that would be theoretically needed to lift the income of all people in poverty up to the poverty line. However this is not a measure of the actual cost of eliminating poverty, since it does not take into account the costs involved in making the necessary transfers nor any changes in behaviour they would bring about.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 1000000;3000000;10000000;30000000;100000000;300000000;1000000000 Oranges Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Total shortfall - $2 a day (after tax) total_shortfall_dhi_eq_200 Numeric The total shortfall from a poverty line of $2 a day. This is the amount of money that would be theoretically needed to lift the income of all people in poverty up to the poverty line. However this is not a measure of the actual cost of eliminating poverty, since it does not take into account the costs involved in making the necessary transfers nor any changes in behaviour they would bring about.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 1000000;3000000;10000000;30000000;100000000;300000000;1000000000;3000000000;10000000000 Oranges Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Total shortfall - $5 a day (after tax) total_shortfall_dhi_eq_500 Numeric The total shortfall from a poverty line of $5 a day. This is the amount of money that would be theoretically needed to lift the income of all people in poverty up to the poverty line. However this is not a measure of the actual cost of eliminating poverty, since it does not take into account the costs involved in making the necessary transfers nor any changes in behaviour they would bring about.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 100000000;300000000;1000000000;3000000000;10000000000;30000000000;100000000000;300000000000 Oranges Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Total shortfall - $10 a day (after tax) total_shortfall_dhi_eq_1000 Numeric The total shortfall from a poverty line of $10 a day. This is the amount of money that would be theoretically needed to lift the income of all people in poverty up to the poverty line. However this is not a measure of the actual cost of eliminating poverty, since it does not take into account the costs involved in making the necessary transfers nor any changes in behaviour they would bring about.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 1000000000;3000000000;10000000000;30000000000;100000000000;300000000000;1000000000000 Oranges Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Total shortfall - $20 a day (after tax) total_shortfall_dhi_eq_2000 Numeric The total shortfall from a poverty line of $20 a day. This is the amount of money that would be theoretically needed to lift the income of all people in poverty up to the poverty line. However this is not a measure of the actual cost of eliminating poverty, since it does not take into account the costs involved in making the necessary transfers nor any changes in behaviour they would bring about.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 10000000000;30000000000;100000000000;300000000000;1000000000000 Oranges Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Total shortfall - $30 a day (after tax) total_shortfall_dhi_eq_3000 Numeric The total shortfall from a poverty line of $30 a day. This is the amount of money that would be theoretically needed to lift the income of all people in poverty up to the poverty line. However this is not a measure of the actual cost of eliminating poverty, since it does not take into account the costs involved in making the necessary transfers nor any changes in behaviour they would bring about.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 10000000000;30000000000;100000000000;300000000000;1000000000000;3000000000000 Oranges Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Total shortfall - $40 a day (after tax) total_shortfall_dhi_eq_4000 Numeric The total shortfall from a poverty line of $40 a day. This is the amount of money that would be theoretically needed to lift the income of all people in poverty up to the poverty line. However this is not a measure of the actual cost of eliminating poverty, since it does not take into account the costs involved in making the necessary transfers nor any changes in behaviour they would bring about.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 10000000000;30000000000;100000000000;300000000000;1000000000000;3000000000000;10000000000000;30000000000000 Oranges Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - $1 a day (after tax) avg_shortfall_dhi_eq_100 Numeric The average shortfall from a poverty line of $1 (averaged across the population in poverty).\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 0.2;0.4;0.6;0.8;1 Purples Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - $2 a day (after tax) avg_shortfall_dhi_eq_200 Numeric The average shortfall from a poverty line of $2 (averaged across the population in poverty).\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 0.2;0.4;0.6;0.8;1;1.2;1.4;1.6;1.8;2 Purples Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - $5 a day (after tax) avg_shortfall_dhi_eq_500 Numeric The average shortfall from a poverty line of $5 (averaged across the population in poverty).\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 0.5;1;1.5;2;2.5;3;3.5;4;4.5;5 Purples Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - $10 a day (after tax) avg_shortfall_dhi_eq_1000 Numeric The average shortfall from a poverty line of $10 (averaged across the population in poverty).\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 2;4;6;8;10 Purples Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - $20 a day (after tax) avg_shortfall_dhi_eq_2000 Numeric The average shortfall from a poverty line of $20 (averaged across the population in poverty).\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 3;6;9;12;15;18;21 Purples Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - $30 a day (after tax) avg_shortfall_dhi_eq_3000 Numeric The average shortfall from a poverty line of $30 (averaged across the population in poverty).\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 5;10;15;20;25;30 Purples Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - $40 a day (after tax) avg_shortfall_dhi_eq_4000 Numeric The average shortfall from a poverty line of $40 (averaged across the population in poverty).\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 5;10;15;20;25;30;35;40 Purples Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - $1 a day (after tax) avg_shortfall_dhi_eq_100_day Numeric The average shortfall from a poverty line of $1 (averaged across the population in poverty).\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 0.2;0.4;0.6;0.8;1 Purples multiplyBy avg_shortfall_dhi_eq_100 0.00274 Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - $2 a day (after tax) avg_shortfall_dhi_eq_200_day Numeric The average shortfall from a poverty line of $2 (averaged across the population in poverty).\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 0.2;0.4;0.6;0.8;1;1.2;1.4;1.6;1.8;2 Purples multiplyBy avg_shortfall_dhi_eq_200 0.00274 Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - $5 a day (after tax) avg_shortfall_dhi_eq_500_day Numeric The average shortfall from a poverty line of $5 (averaged across the population in poverty).\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 0.5;1;1.5;2;2.5;3;3.5;4;4.5;5 Purples multiplyBy avg_shortfall_dhi_eq_500 0.00274 Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - $10 a day (after tax) avg_shortfall_dhi_eq_1000_day Numeric The average shortfall from a poverty line of $10 (averaged across the population in poverty).\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 2;4;6;8;10 Purples multiplyBy avg_shortfall_dhi_eq_1000 0.00274 Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - $20 a day (after tax) avg_shortfall_dhi_eq_2000_day Numeric The average shortfall from a poverty line of $20 (averaged across the population in poverty).\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 3;6;9;12;15;18;21 Purples multiplyBy avg_shortfall_dhi_eq_2000 0.00274 Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - $30 a day (after tax) avg_shortfall_dhi_eq_3000_day Numeric The average shortfall from a poverty line of $30 (averaged across the population in poverty).\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 5;10;15;20;25;30 Purples multiplyBy avg_shortfall_dhi_eq_3000 0.00274 Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - $40 a day (after tax) avg_shortfall_dhi_eq_4000_day Numeric The average shortfall from a poverty line of $40 (averaged across the population in poverty).\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 5;10;15;20;25;30;35;40 Purples multiplyBy avg_shortfall_dhi_eq_4000 0.00274 Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Income gap ratio - $1 a day (after tax) income_gap_ratio_dhi_eq_100 Numeric "The average shortfall from a poverty line of $1 a day (averaged across the population in poverty) expressed as a share of the poverty line. This metric is sometimes called the ""income gap ratio"". It captures the depth of poverty of those living on less than the poverty line.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty." % % 10;20;30;40;50;60;70;80;90;100 YlOrRd Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Income gap ratio - $2 a day (after tax) income_gap_ratio_dhi_eq_200 Numeric "The average shortfall from a poverty line of $2 a day (averaged across the population in poverty) expressed as a share of the poverty line. This metric is sometimes called the ""income gap ratio"". It captures the depth of poverty of those living on less than the poverty line.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty." % % 10;20;30;40;50;60;70;80;90;100 YlOrRd Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Income gap ratio - $5 a day (after tax) income_gap_ratio_dhi_eq_500 Numeric "The average shortfall from a poverty line of $5 a day (averaged across the population in poverty) expressed as a share of the poverty line. This metric is sometimes called the ""income gap ratio"". It captures the depth of poverty of those living on less than the poverty line.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty." % % 10;20;30;40;50;60;70;80;90;100 YlOrRd Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Income gap ratio - $10 a day (after tax) income_gap_ratio_dhi_eq_1000 Numeric "The average shortfall from a poverty line of $10 a day (averaged across the population in poverty) expressed as a share of the poverty line. This metric is sometimes called the ""income gap ratio"". It captures the depth of poverty of those living on less than the poverty line.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty." % % 10;20;30;40;50;60;70;80;90;100 YlOrRd Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Income gap ratio - $20 a day (after tax) income_gap_ratio_dhi_eq_2000 Numeric "The average shortfall from a poverty line of $20 a day (averaged across the population in poverty) expressed as a share of the poverty line. This metric is sometimes called the ""income gap ratio"". It captures the depth of poverty of those living on less than the poverty line.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty." % % 10;20;30;40;50;60;70;80;90;100 YlOrRd Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Income gap ratio - $30 a day (after tax) income_gap_ratio_dhi_eq_3000 Numeric "The average shortfall from a poverty line of $30 a day (averaged across the population in poverty) expressed as a share of the poverty line. This metric is sometimes called the ""income gap ratio"". It captures the depth of poverty of those living on less than the poverty line.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty." % % 10;20;30;40;50;60;70;80;90;100 YlOrRd Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Income gap ratio - $40 a day (after tax) income_gap_ratio_dhi_eq_4000 Numeric "The average shortfall from a poverty line of $40 a day (averaged across the population in poverty) expressed as a share of the poverty line. This metric is sometimes called the ""income gap ratio"". It captures the depth of poverty of those living on less than the poverty line.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty." % % 10;20;30;40;50;60;70;80;90;100 YlOrRd Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Poverty gap index - $1 a day (after tax) poverty_gap_index_dhi_eq_100 Numeric The poverty gap index calculated at a poverty line of $1 a day. The poverty gap index is a measure that reflects both the depth and prevalence of poverty. It is defined as the mean shortfall of the total population from the poverty line counting the non-poor as having zero shortfall and expressed as a percentage of the poverty line. It is worth unpacking that definition a little. For those below the poverty line, the shortfall corresponds to the amount of money required in order to reach the poverty line. For those at or above the poverty line, the shortfall is counted as zero. The average shortfall is then calculated across the total population – both poor and non-poor – and then expressed as a share of the poverty line. Unlike the more commonly-used metric of the headcount ratio, the poverty gap index is thus sensitive not only to whether a person’s income falls below the poverty line or not, but also by how much – i.e. to the depth of poverty they experience.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 1;2;3;4;5 RdPu Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Poverty gap index - $2 a day (after tax) poverty_gap_index_dhi_eq_200 Numeric The poverty gap index calculated at a poverty line of $2 a day. The poverty gap index is a measure that reflects both the depth and prevalence of poverty. It is defined as the mean shortfall of the total population from the poverty line counting the non-poor as having zero shortfall and expressed as a percentage of the poverty line. It is worth unpacking that definition a little. For those below the poverty line, the shortfall corresponds to the amount of money required in order to reach the poverty line. For those at or above the poverty line, the shortfall is counted as zero. The average shortfall is then calculated across the total population – both poor and non-poor – and then expressed as a share of the poverty line. Unlike the more commonly-used metric of the headcount ratio, the poverty gap index is thus sensitive not only to whether a person’s income falls below the poverty line or not, but also by how much – i.e. to the depth of poverty they experience.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 1;2;3;4;5;6;7;8;9;10 RdPu Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Poverty gap index - $5 a day (after tax) poverty_gap_index_dhi_eq_500 Numeric The poverty gap index calculated at a poverty line of $5 a day. The poverty gap index is a measure that reflects both the depth and prevalence of poverty. It is defined as the mean shortfall of the total population from the poverty line counting the non-poor as having zero shortfall and expressed as a percentage of the poverty line. It is worth unpacking that definition a little. For those below the poverty line, the shortfall corresponds to the amount of money required in order to reach the poverty line. For those at or above the poverty line, the shortfall is counted as zero. The average shortfall is then calculated across the total population – both poor and non-poor – and then expressed as a share of the poverty line. Unlike the more commonly-used metric of the headcount ratio, the poverty gap index is thus sensitive not only to whether a person’s income falls below the poverty line or not, but also by how much – i.e. to the depth of poverty they experience.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 3;6;9;12;15;18;21;24;27;30 RdPu Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Poverty gap index - $10 a day (after tax) poverty_gap_index_dhi_eq_1000 Numeric The poverty gap index calculated at a poverty line of $10 a day. The poverty gap index is a measure that reflects both the depth and prevalence of poverty. It is defined as the mean shortfall of the total population from the poverty line counting the non-poor as having zero shortfall and expressed as a percentage of the poverty line. It is worth unpacking that definition a little. For those below the poverty line, the shortfall corresponds to the amount of money required in order to reach the poverty line. For those at or above the poverty line, the shortfall is counted as zero. The average shortfall is then calculated across the total population – both poor and non-poor – and then expressed as a share of the poverty line. Unlike the more commonly-used metric of the headcount ratio, the poverty gap index is thus sensitive not only to whether a person’s income falls below the poverty line or not, but also by how much – i.e. to the depth of poverty they experience.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 5;10;15;20;25;30;35;40;45;50 RdPu Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Poverty gap index - $20 a day (after tax) poverty_gap_index_dhi_eq_2000 Numeric The poverty gap index calculated at a poverty line of $20 a day. The poverty gap index is a measure that reflects both the depth and prevalence of poverty. It is defined as the mean shortfall of the total population from the poverty line counting the non-poor as having zero shortfall and expressed as a percentage of the poverty line. It is worth unpacking that definition a little. For those below the poverty line, the shortfall corresponds to the amount of money required in order to reach the poverty line. For those at or above the poverty line, the shortfall is counted as zero. The average shortfall is then calculated across the total population – both poor and non-poor – and then expressed as a share of the poverty line. Unlike the more commonly-used metric of the headcount ratio, the poverty gap index is thus sensitive not only to whether a person’s income falls below the poverty line or not, but also by how much – i.e. to the depth of poverty they experience.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 10;20;30;40;50;60;70 RdPu Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Poverty gap index - $30 a day (after tax) poverty_gap_index_dhi_eq_3000 Numeric The poverty gap index calculated at a poverty line of $30 a day. The poverty gap index is a measure that reflects both the depth and prevalence of poverty. It is defined as the mean shortfall of the total population from the poverty line counting the non-poor as having zero shortfall and expressed as a percentage of the poverty line. It is worth unpacking that definition a little. For those below the poverty line, the shortfall corresponds to the amount of money required in order to reach the poverty line. For those at or above the poverty line, the shortfall is counted as zero. The average shortfall is then calculated across the total population – both poor and non-poor – and then expressed as a share of the poverty line. Unlike the more commonly-used metric of the headcount ratio, the poverty gap index is thus sensitive not only to whether a person’s income falls below the poverty line or not, but also by how much – i.e. to the depth of poverty they experience.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 10;20;30;40;50;60;70;80 RdPu Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Poverty gap index - $40 a day (after tax) poverty_gap_index_dhi_eq_4000 Numeric The poverty gap index calculated at a poverty line of $40 a day. The poverty gap index is a measure that reflects both the depth and prevalence of poverty. It is defined as the mean shortfall of the total population from the poverty line counting the non-poor as having zero shortfall and expressed as a percentage of the poverty line. It is worth unpacking that definition a little. For those below the poverty line, the shortfall corresponds to the amount of money required in order to reach the poverty line. For those at or above the poverty line, the shortfall is counted as zero. The average shortfall is then calculated across the total population – both poor and non-poor – and then expressed as a share of the poverty line. Unlike the more commonly-used metric of the headcount ratio, the poverty gap index is thus sensitive not only to whether a person’s income falls below the poverty line or not, but also by how much – i.e. to the depth of poverty they experience.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 10;20;30;40;50;60;70;80;90 RdPu Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Share below 40% of median (after tax) headcount_ratio_40_median_dhi_eq Numeric % of population living in households with income below 40% of the median income.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 5;10;15;20;25;30 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Share below 50% of median (after tax) headcount_ratio_50_median_dhi_eq Numeric % of population living in households with income below 50% of the median income.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 5;10;15;20;25;30 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Share below 60% of median (after tax) headcount_ratio_60_median_dhi_eq Numeric % of population living in households with income below 60% of the median income.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 5;10;15;20;25;30 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Number below 40% of median (after tax) headcount_40_median_dhi_eq Numeric Number of people living in households with income below 40% of the median income.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. 100000;300000;1000000;3000000;10000000;30000000;100000000;300000000;1000000000 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Number below 50% of median (after tax) headcount_50_median_dhi_eq Numeric Number of people living in households with income below 50% of the median income.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. 100000;300000;1000000;3000000;10000000;30000000;100000000;300000000;1000000000 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Number below 60% of median (after tax) headcount_60_median_dhi_eq Numeric Number of people living in households with income below 60% of the median income.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. 100000;300000;1000000;3000000;10000000;30000000;100000000;300000000;1000000000 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Total shortfall - 40% of median (after tax) total_shortfall_40_median_dhi_eq Numeric The total shortfall from a poverty line of 40% of the median income. This is the amount of money that would be theoretically needed to lift the income of all people in poverty up to the poverty line. However this is not a measure of the actual cost of eliminating poverty, since it does not take into account the costs involved in making the necessary transfers nor any changes in behaviour they would bring about.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. 100000000;300000000;1000000000;3000000000;10000000000;30000000000 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Total shortfall - 50% of median (after tax) total_shortfall_50_median_dhi_eq Numeric The total shortfall from a poverty line of 50% of the median income. This is the amount of money that would be theoretically needed to lift the income of all people in poverty up to the poverty line. However this is not a measure of the actual cost of eliminating poverty, since it does not take into account the costs involved in making the necessary transfers nor any changes in behaviour they would bring about.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. 100000000;300000000;1000000000;3000000000;10000000000;30000000000 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Total shortfall - 60% of median (after tax) total_shortfall_60_median_dhi_eq Numeric The total shortfall from a poverty line of 60% of the median income. This is the amount of money that would be theoretically needed to lift the income of all people in poverty up to the poverty line. However this is not a measure of the actual cost of eliminating poverty, since it does not take into account the costs involved in making the necessary transfers nor any changes in behaviour they would bring about.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. 100000000;300000000;1000000000;3000000000;10000000000;30000000000;100000000000 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - 40% of median (after tax) avg_shortfall_40_median_dhi_eq Numeric The average shortfall from a poverty line of of 40% of the median income (averaged across the population in poverty).\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 1000;2000;3000;4000;5000 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - 50% of median (after tax) avg_shortfall_50_median_dhi_eq Numeric The average shortfall from a poverty line of of 50% of the median income (averaged across the population in poverty).\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 1000;2000;3000;4000;5000 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - 60% of median (after tax) avg_shortfall_60_median_dhi_eq Numeric The average shortfall from a poverty line of of 60% of the median income (averaged across the population in poverty).\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 1000;2000;3000;4000;5000 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - 40% of median (after tax) avg_shortfall_40_median_dhi_eq_day Numeric The average shortfall from a poverty line of of 40% of the median income (averaged across the population in poverty).\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 1;2;5;10;20;20.0001 YlOrBr multiplyBy avg_shortfall_40_median_dhi_eq 0.00274 Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - 50% of median (after tax) avg_shortfall_50_median_dhi_eq_day Numeric The average shortfall from a poverty line of of 50% of the median income (averaged across the population in poverty).\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 1;2;5;10;20;20.0001 YlOrBr multiplyBy avg_shortfall_50_median_dhi_eq 0.00274 Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - 60% of median (after tax) avg_shortfall_60_median_dhi_eq_day Numeric The average shortfall from a poverty line of of 60% of the median income (averaged across the population in poverty).\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 1;2;5;10;20;20.0001 YlOrBr multiplyBy avg_shortfall_60_median_dhi_eq 0.00274 Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Income gap ratio - 40% of median (after tax) income_gap_ratio_40_median_dhi_eq Numeric "The average shortfall from a poverty line of of 40% of the median income (averaged across the population in poverty) expressed as a share of the poverty line. This metric is sometimes called the ""income gap ratio"". It captures the depth of poverty of those living on less than the poverty line.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty." % % 5;10;15;20;25;30;35;40 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Income gap ratio - 50% of median (after tax) income_gap_ratio_50_median_dhi_eq Numeric "The average shortfall from a poverty line of of 50% of the median income (averaged across the population in poverty) expressed as a share of the poverty line. This metric is sometimes called the ""income gap ratio"". It captures the depth of poverty of those living on less than the poverty line.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty." % % 5;10;15;20;25;30;35;40 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Income gap ratio - 60% of median (after tax) income_gap_ratio_60_median_dhi_eq Numeric "The average shortfall from a poverty line of of 60% of the median income (averaged across the population in poverty) expressed as a share of the poverty line. This metric is sometimes called the ""income gap ratio"". It captures the depth of poverty of those living on less than the poverty line.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty." % % 5;10;15;20;25;30;35;40 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Poverty gap index - 40% of median (after tax) poverty_gap_index_40_median_dhi_eq Numeric The poverty gap index calculated at a poverty line of 40% of the median income. The poverty gap index is a measure that reflects both the depth and prevalence of poverty. It is defined as the mean shortfall of the total population from the poverty line counting the non-poor as having zero shortfall and expressed as a percentage of the poverty line. It is worth unpacking that definition a little. For those below the poverty line, the shortfall corresponds to the amount of money required in order to reach the poverty line. For those at or above the poverty line, the shortfall is counted as zero. The average shortfall is then calculated across the total population – both poor and non-poor – and then expressed as a share of the poverty line. Unlike the more commonly-used metric of the headcount ratio, the poverty gap index is thus sensitive not only to whether a person’s income falls below the poverty line or not, but also by how much – i.e. to the depth of poverty they experience.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 2;4;6;8;10;12 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Poverty gap index - 50% of median (after tax) poverty_gap_index_50_median_dhi_eq Numeric The poverty gap index calculated at a poverty line of 50% of the median income. The poverty gap index is a measure that reflects both the depth and prevalence of poverty. It is defined as the mean shortfall of the total population from the poverty line counting the non-poor as having zero shortfall and expressed as a percentage of the poverty line. It is worth unpacking that definition a little. For those below the poverty line, the shortfall corresponds to the amount of money required in order to reach the poverty line. For those at or above the poverty line, the shortfall is counted as zero. The average shortfall is then calculated across the total population – both poor and non-poor – and then expressed as a share of the poverty line. Unlike the more commonly-used metric of the headcount ratio, the poverty gap index is thus sensitive not only to whether a person’s income falls below the poverty line or not, but also by how much – i.e. to the depth of poverty they experience.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 2;4;6;8;10;12 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Poverty gap index - 60% of median (after tax) poverty_gap_index_60_median_dhi_eq Numeric The poverty gap index calculated at a poverty line of 60% of the median income. The poverty gap index is a measure that reflects both the depth and prevalence of poverty. It is defined as the mean shortfall of the total population from the poverty line counting the non-poor as having zero shortfall and expressed as a percentage of the poverty line. It is worth unpacking that definition a little. For those below the poverty line, the shortfall corresponds to the amount of money required in order to reach the poverty line. For those at or above the poverty line, the shortfall is counted as zero. The average shortfall is then calculated across the total population – both poor and non-poor – and then expressed as a share of the poverty line. Unlike the more commonly-used metric of the headcount ratio, the poverty gap index is thus sensitive not only to whether a person’s income falls below the poverty line or not, but also by how much – i.e. to the depth of poverty they experience.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 2;4;6;8;10;12 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Share below $1 a day (after tax) headcount_ratio_dhi_pc_100 Numeric % of population living in households with income below $1 a day.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 3;10;20;30;40;50;60;70;80;90;100 OrRd Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Share below $2 a day (after tax) headcount_ratio_dhi_pc_200 Numeric % of population living in households with income below $2 a day.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 3;10;20;30;40;50;60;70;80;90;100 OrRd Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Share below $5 a day (after tax) headcount_ratio_dhi_pc_500 Numeric % of population living in households with income below $5 a day.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 3;10;20;30;40;50;60;70;80;90;100 OrRd Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Share below $10 a day (after tax) headcount_ratio_dhi_pc_1000 Numeric % of population living in households with income below $10 a day.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 3;10;20;30;40;50;60;70;80;90;100 OrRd Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Share below $20 a day (after tax) headcount_ratio_dhi_pc_2000 Numeric % of population living in households with income below $20 a day.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 3;10;20;30;40;50;60;70;80;90;100 OrRd Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Share below $30 a day (after tax) headcount_ratio_dhi_pc_3000 Numeric % of population living in households with income below $30 a day.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 3;10;20;30;40;50;60;70;80;90;100 OrRd Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Share below $40 a day (after tax) headcount_ratio_dhi_pc_4000 Numeric % of population living in households with income below $40 a day.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 3;10;20;30;40;50;60;70;80;90;100 OrRd Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Number below $1 a day (after tax) headcount_dhi_pc_100 Numeric Number of people living in households with income below $1 a day.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. 100000;300000;1000000;3000000;10000000;30000000;100000000;300000000;1000000000 Reds Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Number below $2 a day (after tax) headcount_dhi_pc_200 Numeric Number of people living in households with income below $2 a day.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. 100000;300000;1000000;3000000;10000000;30000000;100000000;300000000;1000000000 Reds Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Number below $5 a day (after tax) headcount_dhi_pc_500 Numeric Number of people living in households with income below $5 a day.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. 100000;300000;1000000;3000000;10000000;30000000;100000000;300000000;1000000000 Reds Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Number below $10 a day (after tax) headcount_dhi_pc_1000 Numeric Number of people living in households with income below $10 a day.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. 100000;300000;1000000;3000000;10000000;30000000;100000000;300000000;1000000000 Reds Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Number below $20 a day (after tax) headcount_dhi_pc_2000 Numeric Number of people living in households with income below $20 a day.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. 100000;300000;1000000;3000000;10000000;30000000;100000000;300000000;1000000000 Reds Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Number below $30 a day (after tax) headcount_dhi_pc_3000 Numeric Number of people living in households with income below $30 a day.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. 100000;300000;1000000;3000000;10000000;30000000;100000000;300000000;1000000000 Reds Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Number below $40 a day (after tax) headcount_dhi_pc_4000 Numeric Number of people living in households with income below $40 a day.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. 100000;300000;1000000;3000000;10000000;30000000;100000000;300000000;1000000000 Reds Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Total shortfall - $1 a day (after tax) total_shortfall_dhi_pc_100 Numeric The total shortfall from a poverty line of $1 a day. This is the amount of money that would be theoretically needed to lift the income of all people in poverty up to the poverty line. However this is not a measure of the actual cost of eliminating poverty, since it does not take into account the costs involved in making the necessary transfers nor any changes in behaviour they would bring about.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 1000000;3000000;10000000;30000000;100000000;300000000;1000000000 Oranges Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Total shortfall - $2 a day (after tax) total_shortfall_dhi_pc_200 Numeric The total shortfall from a poverty line of $2 a day. This is the amount of money that would be theoretically needed to lift the income of all people in poverty up to the poverty line. However this is not a measure of the actual cost of eliminating poverty, since it does not take into account the costs involved in making the necessary transfers nor any changes in behaviour they would bring about.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 1000000;3000000;10000000;30000000;100000000;300000000;1000000000;3000000000;10000000000 Oranges Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Total shortfall - $5 a day (after tax) total_shortfall_dhi_pc_500 Numeric The total shortfall from a poverty line of $5 a day. This is the amount of money that would be theoretically needed to lift the income of all people in poverty up to the poverty line. However this is not a measure of the actual cost of eliminating poverty, since it does not take into account the costs involved in making the necessary transfers nor any changes in behaviour they would bring about.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 100000000;300000000;1000000000;3000000000;10000000000;30000000000;100000000000;300000000000 Oranges Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Total shortfall - $10 a day (after tax) total_shortfall_dhi_pc_1000 Numeric The total shortfall from a poverty line of $10 a day. This is the amount of money that would be theoretically needed to lift the income of all people in poverty up to the poverty line. However this is not a measure of the actual cost of eliminating poverty, since it does not take into account the costs involved in making the necessary transfers nor any changes in behaviour they would bring about.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 1000000000;3000000000;10000000000;30000000000;100000000000;300000000000;1000000000000 Oranges Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Total shortfall - $20 a day (after tax) total_shortfall_dhi_pc_2000 Numeric The total shortfall from a poverty line of $20 a day. This is the amount of money that would be theoretically needed to lift the income of all people in poverty up to the poverty line. However this is not a measure of the actual cost of eliminating poverty, since it does not take into account the costs involved in making the necessary transfers nor any changes in behaviour they would bring about.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 10000000000;30000000000;100000000000;300000000000;1000000000000 Oranges Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Total shortfall - $30 a day (after tax) total_shortfall_dhi_pc_3000 Numeric The total shortfall from a poverty line of $30 a day. This is the amount of money that would be theoretically needed to lift the income of all people in poverty up to the poverty line. However this is not a measure of the actual cost of eliminating poverty, since it does not take into account the costs involved in making the necessary transfers nor any changes in behaviour they would bring about.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 10000000000;30000000000;100000000000;300000000000;1000000000000;3000000000000 Oranges Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Total shortfall - $40 a day (after tax) total_shortfall_dhi_pc_4000 Numeric The total shortfall from a poverty line of $40 a day. This is the amount of money that would be theoretically needed to lift the income of all people in poverty up to the poverty line. However this is not a measure of the actual cost of eliminating poverty, since it does not take into account the costs involved in making the necessary transfers nor any changes in behaviour they would bring about.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 10000000000;30000000000;100000000000;300000000000;1000000000000;3000000000000;10000000000000;30000000000000 Oranges Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - $1 a day (after tax) avg_shortfall_dhi_pc_100 Numeric The average shortfall from a poverty line of $1 (averaged across the population in poverty).\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 0.2;0.4;0.6;0.8;1 Purples Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - $2 a day (after tax) avg_shortfall_dhi_pc_200 Numeric The average shortfall from a poverty line of $2 (averaged across the population in poverty).\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 0.2;0.4;0.6;0.8;1;1.2;1.4;1.6;1.8;2 Purples Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - $5 a day (after tax) avg_shortfall_dhi_pc_500 Numeric The average shortfall from a poverty line of $5 (averaged across the population in poverty).\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 0.5;1;1.5;2;2.5;3;3.5;4;4.5;5 Purples Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - $10 a day (after tax) avg_shortfall_dhi_pc_1000 Numeric The average shortfall from a poverty line of $10 (averaged across the population in poverty).\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 2;4;6;8;10 Purples Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - $20 a day (after tax) avg_shortfall_dhi_pc_2000 Numeric The average shortfall from a poverty line of $20 (averaged across the population in poverty).\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 3;6;9;12;15;18;21 Purples Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - $30 a day (after tax) avg_shortfall_dhi_pc_3000 Numeric The average shortfall from a poverty line of $30 (averaged across the population in poverty).\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 5;10;15;20;25;30 Purples Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - $40 a day (after tax) avg_shortfall_dhi_pc_4000 Numeric The average shortfall from a poverty line of $40 (averaged across the population in poverty).\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 5;10;15;20;25;30;35;40 Purples Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - $1 a day (after tax) avg_shortfall_dhi_pc_100_day Numeric The average shortfall from a poverty line of $1 (averaged across the population in poverty).\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 0.2;0.4;0.6;0.8;1 Purples multiplyBy avg_shortfall_dhi_pc_100 0.00274 Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - $2 a day (after tax) avg_shortfall_dhi_pc_200_day Numeric The average shortfall from a poverty line of $2 (averaged across the population in poverty).\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 0.2;0.4;0.6;0.8;1;1.2;1.4;1.6;1.8;2 Purples multiplyBy avg_shortfall_dhi_pc_200 0.00274 Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - $5 a day (after tax) avg_shortfall_dhi_pc_500_day Numeric The average shortfall from a poverty line of $5 (averaged across the population in poverty).\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 0.5;1;1.5;2;2.5;3;3.5;4;4.5;5 Purples multiplyBy avg_shortfall_dhi_pc_500 0.00274 Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - $10 a day (after tax) avg_shortfall_dhi_pc_1000_day Numeric The average shortfall from a poverty line of $10 (averaged across the population in poverty).\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 2;4;6;8;10 Purples multiplyBy avg_shortfall_dhi_pc_1000 0.00274 Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - $20 a day (after tax) avg_shortfall_dhi_pc_2000_day Numeric The average shortfall from a poverty line of $20 (averaged across the population in poverty).\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 3;6;9;12;15;18;21 Purples multiplyBy avg_shortfall_dhi_pc_2000 0.00274 Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - $30 a day (after tax) avg_shortfall_dhi_pc_3000_day Numeric The average shortfall from a poverty line of $30 (averaged across the population in poverty).\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 5;10;15;20;25;30 Purples multiplyBy avg_shortfall_dhi_pc_3000 0.00274 Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - $40 a day (after tax) avg_shortfall_dhi_pc_4000_day Numeric The average shortfall from a poverty line of $40 (averaged across the population in poverty).\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 5;10;15;20;25;30;35;40 Purples multiplyBy avg_shortfall_dhi_pc_4000 0.00274 Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Income gap ratio - $1 a day (after tax) income_gap_ratio_dhi_pc_100 Numeric "The average shortfall from a poverty line of $1 a day (averaged across the population in poverty) expressed as a share of the poverty line. This metric is sometimes called the ""income gap ratio"". It captures the depth of poverty of those living on less than the poverty line.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty." % % 10;20;30;40;50;60;70;80;90;100 YlOrRd Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Income gap ratio - $2 a day (after tax) income_gap_ratio_dhi_pc_200 Numeric "The average shortfall from a poverty line of $2 a day (averaged across the population in poverty) expressed as a share of the poverty line. This metric is sometimes called the ""income gap ratio"". It captures the depth of poverty of those living on less than the poverty line.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty." % % 10;20;30;40;50;60;70;80;90;100 YlOrRd Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Income gap ratio - $5 a day (after tax) income_gap_ratio_dhi_pc_500 Numeric "The average shortfall from a poverty line of $5 a day (averaged across the population in poverty) expressed as a share of the poverty line. This metric is sometimes called the ""income gap ratio"". It captures the depth of poverty of those living on less than the poverty line.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty." % % 10;20;30;40;50;60;70;80;90;100 YlOrRd Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Income gap ratio - $10 a day (after tax) income_gap_ratio_dhi_pc_1000 Numeric "The average shortfall from a poverty line of $10 a day (averaged across the population in poverty) expressed as a share of the poverty line. This metric is sometimes called the ""income gap ratio"". It captures the depth of poverty of those living on less than the poverty line.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty." % % 10;20;30;40;50;60;70;80;90;100 YlOrRd Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Income gap ratio - $20 a day (after tax) income_gap_ratio_dhi_pc_2000 Numeric "The average shortfall from a poverty line of $20 a day (averaged across the population in poverty) expressed as a share of the poverty line. This metric is sometimes called the ""income gap ratio"". It captures the depth of poverty of those living on less than the poverty line.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty." % % 10;20;30;40;50;60;70;80;90;100 YlOrRd Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Income gap ratio - $30 a day (after tax) income_gap_ratio_dhi_pc_3000 Numeric "The average shortfall from a poverty line of $30 a day (averaged across the population in poverty) expressed as a share of the poverty line. This metric is sometimes called the ""income gap ratio"". It captures the depth of poverty of those living on less than the poverty line.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty." % % 10;20;30;40;50;60;70;80;90;100 YlOrRd Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Income gap ratio - $40 a day (after tax) income_gap_ratio_dhi_pc_4000 Numeric "The average shortfall from a poverty line of $40 a day (averaged across the population in poverty) expressed as a share of the poverty line. This metric is sometimes called the ""income gap ratio"". It captures the depth of poverty of those living on less than the poverty line.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty." % % 10;20;30;40;50;60;70;80;90;100 YlOrRd Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Poverty gap index - $1 a day (after tax) poverty_gap_index_dhi_pc_100 Numeric The poverty gap index calculated at a poverty line of $1 a day. The poverty gap index is a measure that reflects both the depth and prevalence of poverty. It is defined as the mean shortfall of the total population from the poverty line counting the non-poor as having zero shortfall and expressed as a percentage of the poverty line. It is worth unpacking that definition a little. For those below the poverty line, the shortfall corresponds to the amount of money required in order to reach the poverty line. For those at or above the poverty line, the shortfall is counted as zero. The average shortfall is then calculated across the total population – both poor and non-poor – and then expressed as a share of the poverty line. Unlike the more commonly-used metric of the headcount ratio, the poverty gap index is thus sensitive not only to whether a person’s income falls below the poverty line or not, but also by how much – i.e. to the depth of poverty they experience.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 1;2;3;4;5 RdPu Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Poverty gap index - $2 a day (after tax) poverty_gap_index_dhi_pc_200 Numeric The poverty gap index calculated at a poverty line of $2 a day. The poverty gap index is a measure that reflects both the depth and prevalence of poverty. It is defined as the mean shortfall of the total population from the poverty line counting the non-poor as having zero shortfall and expressed as a percentage of the poverty line. It is worth unpacking that definition a little. For those below the poverty line, the shortfall corresponds to the amount of money required in order to reach the poverty line. For those at or above the poverty line, the shortfall is counted as zero. The average shortfall is then calculated across the total population – both poor and non-poor – and then expressed as a share of the poverty line. Unlike the more commonly-used metric of the headcount ratio, the poverty gap index is thus sensitive not only to whether a person’s income falls below the poverty line or not, but also by how much – i.e. to the depth of poverty they experience.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 1;2;3;4;5;6;7;8;9;10 RdPu Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Poverty gap index - $5 a day (after tax) poverty_gap_index_dhi_pc_500 Numeric The poverty gap index calculated at a poverty line of $5 a day. The poverty gap index is a measure that reflects both the depth and prevalence of poverty. It is defined as the mean shortfall of the total population from the poverty line counting the non-poor as having zero shortfall and expressed as a percentage of the poverty line. It is worth unpacking that definition a little. For those below the poverty line, the shortfall corresponds to the amount of money required in order to reach the poverty line. For those at or above the poverty line, the shortfall is counted as zero. The average shortfall is then calculated across the total population – both poor and non-poor – and then expressed as a share of the poverty line. Unlike the more commonly-used metric of the headcount ratio, the poverty gap index is thus sensitive not only to whether a person’s income falls below the poverty line or not, but also by how much – i.e. to the depth of poverty they experience.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 3;6;9;12;15;18;21;24;27;30 RdPu Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Poverty gap index - $10 a day (after tax) poverty_gap_index_dhi_pc_1000 Numeric The poverty gap index calculated at a poverty line of $10 a day. The poverty gap index is a measure that reflects both the depth and prevalence of poverty. It is defined as the mean shortfall of the total population from the poverty line counting the non-poor as having zero shortfall and expressed as a percentage of the poverty line. It is worth unpacking that definition a little. For those below the poverty line, the shortfall corresponds to the amount of money required in order to reach the poverty line. For those at or above the poverty line, the shortfall is counted as zero. The average shortfall is then calculated across the total population – both poor and non-poor – and then expressed as a share of the poverty line. Unlike the more commonly-used metric of the headcount ratio, the poverty gap index is thus sensitive not only to whether a person’s income falls below the poverty line or not, but also by how much – i.e. to the depth of poverty they experience.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 5;10;15;20;25;30;35;40;45;50 RdPu Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Poverty gap index - $20 a day (after tax) poverty_gap_index_dhi_pc_2000 Numeric The poverty gap index calculated at a poverty line of $20 a day. The poverty gap index is a measure that reflects both the depth and prevalence of poverty. It is defined as the mean shortfall of the total population from the poverty line counting the non-poor as having zero shortfall and expressed as a percentage of the poverty line. It is worth unpacking that definition a little. For those below the poverty line, the shortfall corresponds to the amount of money required in order to reach the poverty line. For those at or above the poverty line, the shortfall is counted as zero. The average shortfall is then calculated across the total population – both poor and non-poor – and then expressed as a share of the poverty line. Unlike the more commonly-used metric of the headcount ratio, the poverty gap index is thus sensitive not only to whether a person’s income falls below the poverty line or not, but also by how much – i.e. to the depth of poverty they experience.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 10;20;30;40;50;60;70 RdPu Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Poverty gap index - $30 a day (after tax) poverty_gap_index_dhi_pc_3000 Numeric The poverty gap index calculated at a poverty line of $30 a day. The poverty gap index is a measure that reflects both the depth and prevalence of poverty. It is defined as the mean shortfall of the total population from the poverty line counting the non-poor as having zero shortfall and expressed as a percentage of the poverty line. It is worth unpacking that definition a little. For those below the poverty line, the shortfall corresponds to the amount of money required in order to reach the poverty line. For those at or above the poverty line, the shortfall is counted as zero. The average shortfall is then calculated across the total population – both poor and non-poor – and then expressed as a share of the poverty line. Unlike the more commonly-used metric of the headcount ratio, the poverty gap index is thus sensitive not only to whether a person’s income falls below the poverty line or not, but also by how much – i.e. to the depth of poverty they experience.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 10;20;30;40;50;60;70;80 RdPu Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Poverty gap index - $40 a day (after tax) poverty_gap_index_dhi_pc_4000 Numeric The poverty gap index calculated at a poverty line of $40 a day. The poverty gap index is a measure that reflects both the depth and prevalence of poverty. It is defined as the mean shortfall of the total population from the poverty line counting the non-poor as having zero shortfall and expressed as a percentage of the poverty line. It is worth unpacking that definition a little. For those below the poverty line, the shortfall corresponds to the amount of money required in order to reach the poverty line. For those at or above the poverty line, the shortfall is counted as zero. The average shortfall is then calculated across the total population – both poor and non-poor – and then expressed as a share of the poverty line. Unlike the more commonly-used metric of the headcount ratio, the poverty gap index is thus sensitive not only to whether a person’s income falls below the poverty line or not, but also by how much – i.e. to the depth of poverty they experience.\n\nIncome is 'post-tax' — measured after taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 10;20;30;40;50;60;70;80;90 RdPu Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Share below 40% of median (after tax) headcount_ratio_40_median_dhi_pc Numeric % of population living in households with income below 40% of the median income.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'post-tax' — 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 5;10;15;20;25;30 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Share below 50% of median (after tax) headcount_ratio_50_median_dhi_pc Numeric % of population living in households with income below 50% of the median income.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'post-tax' — 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 5;10;15;20;25;30 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Share below 60% of median (after tax) headcount_ratio_60_median_dhi_pc Numeric % of population living in households with income below 60% of the median income.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'post-tax' — 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 5;10;15;20;25;30 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Number below 40% of median (after tax) headcount_40_median_dhi_pc Numeric Number of people living in households with income below 40% of the median income.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'post-tax' — 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. 100000;300000;1000000;3000000;10000000;30000000;100000000;300000000;1000000000 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Number below 50% of median (after tax) headcount_50_median_dhi_pc Numeric Number of people living in households with income below 50% of the median income.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'post-tax' — 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. 100000;300000;1000000;3000000;10000000;30000000;100000000;300000000;1000000000 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Number below 60% of median (after tax) headcount_60_median_dhi_pc Numeric Number of people living in households with income below 60% of the median income.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'post-tax' — 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. 100000;300000;1000000;3000000;10000000;30000000;100000000;300000000;1000000000 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Total shortfall - 40% of median (after tax) total_shortfall_40_median_dhi_pc Numeric The total shortfall from a poverty line of 40% of the median income. This is the amount of money that would be theoretically needed to lift the income of all people in poverty up to the poverty line. However this is not a measure of the actual cost of eliminating poverty, since it does not take into account the costs involved in making the necessary transfers nor any changes in behaviour they would bring about.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'post-tax' — 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. 100000000;300000000;1000000000;3000000000;10000000000;30000000000 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Total shortfall - 50% of median (after tax) total_shortfall_50_median_dhi_pc Numeric The total shortfall from a poverty line of 50% of the median income. This is the amount of money that would be theoretically needed to lift the income of all people in poverty up to the poverty line. However this is not a measure of the actual cost of eliminating poverty, since it does not take into account the costs involved in making the necessary transfers nor any changes in behaviour they would bring about.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'post-tax' — 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. 100000000;300000000;1000000000;3000000000;10000000000;30000000000 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Total shortfall - 60% of median (after tax) total_shortfall_60_median_dhi_pc Numeric The total shortfall from a poverty line of 60% of the median income. This is the amount of money that would be theoretically needed to lift the income of all people in poverty up to the poverty line. However this is not a measure of the actual cost of eliminating poverty, since it does not take into account the costs involved in making the necessary transfers nor any changes in behaviour they would bring about.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'post-tax' — 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. 100000000;300000000;1000000000;3000000000;10000000000;30000000000;100000000000 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - 40% of median (after tax) avg_shortfall_40_median_dhi_pc Numeric The average shortfall from a poverty line of of 40% of the median income (averaged across the population in poverty).\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'post-tax' — 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 1000;2000;3000;4000;5000 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - 50% of median (after tax) avg_shortfall_50_median_dhi_pc Numeric The average shortfall from a poverty line of of 50% of the median income (averaged across the population in poverty).\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'post-tax' — 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 1000;2000;3000;4000;5000 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - 60% of median (after tax) avg_shortfall_60_median_dhi_pc Numeric The average shortfall from a poverty line of of 60% of the median income (averaged across the population in poverty).\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'post-tax' — 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 1000;2000;3000;4000;5000 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - 40% of median (after tax) avg_shortfall_40_median_dhi_pc_day Numeric The average shortfall from a poverty line of of 40% of the median income (averaged across the population in poverty).\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'post-tax' — 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 1;2;5;10;20;20.0001 YlOrBr multiplyBy avg_shortfall_40_median_dhi_pc 0.00274 Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - 50% of median (after tax) avg_shortfall_50_median_dhi_pc_day Numeric The average shortfall from a poverty line of of 50% of the median income (averaged across the population in poverty).\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'post-tax' — 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 1;2;5;10;20;20.0001 YlOrBr multiplyBy avg_shortfall_50_median_dhi_pc 0.00274 Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - 60% of median (after tax) avg_shortfall_60_median_dhi_pc_day Numeric The average shortfall from a poverty line of of 60% of the median income (averaged across the population in poverty).\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'post-tax' — 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 1;2;5;10;20;20.0001 YlOrBr multiplyBy avg_shortfall_60_median_dhi_pc 0.00274 Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Income gap ratio - 40% of median (after tax) income_gap_ratio_40_median_dhi_pc Numeric "The average shortfall from a poverty line of of 40% of the median income (averaged across the population in poverty) expressed as a share of the poverty line. This metric is sometimes called the ""income gap ratio"". It captures the depth of poverty of those living on less than the poverty line.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'post-tax' — 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty." % % 5;10;15;20;25;30;35;40 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Income gap ratio - 50% of median (after tax) income_gap_ratio_50_median_dhi_pc Numeric "The average shortfall from a poverty line of of 50% of the median income (averaged across the population in poverty) expressed as a share of the poverty line. This metric is sometimes called the ""income gap ratio"". It captures the depth of poverty of those living on less than the poverty line.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'post-tax' — 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty." % % 5;10;15;20;25;30;35;40 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Income gap ratio - 60% of median (after tax) income_gap_ratio_60_median_dhi_pc Numeric "The average shortfall from a poverty line of of 60% of the median income (averaged across the population in poverty) expressed as a share of the poverty line. This metric is sometimes called the ""income gap ratio"". It captures the depth of poverty of those living on less than the poverty line.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'post-tax' — 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty." % % 5;10;15;20;25;30;35;40 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Poverty gap index - 40% of median (after tax) poverty_gap_index_40_median_dhi_pc Numeric The poverty gap index calculated at a poverty line of 40% of the median income. The poverty gap index is a measure that reflects both the depth and prevalence of poverty. It is defined as the mean shortfall of the total population from the poverty line counting the non-poor as having zero shortfall and expressed as a percentage of the poverty line. It is worth unpacking that definition a little. For those below the poverty line, the shortfall corresponds to the amount of money required in order to reach the poverty line. For those at or above the poverty line, the shortfall is counted as zero. The average shortfall is then calculated across the total population – both poor and non-poor – and then expressed as a share of the poverty line. Unlike the more commonly-used metric of the headcount ratio, the poverty gap index is thus sensitive not only to whether a person’s income falls below the poverty line or not, but also by how much – i.e. to the depth of poverty they experience.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'post-tax' — 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 2;4;6;8;10;12 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Poverty gap index - 50% of median (after tax) poverty_gap_index_50_median_dhi_pc Numeric The poverty gap index calculated at a poverty line of 50% of the median income. The poverty gap index is a measure that reflects both the depth and prevalence of poverty. It is defined as the mean shortfall of the total population from the poverty line counting the non-poor as having zero shortfall and expressed as a percentage of the poverty line. It is worth unpacking that definition a little. For those below the poverty line, the shortfall corresponds to the amount of money required in order to reach the poverty line. For those at or above the poverty line, the shortfall is counted as zero. The average shortfall is then calculated across the total population – both poor and non-poor – and then expressed as a share of the poverty line. Unlike the more commonly-used metric of the headcount ratio, the poverty gap index is thus sensitive not only to whether a person’s income falls below the poverty line or not, but also by how much – i.e. to the depth of poverty they experience.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'post-tax' — 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 2;4;6;8;10;12 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Poverty gap index - 60% of median (after tax) poverty_gap_index_60_median_dhi_pc Numeric The poverty gap index calculated at a poverty line of 60% of the median income. The poverty gap index is a measure that reflects both the depth and prevalence of poverty. It is defined as the mean shortfall of the total population from the poverty line counting the non-poor as having zero shortfall and expressed as a percentage of the poverty line. It is worth unpacking that definition a little. For those below the poverty line, the shortfall corresponds to the amount of money required in order to reach the poverty line. For those at or above the poverty line, the shortfall is counted as zero. The average shortfall is then calculated across the total population – both poor and non-poor – and then expressed as a share of the poverty line. Unlike the more commonly-used metric of the headcount ratio, the poverty gap index is thus sensitive not only to whether a person’s income falls below the poverty line or not, but also by how much – i.e. to the depth of poverty they experience.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'post-tax' — 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 2;4;6;8;10;12 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Share below $1 a day (before tax) headcount_ratio_mi_eq_100 Numeric % of population living in households with income below $1 a day.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 3;10;20;30;40;50;60;70;80;90;100 OrRd Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Share below $2 a day (before tax) headcount_ratio_mi_eq_200 Numeric % of population living in households with income below $2 a day.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 3;10;20;30;40;50;60;70;80;90;100 OrRd Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Share below $5 a day (before tax) headcount_ratio_mi_eq_500 Numeric % of population living in households with income below $5 a day.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 3;10;20;30;40;50;60;70;80;90;100 OrRd Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Share below $10 a day (before tax) headcount_ratio_mi_eq_1000 Numeric % of population living in households with income below $10 a day.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 3;10;20;30;40;50;60;70;80;90;100 OrRd Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Share below $20 a day (before tax) headcount_ratio_mi_eq_2000 Numeric % of population living in households with income below $20 a day.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 3;10;20;30;40;50;60;70;80;90;100 OrRd Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Share below $30 a day (before tax) headcount_ratio_mi_eq_3000 Numeric % of population living in households with income below $30 a day.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 3;10;20;30;40;50;60;70;80;90;100 OrRd Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Share below $40 a day (before tax) headcount_ratio_mi_eq_4000 Numeric % of population living in households with income below $40 a day.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 3;10;20;30;40;50;60;70;80;90;100 OrRd Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Number below $1 a day (before tax) headcount_mi_eq_100 Numeric Number of people living in households with income below $1 a day.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. 100000;300000;1000000;3000000;10000000;30000000;100000000;300000000;1000000000 Reds Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Number below $2 a day (before tax) headcount_mi_eq_200 Numeric Number of people living in households with income below $2 a day.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. 100000;300000;1000000;3000000;10000000;30000000;100000000;300000000;1000000000 Reds Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Number below $5 a day (before tax) headcount_mi_eq_500 Numeric Number of people living in households with income below $5 a day.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. 100000;300000;1000000;3000000;10000000;30000000;100000000;300000000;1000000000 Reds Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Number below $10 a day (before tax) headcount_mi_eq_1000 Numeric Number of people living in households with income below $10 a day.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. 100000;300000;1000000;3000000;10000000;30000000;100000000;300000000;1000000000 Reds Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Number below $20 a day (before tax) headcount_mi_eq_2000 Numeric Number of people living in households with income below $20 a day.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. 100000;300000;1000000;3000000;10000000;30000000;100000000;300000000;1000000000 Reds Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Number below $30 a day (before tax) headcount_mi_eq_3000 Numeric Number of people living in households with income below $30 a day.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. 100000;300000;1000000;3000000;10000000;30000000;100000000;300000000;1000000000 Reds Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Number below $40 a day (before tax) headcount_mi_eq_4000 Numeric Number of people living in households with income below $40 a day.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. 100000;300000;1000000;3000000;10000000;30000000;100000000;300000000;1000000000 Reds Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Total shortfall - $1 a day (before tax) total_shortfall_mi_eq_100 Numeric The total shortfall from a poverty line of $1 a day. This is the amount of money that would be theoretically needed to lift the income of all people in poverty up to the poverty line. However this is not a measure of the actual cost of eliminating poverty, since it does not take into account the costs involved in making the necessary transfers nor any changes in behaviour they would bring about.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 1000000;3000000;10000000;30000000;100000000;300000000;1000000000 Oranges Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Total shortfall - $2 a day (before tax) total_shortfall_mi_eq_200 Numeric The total shortfall from a poverty line of $2 a day. This is the amount of money that would be theoretically needed to lift the income of all people in poverty up to the poverty line. However this is not a measure of the actual cost of eliminating poverty, since it does not take into account the costs involved in making the necessary transfers nor any changes in behaviour they would bring about.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 1000000;3000000;10000000;30000000;100000000;300000000;1000000000;3000000000;10000000000 Oranges Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Total shortfall - $5 a day (before tax) total_shortfall_mi_eq_500 Numeric The total shortfall from a poverty line of $5 a day. This is the amount of money that would be theoretically needed to lift the income of all people in poverty up to the poverty line. However this is not a measure of the actual cost of eliminating poverty, since it does not take into account the costs involved in making the necessary transfers nor any changes in behaviour they would bring about.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 100000000;300000000;1000000000;3000000000;10000000000;30000000000;100000000000;300000000000 Oranges Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Total shortfall - $10 a day (before tax) total_shortfall_mi_eq_1000 Numeric The total shortfall from a poverty line of $10 a day. This is the amount of money that would be theoretically needed to lift the income of all people in poverty up to the poverty line. However this is not a measure of the actual cost of eliminating poverty, since it does not take into account the costs involved in making the necessary transfers nor any changes in behaviour they would bring about.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 1000000000;3000000000;10000000000;30000000000;100000000000;300000000000;1000000000000 Oranges Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Total shortfall - $20 a day (before tax) total_shortfall_mi_eq_2000 Numeric The total shortfall from a poverty line of $20 a day. This is the amount of money that would be theoretically needed to lift the income of all people in poverty up to the poverty line. However this is not a measure of the actual cost of eliminating poverty, since it does not take into account the costs involved in making the necessary transfers nor any changes in behaviour they would bring about.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 10000000000;30000000000;100000000000;300000000000;1000000000000 Oranges Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Total shortfall - $30 a day (before tax) total_shortfall_mi_eq_3000 Numeric The total shortfall from a poverty line of $30 a day. This is the amount of money that would be theoretically needed to lift the income of all people in poverty up to the poverty line. However this is not a measure of the actual cost of eliminating poverty, since it does not take into account the costs involved in making the necessary transfers nor any changes in behaviour they would bring about.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 10000000000;30000000000;100000000000;300000000000;1000000000000;3000000000000 Oranges Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Total shortfall - $40 a day (before tax) total_shortfall_mi_eq_4000 Numeric The total shortfall from a poverty line of $40 a day. This is the amount of money that would be theoretically needed to lift the income of all people in poverty up to the poverty line. However this is not a measure of the actual cost of eliminating poverty, since it does not take into account the costs involved in making the necessary transfers nor any changes in behaviour they would bring about.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 10000000000;30000000000;100000000000;300000000000;1000000000000;3000000000000;10000000000000;30000000000000 Oranges Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - $1 a day (before tax) avg_shortfall_mi_eq_100 Numeric The average shortfall from a poverty line of $1 (averaged across the population in poverty).\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 0.2;0.4;0.6;0.8;1 Purples Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - $2 a day (before tax) avg_shortfall_mi_eq_200 Numeric The average shortfall from a poverty line of $2 (averaged across the population in poverty).\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 0.2;0.4;0.6;0.8;1;1.2;1.4;1.6;1.8;2 Purples Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - $5 a day (before tax) avg_shortfall_mi_eq_500 Numeric The average shortfall from a poverty line of $5 (averaged across the population in poverty).\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 0.5;1;1.5;2;2.5;3;3.5;4;4.5;5 Purples Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - $10 a day (before tax) avg_shortfall_mi_eq_1000 Numeric The average shortfall from a poverty line of $10 (averaged across the population in poverty).\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 2;4;6;8;10 Purples Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - $20 a day (before tax) avg_shortfall_mi_eq_2000 Numeric The average shortfall from a poverty line of $20 (averaged across the population in poverty).\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 3;6;9;12;15;18;21 Purples Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - $30 a day (before tax) avg_shortfall_mi_eq_3000 Numeric The average shortfall from a poverty line of $30 (averaged across the population in poverty).\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 5;10;15;20;25;30 Purples Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - $40 a day (before tax) avg_shortfall_mi_eq_4000 Numeric The average shortfall from a poverty line of $40 (averaged across the population in poverty).\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 5;10;15;20;25;30;35;40 Purples Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - $1 a day (before tax) avg_shortfall_mi_eq_100_day Numeric The average shortfall from a poverty line of $1 (averaged across the population in poverty).\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 0.2;0.4;0.6;0.8;1 Purples multiplyBy avg_shortfall_mi_eq_100 0.00274 Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - $2 a day (before tax) avg_shortfall_mi_eq_200_day Numeric The average shortfall from a poverty line of $2 (averaged across the population in poverty).\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 0.2;0.4;0.6;0.8;1;1.2;1.4;1.6;1.8;2 Purples multiplyBy avg_shortfall_mi_eq_200 0.00274 Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - $5 a day (before tax) avg_shortfall_mi_eq_500_day Numeric The average shortfall from a poverty line of $5 (averaged across the population in poverty).\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 0.5;1;1.5;2;2.5;3;3.5;4;4.5;5 Purples multiplyBy avg_shortfall_mi_eq_500 0.00274 Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - $10 a day (before tax) avg_shortfall_mi_eq_1000_day Numeric The average shortfall from a poverty line of $10 (averaged across the population in poverty).\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 2;4;6;8;10 Purples multiplyBy avg_shortfall_mi_eq_1000 0.00274 Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - $20 a day (before tax) avg_shortfall_mi_eq_2000_day Numeric The average shortfall from a poverty line of $20 (averaged across the population in poverty).\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 3;6;9;12;15;18;21 Purples multiplyBy avg_shortfall_mi_eq_2000 0.00274 Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - $30 a day (before tax) avg_shortfall_mi_eq_3000_day Numeric The average shortfall from a poverty line of $30 (averaged across the population in poverty).\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 5;10;15;20;25;30 Purples multiplyBy avg_shortfall_mi_eq_3000 0.00274 Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - $40 a day (before tax) avg_shortfall_mi_eq_4000_day Numeric The average shortfall from a poverty line of $40 (averaged across the population in poverty).\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 5;10;15;20;25;30;35;40 Purples multiplyBy avg_shortfall_mi_eq_4000 0.00274 Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Income gap ratio - $1 a day (before tax) income_gap_ratio_mi_eq_100 Numeric "The average shortfall from a poverty line of $1 a day (averaged across the population in poverty) expressed as a share of the poverty line. This metric is sometimes called the ""income gap ratio"". It captures the depth of poverty of those living on less than the poverty line.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty." % % 10;20;30;40;50;60;70;80;90;100 YlOrRd Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Income gap ratio - $2 a day (before tax) income_gap_ratio_mi_eq_200 Numeric "The average shortfall from a poverty line of $2 a day (averaged across the population in poverty) expressed as a share of the poverty line. This metric is sometimes called the ""income gap ratio"". It captures the depth of poverty of those living on less than the poverty line.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty." % % 10;20;30;40;50;60;70;80;90;100 YlOrRd Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Income gap ratio - $5 a day (before tax) income_gap_ratio_mi_eq_500 Numeric "The average shortfall from a poverty line of $5 a day (averaged across the population in poverty) expressed as a share of the poverty line. This metric is sometimes called the ""income gap ratio"". It captures the depth of poverty of those living on less than the poverty line.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty." % % 10;20;30;40;50;60;70;80;90;100 YlOrRd Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Income gap ratio - $10 a day (before tax) income_gap_ratio_mi_eq_1000 Numeric "The average shortfall from a poverty line of $10 a day (averaged across the population in poverty) expressed as a share of the poverty line. This metric is sometimes called the ""income gap ratio"". It captures the depth of poverty of those living on less than the poverty line.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty." % % 10;20;30;40;50;60;70;80;90;100 YlOrRd Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Income gap ratio - $20 a day (before tax) income_gap_ratio_mi_eq_2000 Numeric "The average shortfall from a poverty line of $20 a day (averaged across the population in poverty) expressed as a share of the poverty line. This metric is sometimes called the ""income gap ratio"". It captures the depth of poverty of those living on less than the poverty line.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty." % % 10;20;30;40;50;60;70;80;90;100 YlOrRd Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Income gap ratio - $30 a day (before tax) income_gap_ratio_mi_eq_3000 Numeric "The average shortfall from a poverty line of $30 a day (averaged across the population in poverty) expressed as a share of the poverty line. This metric is sometimes called the ""income gap ratio"". It captures the depth of poverty of those living on less than the poverty line.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty." % % 10;20;30;40;50;60;70;80;90;100 YlOrRd Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Income gap ratio - $40 a day (before tax) income_gap_ratio_mi_eq_4000 Numeric "The average shortfall from a poverty line of $40 a day (averaged across the population in poverty) expressed as a share of the poverty line. This metric is sometimes called the ""income gap ratio"". It captures the depth of poverty of those living on less than the poverty line.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty." % % 10;20;30;40;50;60;70;80;90;100 YlOrRd Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Poverty gap index - $1 a day (before tax) poverty_gap_index_mi_eq_100 Numeric The poverty gap index calculated at a poverty line of $1 a day. The poverty gap index is a measure that reflects both the depth and prevalence of poverty. It is defined as the mean shortfall of the total population from the poverty line counting the non-poor as having zero shortfall and expressed as a percentage of the poverty line. It is worth unpacking that definition a little. For those below the poverty line, the shortfall corresponds to the amount of money required in order to reach the poverty line. For those at or above the poverty line, the shortfall is counted as zero. The average shortfall is then calculated across the total population – both poor and non-poor – and then expressed as a share of the poverty line. Unlike the more commonly-used metric of the headcount ratio, the poverty gap index is thus sensitive not only to whether a person’s income falls below the poverty line or not, but also by how much – i.e. to the depth of poverty they experience.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 2;4;6;8;10;12;14;16 RdPu Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Poverty gap index - $2 a day (before tax) poverty_gap_index_mi_eq_200 Numeric The poverty gap index calculated at a poverty line of $2 a day. The poverty gap index is a measure that reflects both the depth and prevalence of poverty. It is defined as the mean shortfall of the total population from the poverty line counting the non-poor as having zero shortfall and expressed as a percentage of the poverty line. It is worth unpacking that definition a little. For those below the poverty line, the shortfall corresponds to the amount of money required in order to reach the poverty line. For those at or above the poverty line, the shortfall is counted as zero. The average shortfall is then calculated across the total population – both poor and non-poor – and then expressed as a share of the poverty line. Unlike the more commonly-used metric of the headcount ratio, the poverty gap index is thus sensitive not only to whether a person’s income falls below the poverty line or not, but also by how much – i.e. to the depth of poverty they experience.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 2;4;6;8;10;12;14;16;18;20 RdPu Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Poverty gap index - $5 a day (before tax) poverty_gap_index_mi_eq_500 Numeric The poverty gap index calculated at a poverty line of $5 a day. The poverty gap index is a measure that reflects both the depth and prevalence of poverty. It is defined as the mean shortfall of the total population from the poverty line counting the non-poor as having zero shortfall and expressed as a percentage of the poverty line. It is worth unpacking that definition a little. For those below the poverty line, the shortfall corresponds to the amount of money required in order to reach the poverty line. For those at or above the poverty line, the shortfall is counted as zero. The average shortfall is then calculated across the total population – both poor and non-poor – and then expressed as a share of the poverty line. Unlike the more commonly-used metric of the headcount ratio, the poverty gap index is thus sensitive not only to whether a person’s income falls below the poverty line or not, but also by how much – i.e. to the depth of poverty they experience.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 4;8;12;16;20;14;28 RdPu Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Poverty gap index - $10 a day (before tax) poverty_gap_index_mi_eq_1000 Numeric The poverty gap index calculated at a poverty line of $10 a day. The poverty gap index is a measure that reflects both the depth and prevalence of poverty. It is defined as the mean shortfall of the total population from the poverty line counting the non-poor as having zero shortfall and expressed as a percentage of the poverty line. It is worth unpacking that definition a little. For those below the poverty line, the shortfall corresponds to the amount of money required in order to reach the poverty line. For those at or above the poverty line, the shortfall is counted as zero. The average shortfall is then calculated across the total population – both poor and non-poor – and then expressed as a share of the poverty line. Unlike the more commonly-used metric of the headcount ratio, the poverty gap index is thus sensitive not only to whether a person’s income falls below the poverty line or not, but also by how much – i.e. to the depth of poverty they experience.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 5;10;15;20;25;35;40 RdPu Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Poverty gap index - $20 a day (before tax) poverty_gap_index_mi_eq_2000 Numeric The poverty gap index calculated at a poverty line of $20 a day. The poverty gap index is a measure that reflects both the depth and prevalence of poverty. It is defined as the mean shortfall of the total population from the poverty line counting the non-poor as having zero shortfall and expressed as a percentage of the poverty line. It is worth unpacking that definition a little. For those below the poverty line, the shortfall corresponds to the amount of money required in order to reach the poverty line. For those at or above the poverty line, the shortfall is counted as zero. The average shortfall is then calculated across the total population – both poor and non-poor – and then expressed as a share of the poverty line. Unlike the more commonly-used metric of the headcount ratio, the poverty gap index is thus sensitive not only to whether a person’s income falls below the poverty line or not, but also by how much – i.e. to the depth of poverty they experience.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 10;20;30;40;50;60 RdPu Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Poverty gap index - $30 a day (before tax) poverty_gap_index_mi_eq_3000 Numeric The poverty gap index calculated at a poverty line of $30 a day. The poverty gap index is a measure that reflects both the depth and prevalence of poverty. It is defined as the mean shortfall of the total population from the poverty line counting the non-poor as having zero shortfall and expressed as a percentage of the poverty line. It is worth unpacking that definition a little. For those below the poverty line, the shortfall corresponds to the amount of money required in order to reach the poverty line. For those at or above the poverty line, the shortfall is counted as zero. The average shortfall is then calculated across the total population – both poor and non-poor – and then expressed as a share of the poverty line. Unlike the more commonly-used metric of the headcount ratio, the poverty gap index is thus sensitive not only to whether a person’s income falls below the poverty line or not, but also by how much – i.e. to the depth of poverty they experience.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 10;20;30;40;50;60;70 RdPu Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Poverty gap index - $40 a day (before tax) poverty_gap_index_mi_eq_4000 Numeric The poverty gap index calculated at a poverty line of $40 a day. The poverty gap index is a measure that reflects both the depth and prevalence of poverty. It is defined as the mean shortfall of the total population from the poverty line counting the non-poor as having zero shortfall and expressed as a percentage of the poverty line. It is worth unpacking that definition a little. For those below the poverty line, the shortfall corresponds to the amount of money required in order to reach the poverty line. For those at or above the poverty line, the shortfall is counted as zero. The average shortfall is then calculated across the total population – both poor and non-poor – and then expressed as a share of the poverty line. Unlike the more commonly-used metric of the headcount ratio, the poverty gap index is thus sensitive not only to whether a person’s income falls below the poverty line or not, but also by how much – i.e. to the depth of poverty they experience.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – adjusted to account for the fact that people in the same household can share costs like rent and heating.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 10;20;30;40;50;60;70;80 RdPu Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Share below 40% of median (before tax) headcount_ratio_40_median_mi_eq Numeric % of population living in households with income below 40% of the median income.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 5;10;15;20;25;30 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Share below 50% of median (before tax) headcount_ratio_50_median_mi_eq Numeric % of population living in households with income below 50% of the median income.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 5;10;15;20;25;30 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Share below 60% of median (before tax) headcount_ratio_60_median_mi_eq Numeric % of population living in households with income below 60% of the median income.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 5;10;15;20;25;30 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Number below 40% of median (before tax) headcount_40_median_mi_eq Numeric Number of people living in households with income below 40% of the median income.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. 100000;300000;1000000;3000000;10000000;30000000;100000000;300000000;1000000000 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Number below 50% of median (before tax) headcount_50_median_mi_eq Numeric Number of people living in households with income below 50% of the median income.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. 100000;300000;1000000;3000000;10000000;30000000;100000000;300000000;1000000000 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Number below 60% of median (before tax) headcount_60_median_mi_eq Numeric Number of people living in households with income below 60% of the median income.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. 100000;300000;1000000;3000000;10000000;30000000;100000000;300000000;1000000000 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Total shortfall - 40% of median (before tax) total_shortfall_40_median_mi_eq Numeric The total shortfall from a poverty line of 40% of the median income. This is the amount of money that would be theoretically needed to lift the income of all people in poverty up to the poverty line. However this is not a measure of the actual cost of eliminating poverty, since it does not take into account the costs involved in making the necessary transfers nor any changes in behaviour they would bring about.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. 100000000;300000000;1000000000;3000000000;10000000000;30000000000 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Total shortfall - 50% of median (before tax) total_shortfall_50_median_mi_eq Numeric The total shortfall from a poverty line of 50% of the median income. This is the amount of money that would be theoretically needed to lift the income of all people in poverty up to the poverty line. However this is not a measure of the actual cost of eliminating poverty, since it does not take into account the costs involved in making the necessary transfers nor any changes in behaviour they would bring about.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. 100000000;300000000;1000000000;3000000000;10000000000;30000000000 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Total shortfall - 60% of median (before tax) total_shortfall_60_median_mi_eq Numeric The total shortfall from a poverty line of 60% of the median income. This is the amount of money that would be theoretically needed to lift the income of all people in poverty up to the poverty line. However this is not a measure of the actual cost of eliminating poverty, since it does not take into account the costs involved in making the necessary transfers nor any changes in behaviour they would bring about.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. 100000000;300000000;1000000000;3000000000;10000000000;30000000000;100000000000 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - 40% of median (before tax) avg_shortfall_40_median_mi_eq Numeric The average shortfall from a poverty line of of 40% of the median income (averaged across the population in poverty).\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 1000;2000;3000;4000;5000 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - 50% of median (before tax) avg_shortfall_50_median_mi_eq Numeric The average shortfall from a poverty line of of 50% of the median income (averaged across the population in poverty).\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 1000;2000;3000;4000;5000 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - 60% of median (before tax) avg_shortfall_60_median_mi_eq Numeric The average shortfall from a poverty line of of 60% of the median income (averaged across the population in poverty).\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 1000;2000;3000;4000;5000 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - 40% of median (before tax) avg_shortfall_40_median_mi_eq_day Numeric The average shortfall from a poverty line of of 40% of the median income (averaged across the population in poverty).\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 1;2;5;10;20;20.0001 YlOrBr multiplyBy avg_shortfall_40_median_mi_eq 0.00274 Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - 50% of median (before tax) avg_shortfall_50_median_mi_eq_day Numeric The average shortfall from a poverty line of of 50% of the median income (averaged across the population in poverty).\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 1;2;5;10;20;20.0001 YlOrBr multiplyBy avg_shortfall_50_median_mi_eq 0.00274 Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - 60% of median (before tax) avg_shortfall_60_median_mi_eq_day Numeric The average shortfall from a poverty line of of 60% of the median income (averaged across the population in poverty).\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 1;2;5;10;20;20.0001 YlOrBr multiplyBy avg_shortfall_60_median_mi_eq 0.00274 Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Income gap ratio - 40% of median (before tax) income_gap_ratio_40_median_mi_eq Numeric "The average shortfall from a poverty line of of 40% of the median income (averaged across the population in poverty) expressed as a share of the poverty line. This metric is sometimes called the ""income gap ratio"". It captures the depth of poverty of those living on less than the poverty line.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty." % % 5;10;15;20;25;30;35;40 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Income gap ratio - 50% of median (before tax) income_gap_ratio_50_median_mi_eq Numeric "The average shortfall from a poverty line of of 50% of the median income (averaged across the population in poverty) expressed as a share of the poverty line. This metric is sometimes called the ""income gap ratio"". It captures the depth of poverty of those living on less than the poverty line.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty." % % 5;10;15;20;25;30;35;40 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Income gap ratio - 60% of median (before tax) income_gap_ratio_60_median_mi_eq Numeric "The average shortfall from a poverty line of of 60% of the median income (averaged across the population in poverty) expressed as a share of the poverty line. This metric is sometimes called the ""income gap ratio"". It captures the depth of poverty of those living on less than the poverty line.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty." % % 5;10;15;20;25;30;35;40 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Poverty gap index - 40% of median (before tax) poverty_gap_index_40_median_mi_eq Numeric The poverty gap index calculated at a poverty line of 40% of the median income. The poverty gap index is a measure that reflects both the depth and prevalence of poverty. It is defined as the mean shortfall of the total population from the poverty line counting the non-poor as having zero shortfall and expressed as a percentage of the poverty line. It is worth unpacking that definition a little. For those below the poverty line, the shortfall corresponds to the amount of money required in order to reach the poverty line. For those at or above the poverty line, the shortfall is counted as zero. The average shortfall is then calculated across the total population – both poor and non-poor – and then expressed as a share of the poverty line. Unlike the more commonly-used metric of the headcount ratio, the poverty gap index is thus sensitive not only to whether a person’s income falls below the poverty line or not, but also by how much – i.e. to the depth of poverty they experience.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 2;4;6;8;10;12 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Poverty gap index - 50% of median (before tax) poverty_gap_index_50_median_mi_eq Numeric The poverty gap index calculated at a poverty line of 50% of the median income. The poverty gap index is a measure that reflects both the depth and prevalence of poverty. It is defined as the mean shortfall of the total population from the poverty line counting the non-poor as having zero shortfall and expressed as a percentage of the poverty line. It is worth unpacking that definition a little. For those below the poverty line, the shortfall corresponds to the amount of money required in order to reach the poverty line. For those at or above the poverty line, the shortfall is counted as zero. The average shortfall is then calculated across the total population – both poor and non-poor – and then expressed as a share of the poverty line. Unlike the more commonly-used metric of the headcount ratio, the poverty gap index is thus sensitive not only to whether a person’s income falls below the poverty line or not, but also by how much – i.e. to the depth of poverty they experience.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 2;4;6;8;10;12 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Poverty gap index - 60% of median (before tax) poverty_gap_index_60_median_mi_eq Numeric The poverty gap index calculated at a poverty line of 60% of the median income. The poverty gap index is a measure that reflects both the depth and prevalence of poverty. It is defined as the mean shortfall of the total population from the poverty line counting the non-poor as having zero shortfall and expressed as a percentage of the poverty line. It is worth unpacking that definition a little. For those below the poverty line, the shortfall corresponds to the amount of money required in order to reach the poverty line. For those at or above the poverty line, the shortfall is counted as zero. The average shortfall is then calculated across the total population – both poor and non-poor – and then expressed as a share of the poverty line. Unlike the more commonly-used metric of the headcount ratio, the poverty gap index is thus sensitive not only to whether a person’s income falls below the poverty line or not, but also by how much – i.e. to the depth of poverty they experience.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome has been [equivalized](#dod:equivalization) – 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 2;4;6;8;10;12 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Share below $1 a day (before tax) headcount_ratio_mi_pc_100 Numeric % of population living in households with income below $1 a day.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 3;10;20;30;40;50;60;70;80;90;100 OrRd Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Share below $2 a day (before tax) headcount_ratio_mi_pc_200 Numeric % of population living in households with income below $2 a day.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 3;10;20;30;40;50;60;70;80;90;100 OrRd Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Share below $5 a day (before tax) headcount_ratio_mi_pc_500 Numeric % of population living in households with income below $5 a day.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 3;10;20;30;40;50;60;70;80;90;100 OrRd Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Share below $10 a day (before tax) headcount_ratio_mi_pc_1000 Numeric % of population living in households with income below $10 a day.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 3;10;20;30;40;50;60;70;80;90;100 OrRd Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Share below $20 a day (before tax) headcount_ratio_mi_pc_2000 Numeric % of population living in households with income below $20 a day.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 3;10;20;30;40;50;60;70;80;90;100 OrRd Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Share below $30 a day (before tax) headcount_ratio_mi_pc_3000 Numeric % of population living in households with income below $30 a day.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 3;10;20;30;40;50;60;70;80;90;100 OrRd Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Share below $40 a day (before tax) headcount_ratio_mi_pc_4000 Numeric % of population living in households with income below $40 a day.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 3;10;20;30;40;50;60;70;80;90;100 OrRd Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Number below $1 a day (before tax) headcount_mi_pc_100 Numeric Number of people living in households with income below $1 a day.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. 100000;300000;1000000;3000000;10000000;30000000;100000000;300000000;1000000000 Reds Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Number below $2 a day (before tax) headcount_mi_pc_200 Numeric Number of people living in households with income below $2 a day.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. 100000;300000;1000000;3000000;10000000;30000000;100000000;300000000;1000000000 Reds Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Number below $5 a day (before tax) headcount_mi_pc_500 Numeric Number of people living in households with income below $5 a day.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. 100000;300000;1000000;3000000;10000000;30000000;100000000;300000000;1000000000 Reds Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Number below $10 a day (before tax) headcount_mi_pc_1000 Numeric Number of people living in households with income below $10 a day.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. 100000;300000;1000000;3000000;10000000;30000000;100000000;300000000;1000000000 Reds Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Number below $20 a day (before tax) headcount_mi_pc_2000 Numeric Number of people living in households with income below $20 a day.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. 100000;300000;1000000;3000000;10000000;30000000;100000000;300000000;1000000000 Reds Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Number below $30 a day (before tax) headcount_mi_pc_3000 Numeric Number of people living in households with income below $30 a day.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. 100000;300000;1000000;3000000;10000000;30000000;100000000;300000000;1000000000 Reds Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Number below $40 a day (before tax) headcount_mi_pc_4000 Numeric Number of people living in households with income below $40 a day.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. 100000;300000;1000000;3000000;10000000;30000000;100000000;300000000;1000000000 Reds Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Total shortfall - $1 a day (before tax) total_shortfall_mi_pc_100 Numeric The total shortfall from a poverty line of $1 a day. This is the amount of money that would be theoretically needed to lift the income of all people in poverty up to the poverty line. However this is not a measure of the actual cost of eliminating poverty, since it does not take into account the costs involved in making the necessary transfers nor any changes in behaviour they would bring about.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 1000000;3000000;10000000;30000000;100000000;300000000;1000000000 Oranges Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Total shortfall - $2 a day (before tax) total_shortfall_mi_pc_200 Numeric The total shortfall from a poverty line of $2 a day. This is the amount of money that would be theoretically needed to lift the income of all people in poverty up to the poverty line. However this is not a measure of the actual cost of eliminating poverty, since it does not take into account the costs involved in making the necessary transfers nor any changes in behaviour they would bring about.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 1000000;3000000;10000000;30000000;100000000;300000000;1000000000;3000000000;10000000000 Oranges Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Total shortfall - $5 a day (before tax) total_shortfall_mi_pc_500 Numeric The total shortfall from a poverty line of $5 a day. This is the amount of money that would be theoretically needed to lift the income of all people in poverty up to the poverty line. However this is not a measure of the actual cost of eliminating poverty, since it does not take into account the costs involved in making the necessary transfers nor any changes in behaviour they would bring about.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 100000000;300000000;1000000000;3000000000;10000000000;30000000000;100000000000;300000000000 Oranges Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Total shortfall - $10 a day (before tax) total_shortfall_mi_pc_1000 Numeric The total shortfall from a poverty line of $10 a day. This is the amount of money that would be theoretically needed to lift the income of all people in poverty up to the poverty line. However this is not a measure of the actual cost of eliminating poverty, since it does not take into account the costs involved in making the necessary transfers nor any changes in behaviour they would bring about.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 1000000000;3000000000;10000000000;30000000000;100000000000;300000000000;1000000000000 Oranges Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Total shortfall - $20 a day (before tax) total_shortfall_mi_pc_2000 Numeric The total shortfall from a poverty line of $20 a day. This is the amount of money that would be theoretically needed to lift the income of all people in poverty up to the poverty line. However this is not a measure of the actual cost of eliminating poverty, since it does not take into account the costs involved in making the necessary transfers nor any changes in behaviour they would bring about.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 10000000000;30000000000;100000000000;300000000000;1000000000000 Oranges Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Total shortfall - $30 a day (before tax) total_shortfall_mi_pc_3000 Numeric The total shortfall from a poverty line of $30 a day. This is the amount of money that would be theoretically needed to lift the income of all people in poverty up to the poverty line. However this is not a measure of the actual cost of eliminating poverty, since it does not take into account the costs involved in making the necessary transfers nor any changes in behaviour they would bring about.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 10000000000;30000000000;100000000000;300000000000;1000000000000;3000000000000 Oranges Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Total shortfall - $40 a day (before tax) total_shortfall_mi_pc_4000 Numeric The total shortfall from a poverty line of $40 a day. This is the amount of money that would be theoretically needed to lift the income of all people in poverty up to the poverty line. However this is not a measure of the actual cost of eliminating poverty, since it does not take into account the costs involved in making the necessary transfers nor any changes in behaviour they would bring about.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 10000000000;30000000000;100000000000;300000000000;1000000000000;3000000000000;10000000000000;30000000000000 Oranges Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - $1 a day (before tax) avg_shortfall_mi_pc_100 Numeric The average shortfall from a poverty line of $1 (averaged across the population in poverty).\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 0.2;0.4;0.6;0.8;1 Purples Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - $2 a day (before tax) avg_shortfall_mi_pc_200 Numeric The average shortfall from a poverty line of $2 (averaged across the population in poverty).\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 0.2;0.4;0.6;0.8;1;1.2;1.4;1.6;1.8;2 Purples Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - $5 a day (before tax) avg_shortfall_mi_pc_500 Numeric The average shortfall from a poverty line of $5 (averaged across the population in poverty).\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 0.5;1;1.5;2;2.5;3;3.5;4;4.5;5 Purples Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - $10 a day (before tax) avg_shortfall_mi_pc_1000 Numeric The average shortfall from a poverty line of $10 (averaged across the population in poverty).\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 2;4;6;8;10 Purples Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - $20 a day (before tax) avg_shortfall_mi_pc_2000 Numeric The average shortfall from a poverty line of $20 (averaged across the population in poverty).\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 3;6;9;12;15;18;21 Purples Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - $30 a day (before tax) avg_shortfall_mi_pc_3000 Numeric The average shortfall from a poverty line of $30 (averaged across the population in poverty).\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 5;10;15;20;25;30 Purples Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - $40 a day (before tax) avg_shortfall_mi_pc_4000 Numeric The average shortfall from a poverty line of $40 (averaged across the population in poverty).\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 5;10;15;20;25;30;35;40 Purples Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - $1 a day (before tax) avg_shortfall_mi_pc_100_day Numeric The average shortfall from a poverty line of $1 (averaged across the population in poverty).\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 0.2;0.4;0.6;0.8;1 Purples multiplyBy avg_shortfall_mi_pc_100 0.00274 Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - $2 a day (before tax) avg_shortfall_mi_pc_200_day Numeric The average shortfall from a poverty line of $2 (averaged across the population in poverty).\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 0.2;0.4;0.6;0.8;1;1.2;1.4;1.6;1.8;2 Purples multiplyBy avg_shortfall_mi_pc_200 0.00274 Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - $5 a day (before tax) avg_shortfall_mi_pc_500_day Numeric The average shortfall from a poverty line of $5 (averaged across the population in poverty).\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 0.5;1;1.5;2;2.5;3;3.5;4;4.5;5 Purples multiplyBy avg_shortfall_mi_pc_500 0.00274 Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - $10 a day (before tax) avg_shortfall_mi_pc_1000_day Numeric The average shortfall from a poverty line of $10 (averaged across the population in poverty).\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 2;4;6;8;10 Purples multiplyBy avg_shortfall_mi_pc_1000 0.00274 Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - $20 a day (before tax) avg_shortfall_mi_pc_2000_day Numeric The average shortfall from a poverty line of $20 (averaged across the population in poverty).\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 3;6;9;12;15;18;21 Purples multiplyBy avg_shortfall_mi_pc_2000 0.00274 Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - $30 a day (before tax) avg_shortfall_mi_pc_3000_day Numeric The average shortfall from a poverty line of $30 (averaged across the population in poverty).\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 5;10;15;20;25;30 Purples multiplyBy avg_shortfall_mi_pc_3000 0.00274 Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - $40 a day (before tax) avg_shortfall_mi_pc_4000_day Numeric The average shortfall from a poverty line of $40 (averaged across the population in poverty).\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 5;10;15;20;25;30;35;40 Purples multiplyBy avg_shortfall_mi_pc_4000 0.00274 Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Income gap ratio - $1 a day (before tax) income_gap_ratio_mi_pc_100 Numeric "The average shortfall from a poverty line of $1 a day (averaged across the population in poverty) expressed as a share of the poverty line. This metric is sometimes called the ""income gap ratio"". It captures the depth of poverty of those living on less than the poverty line.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty." % % 10;20;30;40;50;60;70;80;90;100 YlOrRd Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Income gap ratio - $2 a day (before tax) income_gap_ratio_mi_pc_200 Numeric "The average shortfall from a poverty line of $2 a day (averaged across the population in poverty) expressed as a share of the poverty line. This metric is sometimes called the ""income gap ratio"". It captures the depth of poverty of those living on less than the poverty line.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty." % % 10;20;30;40;50;60;70;80;90;100 YlOrRd Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Income gap ratio - $5 a day (before tax) income_gap_ratio_mi_pc_500 Numeric "The average shortfall from a poverty line of $5 a day (averaged across the population in poverty) expressed as a share of the poverty line. This metric is sometimes called the ""income gap ratio"". It captures the depth of poverty of those living on less than the poverty line.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty." % % 10;20;30;40;50;60;70;80;90;100 YlOrRd Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Income gap ratio - $10 a day (before tax) income_gap_ratio_mi_pc_1000 Numeric "The average shortfall from a poverty line of $10 a day (averaged across the population in poverty) expressed as a share of the poverty line. This metric is sometimes called the ""income gap ratio"". It captures the depth of poverty of those living on less than the poverty line.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty." % % 10;20;30;40;50;60;70;80;90;100 YlOrRd Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Income gap ratio - $20 a day (before tax) income_gap_ratio_mi_pc_2000 Numeric "The average shortfall from a poverty line of $20 a day (averaged across the population in poverty) expressed as a share of the poverty line. This metric is sometimes called the ""income gap ratio"". It captures the depth of poverty of those living on less than the poverty line.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty." % % 10;20;30;40;50;60;70;80;90;100 YlOrRd Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Income gap ratio - $30 a day (before tax) income_gap_ratio_mi_pc_3000 Numeric "The average shortfall from a poverty line of $30 a day (averaged across the population in poverty) expressed as a share of the poverty line. This metric is sometimes called the ""income gap ratio"". It captures the depth of poverty of those living on less than the poverty line.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty." % % 10;20;30;40;50;60;70;80;90;100 YlOrRd Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Income gap ratio - $40 a day (before tax) income_gap_ratio_mi_pc_4000 Numeric "The average shortfall from a poverty line of $40 a day (averaged across the population in poverty) expressed as a share of the poverty line. This metric is sometimes called the ""income gap ratio"". It captures the depth of poverty of those living on less than the poverty line.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty." % % 10;20;30;40;50;60;70;80;90;100 YlOrRd Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Poverty gap index - $1 a day (before tax) poverty_gap_index_mi_pc_100 Numeric The poverty gap index calculated at a poverty line of $1 a day. The poverty gap index is a measure that reflects both the depth and prevalence of poverty. It is defined as the mean shortfall of the total population from the poverty line counting the non-poor as having zero shortfall and expressed as a percentage of the poverty line. It is worth unpacking that definition a little. For those below the poverty line, the shortfall corresponds to the amount of money required in order to reach the poverty line. For those at or above the poverty line, the shortfall is counted as zero. The average shortfall is then calculated across the total population – both poor and non-poor – and then expressed as a share of the poverty line. Unlike the more commonly-used metric of the headcount ratio, the poverty gap index is thus sensitive not only to whether a person’s income falls below the poverty line or not, but also by how much – i.e. to the depth of poverty they experience.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 2;4;6;8;10;12;14;16 RdPu Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Poverty gap index - $2 a day (before tax) poverty_gap_index_mi_pc_200 Numeric The poverty gap index calculated at a poverty line of $2 a day. The poverty gap index is a measure that reflects both the depth and prevalence of poverty. It is defined as the mean shortfall of the total population from the poverty line counting the non-poor as having zero shortfall and expressed as a percentage of the poverty line. It is worth unpacking that definition a little. For those below the poverty line, the shortfall corresponds to the amount of money required in order to reach the poverty line. For those at or above the poverty line, the shortfall is counted as zero. The average shortfall is then calculated across the total population – both poor and non-poor – and then expressed as a share of the poverty line. Unlike the more commonly-used metric of the headcount ratio, the poverty gap index is thus sensitive not only to whether a person’s income falls below the poverty line or not, but also by how much – i.e. to the depth of poverty they experience.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 2;4;6;8;10;12;14;16;18;20 RdPu Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Poverty gap index - $5 a day (before tax) poverty_gap_index_mi_pc_500 Numeric The poverty gap index calculated at a poverty line of $5 a day. The poverty gap index is a measure that reflects both the depth and prevalence of poverty. It is defined as the mean shortfall of the total population from the poverty line counting the non-poor as having zero shortfall and expressed as a percentage of the poverty line. It is worth unpacking that definition a little. For those below the poverty line, the shortfall corresponds to the amount of money required in order to reach the poverty line. For those at or above the poverty line, the shortfall is counted as zero. The average shortfall is then calculated across the total population – both poor and non-poor – and then expressed as a share of the poverty line. Unlike the more commonly-used metric of the headcount ratio, the poverty gap index is thus sensitive not only to whether a person’s income falls below the poverty line or not, but also by how much – i.e. to the depth of poverty they experience.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 4;8;12;16;20;14;28 RdPu Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Poverty gap index - $10 a day (before tax) poverty_gap_index_mi_pc_1000 Numeric The poverty gap index calculated at a poverty line of $10 a day. The poverty gap index is a measure that reflects both the depth and prevalence of poverty. It is defined as the mean shortfall of the total population from the poverty line counting the non-poor as having zero shortfall and expressed as a percentage of the poverty line. It is worth unpacking that definition a little. For those below the poverty line, the shortfall corresponds to the amount of money required in order to reach the poverty line. For those at or above the poverty line, the shortfall is counted as zero. The average shortfall is then calculated across the total population – both poor and non-poor – and then expressed as a share of the poverty line. Unlike the more commonly-used metric of the headcount ratio, the poverty gap index is thus sensitive not only to whether a person’s income falls below the poverty line or not, but also by how much – i.e. to the depth of poverty they experience.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 5;10;15;20;25;35;40 RdPu Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Poverty gap index - $20 a day (before tax) poverty_gap_index_mi_pc_2000 Numeric The poverty gap index calculated at a poverty line of $20 a day. The poverty gap index is a measure that reflects both the depth and prevalence of poverty. It is defined as the mean shortfall of the total population from the poverty line counting the non-poor as having zero shortfall and expressed as a percentage of the poverty line. It is worth unpacking that definition a little. For those below the poverty line, the shortfall corresponds to the amount of money required in order to reach the poverty line. For those at or above the poverty line, the shortfall is counted as zero. The average shortfall is then calculated across the total population – both poor and non-poor – and then expressed as a share of the poverty line. Unlike the more commonly-used metric of the headcount ratio, the poverty gap index is thus sensitive not only to whether a person’s income falls below the poverty line or not, but also by how much – i.e. to the depth of poverty they experience.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 10;20;30;40;50;60 RdPu Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Poverty gap index - $30 a day (before tax) poverty_gap_index_mi_pc_3000 Numeric The poverty gap index calculated at a poverty line of $30 a day. The poverty gap index is a measure that reflects both the depth and prevalence of poverty. It is defined as the mean shortfall of the total population from the poverty line counting the non-poor as having zero shortfall and expressed as a percentage of the poverty line. It is worth unpacking that definition a little. For those below the poverty line, the shortfall corresponds to the amount of money required in order to reach the poverty line. For those at or above the poverty line, the shortfall is counted as zero. The average shortfall is then calculated across the total population – both poor and non-poor – and then expressed as a share of the poverty line. Unlike the more commonly-used metric of the headcount ratio, the poverty gap index is thus sensitive not only to whether a person’s income falls below the poverty line or not, but also by how much – i.e. to the depth of poverty they experience.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 10;20;30;40;50;60;70 RdPu Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Poverty gap index - $40 a day (before tax) poverty_gap_index_mi_pc_4000 Numeric The poverty gap index calculated at a poverty line of $40 a day. The poverty gap index is a measure that reflects both the depth and prevalence of poverty. It is defined as the mean shortfall of the total population from the poverty line counting the non-poor as having zero shortfall and expressed as a percentage of the poverty line. It is worth unpacking that definition a little. For those below the poverty line, the shortfall corresponds to the amount of money required in order to reach the poverty line. For those at or above the poverty line, the shortfall is counted as zero. The average shortfall is then calculated across the total population – both poor and non-poor – and then expressed as a share of the poverty line. Unlike the more commonly-used metric of the headcount ratio, the poverty gap index is thus sensitive not only to whether a person’s income falls below the poverty line or not, but also by how much – i.e. to the depth of poverty they experience.\n\nIncome is 'pre-tax' — measured before taxes have been paid and most government benefits have been received.\n\nIncome is per capita, which means that household income is divided by the total number of household members.\n\nThe data is measured in international-$ at 2017 prices – this adjusts for inflation and for differences in living costs between countries.\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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 10;20;30;40;50;60;70;80 RdPu Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Share below 40% of median (before tax) headcount_ratio_40_median_mi_pc Numeric % of population living in households with income below 40% of the median income.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'pre-tax' — 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 5;10;15;20;25;30 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Share below 50% of median (before tax) headcount_ratio_50_median_mi_pc Numeric % of population living in households with income below 50% of the median income.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'pre-tax' — 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 5;10;15;20;25;30 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Share below 60% of median (before tax) headcount_ratio_60_median_mi_pc Numeric % of population living in households with income below 60% of the median income.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'pre-tax' — 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 5;10;15;20;25;30 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Number below 40% of median (before tax) headcount_40_median_mi_pc Numeric Number of people living in households with income below 40% of the median income.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'pre-tax' — 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. 100000;300000;1000000;3000000;10000000;30000000;100000000;300000000;1000000000 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Number below 50% of median (before tax) headcount_50_median_mi_pc Numeric Number of people living in households with income below 50% of the median income.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'pre-tax' — 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. 100000;300000;1000000;3000000;10000000;30000000;100000000;300000000;1000000000 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Number below 60% of median (before tax) headcount_60_median_mi_pc Numeric Number of people living in households with income below 60% of the median income.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'pre-tax' — 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. 100000;300000;1000000;3000000;10000000;30000000;100000000;300000000;1000000000 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Total shortfall - 40% of median (before tax) total_shortfall_40_median_mi_pc Numeric The total shortfall from a poverty line of 40% of the median income. This is the amount of money that would be theoretically needed to lift the income of all people in poverty up to the poverty line. However this is not a measure of the actual cost of eliminating poverty, since it does not take into account the costs involved in making the necessary transfers nor any changes in behaviour they would bring about.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'pre-tax' — 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. 100000000;300000000;1000000000;3000000000;10000000000;30000000000 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Total shortfall - 50% of median (before tax) total_shortfall_50_median_mi_pc Numeric The total shortfall from a poverty line of 50% of the median income. This is the amount of money that would be theoretically needed to lift the income of all people in poverty up to the poverty line. However this is not a measure of the actual cost of eliminating poverty, since it does not take into account the costs involved in making the necessary transfers nor any changes in behaviour they would bring about.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'pre-tax' — 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. 100000000;300000000;1000000000;3000000000;10000000000;30000000000 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Total shortfall - 60% of median (before tax) total_shortfall_60_median_mi_pc Numeric The total shortfall from a poverty line of 60% of the median income. This is the amount of money that would be theoretically needed to lift the income of all people in poverty up to the poverty line. However this is not a measure of the actual cost of eliminating poverty, since it does not take into account the costs involved in making the necessary transfers nor any changes in behaviour they would bring about.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'pre-tax' — 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. 100000000;300000000;1000000000;3000000000;10000000000;30000000000;100000000000 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - 40% of median (before tax) avg_shortfall_40_median_mi_pc Numeric The average shortfall from a poverty line of of 40% of the median income (averaged across the population in poverty).\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'pre-tax' — 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 1000;2000;3000;4000;5000 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - 50% of median (before tax) avg_shortfall_50_median_mi_pc Numeric The average shortfall from a poverty line of of 50% of the median income (averaged across the population in poverty).\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'pre-tax' — 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 1000;2000;3000;4000;5000 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - 60% of median (before tax) avg_shortfall_60_median_mi_pc Numeric The average shortfall from a poverty line of of 60% of the median income (averaged across the population in poverty).\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'pre-tax' — 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 1000;2000;3000;4000;5000 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - 40% of median (before tax) avg_shortfall_40_median_mi_pc_day Numeric The average shortfall from a poverty line of of 40% of the median income (averaged across the population in poverty).\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'pre-tax' — 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 1;2;5;10;20;20.0001 YlOrBr multiplyBy avg_shortfall_40_median_mi_pc 0.00274 Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - 50% of median (before tax) avg_shortfall_50_median_mi_pc_day Numeric The average shortfall from a poverty line of of 50% of the median income (averaged across the population in poverty).\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'pre-tax' — 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 1;2;5;10;20;20.0001 YlOrBr multiplyBy avg_shortfall_50_median_mi_pc 0.00274 Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Average shortfall - 60% of median (before tax) avg_shortfall_60_median_mi_pc_day Numeric The average shortfall from a poverty line of of 60% of the median income (averaged across the population in poverty).\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'pre-tax' — 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. international-$ in 2017 prices $ 1;2;5;10;20;20.0001 YlOrBr multiplyBy avg_shortfall_60_median_mi_pc 0.00274 Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Income gap ratio - 40% of median (before tax) income_gap_ratio_40_median_mi_pc Numeric "The average shortfall from a poverty line of of 40% of the median income (averaged across the population in poverty) expressed as a share of the poverty line. This metric is sometimes called the ""income gap ratio"". It captures the depth of poverty of those living on less than the poverty line.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'pre-tax' — 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty." % % 5;10;15;20;25;30;35;40 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Income gap ratio - 50% of median (before tax) income_gap_ratio_50_median_mi_pc Numeric "The average shortfall from a poverty line of of 50% of the median income (averaged across the population in poverty) expressed as a share of the poverty line. This metric is sometimes called the ""income gap ratio"". It captures the depth of poverty of those living on less than the poverty line.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'pre-tax' — 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty." % % 5;10;15;20;25;30;35;40 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Income gap ratio - 60% of median (before tax) income_gap_ratio_60_median_mi_pc Numeric "The average shortfall from a poverty line of of 60% of the median income (averaged across the population in poverty) expressed as a share of the poverty line. This metric is sometimes called the ""income gap ratio"". It captures the depth of poverty of those living on less than the poverty line.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'pre-tax' — 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty." % % 5;10;15;20;25;30;35;40 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Poverty gap index - 40% of median (before tax) poverty_gap_index_40_median_mi_pc Numeric The poverty gap index calculated at a poverty line of 40% of the median income. The poverty gap index is a measure that reflects both the depth and prevalence of poverty. It is defined as the mean shortfall of the total population from the poverty line counting the non-poor as having zero shortfall and expressed as a percentage of the poverty line. It is worth unpacking that definition a little. For those below the poverty line, the shortfall corresponds to the amount of money required in order to reach the poverty line. For those at or above the poverty line, the shortfall is counted as zero. The average shortfall is then calculated across the total population – both poor and non-poor – and then expressed as a share of the poverty line. Unlike the more commonly-used metric of the headcount ratio, the poverty gap index is thus sensitive not only to whether a person’s income falls below the poverty line or not, but also by how much – i.e. to the depth of poverty they experience.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'pre-tax' — 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 2;4;6;8;10;12 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Poverty gap index - 50% of median (before tax) poverty_gap_index_50_median_mi_pc Numeric The poverty gap index calculated at a poverty line of 50% of the median income. The poverty gap index is a measure that reflects both the depth and prevalence of poverty. It is defined as the mean shortfall of the total population from the poverty line counting the non-poor as having zero shortfall and expressed as a percentage of the poverty line. It is worth unpacking that definition a little. For those below the poverty line, the shortfall corresponds to the amount of money required in order to reach the poverty line. For those at or above the poverty line, the shortfall is counted as zero. The average shortfall is then calculated across the total population – both poor and non-poor – and then expressed as a share of the poverty line. Unlike the more commonly-used metric of the headcount ratio, the poverty gap index is thus sensitive not only to whether a person’s income falls below the poverty line or not, but also by how much – i.e. to the depth of poverty they experience.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'pre-tax' — 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 2;4;6;8;10;12 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true Poverty gap index - 60% of median (before tax) poverty_gap_index_60_median_mi_pc Numeric The poverty gap index calculated at a poverty line of 60% of the median income. The poverty gap index is a measure that reflects both the depth and prevalence of poverty. It is defined as the mean shortfall of the total population from the poverty line counting the non-poor as having zero shortfall and expressed as a percentage of the poverty line. It is worth unpacking that definition a little. For those below the poverty line, the shortfall corresponds to the amount of money required in order to reach the poverty line. For those at or above the poverty line, the shortfall is counted as zero. The average shortfall is then calculated across the total population – both poor and non-poor – and then expressed as a share of the poverty line. Unlike the more commonly-used metric of the headcount ratio, the poverty gap index is thus sensitive not only to whether a person’s income falls below the poverty line or not, but also by how much – i.e. to the depth of poverty they experience.\n\nThis is a measure of _relative_ poverty – it captures the share of people whose income is low by the standards typical in their own country.\n\nIncome is 'pre-tax' — 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’s povdeco function](https://ideas.repec.org/c/boc/bocode/s366004.html). We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). The function generates FGT(0) and FGT(1), headcount ratio and poverty gap index. After extraction, we do further data processing steps to estimate other poverty indicators using these values, population and poverty lines for absolute and relative poverty. % % 2;4;6;8;10;12 YlOrBr Luxembourg Income Study (2024) Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; December 2024). Luxembourg: LIS. https://www.lisdatacenter.org/our-data/lis-database/ 0 5 true | True |