The OECD Income Distribution database (IDD) has been developed to benchmark and monitor countries' performance in the field of income inequality and poverty. It contains a number of standardised indicators based on the central concept of "equivalised household disposable income", i.e. the total income received by the households less the current taxes and transfers they pay, adjusted for household size with an equivalence scale. While household income is only one of the factors shaping people's economic well-being, it is also the one for which comparable data for all OECD countries are most common. Income distribution has a long-standing tradition among household-level statistics, with regular data collections going back to the 1980s (and sometimes earlier) in many OECD countries.

Achieving comparability in this field is a challenge, as national practices differ widely in terms of concepts, measures, and statistical sources. In order to maximise international comparability as well as inter-temporal consistency of data, the IDD data collection and compilation process is based on a common set of statistical conventions (e.g. on income concepts and components). The information obtained by the OECD through a network of national data providers, via a standardized questionnaire, is based on national sources that are deemed to be most representative for each country.

Small changes in estimates between years should be treated with caution as they may not be statistically significant.

Fore more details, please refer to: https://www.oecd.org/els/soc/IDD-Metadata.pdf and https://www.oecd.org/social/income-distribution-database.htm

As of 2023, the European countries who had the greatest share of their national income taken by the top 10 percent of earners were Turkey, Russia, and Georgia, with high earners in these countries taking home around half of all income. By contrast, the top decile in Czechia, Iceland, and Slovakia took home a share of national income almost half as large, at between 26 and 29 percent. On average, the top 10 percent in Europe took home over a third of national income, while the bottom half earned less than a fifth.

1. According to the departmental national income and disposable income published by year, including operating surplus, net property and business income, employee compensation, production and import taxes net, as well as net domestic and foreign current transfer income statistics.2. Collection purpose: Show the distribution of national income and disposable income by department.3. Data collection method: Mainly refer to national economic accounts, household income and expenditure survey reports, financial statistics monthly reports, fiscal statistics monthly reports, and public enterprise final accounts and other related data.

This dataset provides a gridded subnational datasets for Income inequality (Gini coefficient) at admin 1 level Gross national income (GNI) per capita PPP at admin 1 level The datasets are based on reported subnational admin data and spans three decades from 1990 to 2021. The dataset is presented in details in the following publication. Please cite this paper when using data. Chrisendo D, Niva V, Hoffman R, Sayyar SM, Rocha J, Sandström V, Solt F, Kummu M. 2024. Income inequality has increased for over two-thirds of the global population. Preprint. doi: https://doi.org/10.21203/rs.3.rs-5548291/v1 Code is available at following repositories: Gini coefficient data creation: https://github.com/mattikummu/subnatGini GNI per capita data creation: https://github.com/mattikummu/subnatGNI analyses for the article: https://github.com/mattikummu/gini_gni_analyses The following data is given (formats in brackets) Income inequality (Gini coefficient) at admin 0 level (national) (GeoTIFF, gpkg, csv) Income inequality (Gini coefficient) at admin 1 level (subnational) (GeoTIFF, gpkg, csv) Gross national income (GNI) per capita PPP at admin 0 level (national) (GeoTIFF, gpkg, csv) Gross national income (GNI) per capita PPP at admin 1 level (subnational) (GeoTIFF, gpkg, csv) Slope for Gini coefficient at admin 1 level (GeoTIFF; slope is given also in gpk and csv files) Slope for GNI per capita at admin 1 level (GeoTIFF; slope is given also in gpk and csv files) Input data for the script that was used to generate the Gini coefficient (input_data_gini.zip) Input data for the script that was used to generate the GNI per capita PPP (input_data_GNI.zip) Files are named as followsFormat: raster data (GeoTIFF) starts with rast_*, polygon data (gpkg) with polyg_*, and tabulated with tabulated_*. Admin levels: adm0 for admin 0 level, adm1 for admin 1 levelProduct type: _gini_disp_ for gini coefficient based on disposable income _gni_perCapita_ for GNI per capita PPP Metadata Grids Resolution: 5 arc-min (0.083333333 degrees) Spatial extent: Lon: -180, 180; -90, 90 (xmin, xmax, ymin, ymax) Coordinate ref system: EPSG:4326 - WGS 84 Format: Multiband geotiff; one band for each year over 1990-2021 Unit: no unit for Gini coefficient and PPP USD in 2017 international dollars for GNI per capita Geospatial polygon (gpkg) files: Spatial extent: -180, 180; -90, 83.67 (xmin, xmax, ymin, ymax) Temporal extent: annual over 1990-2021 Coordinate ref system: EPSG:4326 - WGS 84 Format: gkpk Unit: no unit for Gini coefficient and PPP USD in 2017 international dollars for GNI per capita

About 50.4 percent of the household income of private households in the U.S. were earned by the highest quintile in 2023, which are the upper 20 percent of the workers. In contrast to that, in the same year, only 3.5 percent of the household income was earned by the lowest quintile. This relation between the quintiles is indicative of the level of income inequality in the United States. Income inequalityIncome inequality is a big topic for public discussion in the United States. About 65 percent of U.S. Americans think that the gap between the rich and the poor has gotten larger in the past ten years. This impression is backed up by U.S. census data showing that the Gini-coefficient for income distribution in the United States has been increasing constantly over the past decades for individuals and households. The Gini coefficient for individual earnings of full-time, year round workers has increased between 1990 and 2020 from 0.36 to 0.42, for example. This indicates an increase in concentration of income. In general, the Gini coefficient is calculated by looking at average income rates. A score of zero would reflect perfect income equality and a score of one indicates a society where one person would have all the money and all other people have nothing. Income distribution is also affected by region. The state of New York had the widest gap between rich and poor people in the United States, with a Gini coefficient of 0.51, as of 2019. In global comparison, South Africa led the ranking of the 20 countries with the biggest inequality in income distribution in 2018. South Africa had a score of 63 points, based on the Gini coefficient. On the other hand, the Gini coefficient stood at 16.6 in Azerbaijan, indicating that income is widely spread among the population and not concentrated on a few rich individuals or families. Slovenia led the ranking of the 20 countries with the greatest income distribution equality in 2018.

NNI: PIR: GG: Rent data was reported at 6,328.000 THB mn in 2016. This records a decrease from the previous number of 7,248.000 THB mn for 2015. NNI: PIR: GG: Rent data is updated yearly, averaging 2,462.000 THB mn from Dec 1990 (Median) to 2016, with 27 observations. The data reached an all-time high of 20,305.000 THB mn in 2012 and a record low of 506.000 THB mn in 1993. NNI: PIR: GG: Rent data remains active status in CEIC and is reported by National Economic and Social Development Board. The data is categorized under Global Database’s Thailand – Table TH.A035: SNA1993: GDP: National Income Distribution: Current Price (Annual).

Thailand Net National Income (NNI) data was reported at 11,370,863.000 THB mn in 2016. This records an increase from the previous number of 10,695,445.000 THB mn for 2015. Thailand Net National Income (NNI) data is updated yearly, averaging 5,082,561.000 THB mn from Dec 1990 (Median) to 2016, with 27 observations. The data reached an all-time high of 11,370,863.000 THB mn in 2016 and a record low of 1,968,657.000 THB mn in 1990. Thailand Net National Income (NNI) data remains active status in CEIC and is reported by National Economic and Social Development Board. The data is categorized under Global Database’s Thailand – Table TH.A035: SNA1993: GDP: National Income Distribution: Current Price (Annual).

Annual indexes and contributions to percent change of real gross domestic product, real gross domestic income, terms of trade and other statistics, 2017=100.

Abstract

The Health Inequality Project uses big data to measure differences in life expectancy by income across areas and identify strategies to improve health outcomes for low-income Americans.

Section 7

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each percentile of the national income distribution. Both race-adjusted and unadjusted estimates are reported.

Source

Section 13

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each percentile of the national income distribution separately by year. Both race-adjusted and unadjusted estimates are reported.

Source

Section 6

This dataset was created on 2020-01-10 18:53:00.508 by merging multiple datasets together. The source datasets for this version were:

Commuting Zone Life Expectancy Estimates by year: CZ-level by-year life expectancy estimates for men and women, by income quartile

Commuting Zone Life Expectancy: Commuting zone (CZ)-level life expectancy estimates for men and women, by income quartile

Commuting Zone Life Expectancy Trends: CZ-level estimates of trends in life expectancy for men and women, by income quartile

Commuting Zone Characteristics: CZ-level characteristics

Commuting Zone Life Expectancy for larger populations: CZ-level life expectancy estimates for men and women, by income ventile

Section 15

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each quartile of the national income distribution by state of residence and year. Both race-adjusted and unadjusted estimates are reported.

Source

Section 11

This table reports US mortality rates by gender, age, year and household income percentile. Household incomes are measured two years prior to the mortality rate for mortality rates at ages 40-63, and at age 61 for mortality rates at ages 64-76. The “lag” variable indicates the number of years between measurement of income and mortality.

Observations with 1 or 2 deaths have been masked: all mortality rates that reflect only 1 or 2 deaths have been recoded to reflect 3 deaths

Source

Section 3

This table reports coefficients and standard errors from regressions of life expectancy estimates for men and women at age 40 for each quartile of the national income distribution on calendar year by commuting zone of residence. Only the slope coefficient, representing the average increase or decrease in life expectancy per year, is reported. Trend estimates for both race-adjusted and unadjusted life expectancies are reported. Estimates are reported for the 100 largest CZs (populations greater than 590,000) only.

Source

Section 9

This table reports life expectancy estimates at age 40 for Males and Females for all countries. Source: World Health Organization, accessed at: http://apps.who.int/gho/athena/

Source

Section 10

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each quartile of the national income distribution by county of residence. Both race-adjusted and unadjusted estimates are reported. Estimates are reported for counties with populations larger than 25,000 only

Source

Section 2

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each quartile of the national income distribution by commuting zone of residence and year. Both race-adjusted and unadjusted estimates are reported. Estimates are reported for the 100 largest CZs (populations greater than 590,000) only.

Source

Section 8

This table reports US population and death counts by age, year, and sex from various sources. Counts labelled “dm1” are derived from the Social Security Administration Data Master 1 file. Counts labelled “irs” are derived from tax data. Counts labelled “cdc” are derived from NCHS life tables.

Source

Section 12

This table reports numerous county characteristics, compiled from various sources. These characteristics are described in the county life expectancy table.

Two variables constructed by the Cen

Graph and download economic data for Gross National Income for United States (MKTGNIUSA646NWDB) from 1960 to 2024 about GNI, income, and USA.

As of 2023, Spain was the major economy in Europe with the highest share of national income taken home by the bottom 50 percent of earners. The country has seen a gradual increase in the share taken by the poorest 50 percent since the 1990s, with this share increasing from roughly 20 percent to over 21 percent in 2023. In stark contrast, Russia is the country which has seen the greatest decrease in the share of income taken by the bottom half. With the end of communist rule in 1991, the income of the poorest Russians nosedived from around 28 percent of national income, to less than 10 percent in 1996. Since then, the bottom half's share in Russia has increased, being approximately 16 percent in 2023.

The period from 1980 to 2023 saw an increase in the share of national income in Europe taken by the top 10 percent of earners. This period has generally been categorized by economists as a period of rising income inequality, especially when compared with the postwar period (1945-1970s) in Europe which saw a compression of the income distribution, with the middle classes in particular making large gains. As financial and labor markets were liberalized in the 1980s and as the effects of economic globalization took hold, however, a growing share of income went to the top earners. This European trend mirrors increases in inequality across the globe during this period, with the United States seeing a particularly sharp rise in the share taken by its top one percent. Rising income inequality has been linked to the rise of populism in Europe throughout the 2000s and 2010s, as voters sought to hit back at economic elites.

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in National City, CA, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 14,140, while the mean income for the highest quintile (20% of households with the highest income) is 183,849. This indicates that the top earners earn 13 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 300,740, which is 163.58% higher compared to the highest quintile, and 2126.87% higher compared to the lowest quintile.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for National City median household income. You can refer the same here

1. Important economic indicators for national income, including economic growth rate, GDP, GNP, NI, and per capita statistics, are published quarterly.2. Purpose of collection: To present important economic indicators in national income statistics.3. Data collection methods: Mainly refer to various surveys conducted by government agencies, public statistics, annual budget and accounting reports of government at all levels.

About this dataset

Context

The dataset tabulates the median household income in National City. It can be utilized to understand the trend in median household income and to analyze the income distribution in National City by household type, size, and across various income brackets.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• National City, CA Median Household Income Trends (2010-2021, in 2022 inflation-adjusted dollars)
• Median Household Income Variation by Family Size in National City, CA: Comparative analysis across 7 household sizes
• Income Distribution by Quintile: Mean Household Income in National City, CA
• National City, CA households by income brackets: family, non-family, and total, in 2022 inflation-adjusted dollars

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of National City median household income. You can refer the same here

Thailand NNI: PIR: HH: Distributed Income of Corporations data was reported at 543,533.000 THB mn in 2016. This records a decrease from the previous number of 553,761.000 THB mn for 2015. Thailand NNI: PIR: HH: Distributed Income of Corporations data is updated yearly, averaging 56,616.000 THB mn from Dec 1990 (Median) to 2016, with 27 observations. The data reached an all-time high of 553,761.000 THB mn in 2015 and a record low of 14,292.000 THB mn in 1990. Thailand NNI: PIR: HH: Distributed Income of Corporations data remains active status in CEIC and is reported by National Economic and Social Development Board. The data is categorized under Global Database’s Thailand – Table TH.A035: GDP: SNA93: National Income Distribution: Current Price (Annual).

1. Statistics on national income, disposable income, savings, and investments announced annually.2. Purpose of collection: To present data on national income, savings, and investments, and their relevance.3. Method of data collection: Mainly refer to relevant data such as national economic accounts, household income and expenditure survey reports, financial statistics monthly reports, fiscal statistics monthly reports, and public enterprise financial statements.

This table describes the income distribution of the sector households in the national accounts over different household groups. Households are identified by main source of income, living situation, household composition, age classes of the head of the household, income class by 20% groups, and net worth class by 20% groups. Data available from: 2015. Status of the figures: All data are provisional. Changes as of October 19th 2023: The figures of 2015-2020 are revised, because national accounts figures are changed due to the revision policy of Statistics Netherlands. Results for 2021 are added to the table. When will new figures be published? New figures will be released in October 2024.

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in National Park, NJ, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 22,309, while the mean income for the highest quintile (20% of households with the highest income) is 256,797. This indicates that the top earners earn 12 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 437,740, which is 170.46% higher compared to the highest quintile, and 1962.17% higher compared to the lowest quintile.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for National Park median household income. You can refer the same here

1. National income statistics data set XML web address link2. Collection purpose: for easy batch download3. Data collection method: aggregation of national income and economic growth data set XML web address links