Cross-national research on the causes and consequences of income inequality has been hindered by the limitations of the existing inequality datasets: greater coverage across countries and over time has been available from these sources only at the cost of significantly reduced comparability across observations. The goal of the Standardized World Income Inequality Database (SWIID) is to meet the needs of those engaged in broadly cross-national research by maximizing the comparability of income inequality data while maintaining the widest possible coverage across countries and over time. The SWIID’s income inequality estimates are based on thousands of reported Gini indices from hundreds of published sources, including the OECD Income Distribution Database, the Socio-Economic Database for Latin America and the Caribbean generated by CEDLAS and the World Bank, Eurostat, the World Bank’s PovcalNet, the UN Economic Commission for Latin America and the Caribbean, national statistical offices around the world, and academic studies while minimizing reliance on problematic assumptions by using as much information as possible from proximate years within the same country. The data collected and harmonized by the Luxembourg Income Study is employed as the standard. The SWIID currently incorporates comparable Gini indices of disposable and market income inequality for 199 countries for as many years as possible from 1960 to the present; it also includes information on absolute and relative redistribution.

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

IRS US Income Data by Zip Code

Number of Returns, Adjusted Gross Income, Total Income, and Taxable Income

By Jon Loyens [source]

About this dataset

This dataset provides a unique insight into the US income patterns in 2013, by zip code. With this data, you can explore how taxes and adjusted gross income (AGI) vary according to geographic area. The data includes total and average incomes reported, number of returns filed in each ZIP code and taxable incomes reported. This dataset is ideal for studying how economic trends have shifted geographically over time or examining regional economic disparities within the US. In addition, this dataset has been cleansed from data removed from items such as ZIP codes with fewer than 100 returns or those identified as a single building or nonresidential ZIP codes that were categorized as “other” (99999) by the IRS. Finally, dollar amounts for all variables are in thousands for better accuracy

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Research Ideas

• Using this dataset to identify potential locations for commercial developments by maping taxable incomes, total income amounts, and average incomes in different zip codes.
• Comparing the number of returns with total income, taxes payable, and income variance between different zip codes to gain insights into areas with higher financial prosperity or disparities between zip codes due to wider economic trends.
• Analyzing average adjusted gross incomes on a state-by-state basis to identify states where high net worth citizens or individuals earning high wages live in order to target marketing campaigns or develop high-end service offerings

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: IRSIncomeByZipCode.csv | Column name | Description | |:------------------------------------------|:-------------------------------------------------------------------------------------| | STATE | The two-letter abbreviation for the state in which the zip code is located. (String) | | ZIPCODE | The five-digit US zip code. (Integer) | | Number of returns | The total number of tax returns filed in the zip code. (Integer) | | Adjusted gross income (AGI) | The total amount of adjusted gross income reported in the zip code. (Integer) | | Avg AGI | The average amount of adjusted gross income reported in the zip code. (Integer) | | Number of returns with total income | The total number of returns with total income reported in the zip code. (Integer) | | Total income amount | The total amount of income reported in the zip code. (Integer) | | Avg total income | The average amount of total income reported in the zip code. (Integer) | | Number of returns with taxable income | The total number of returns with taxable income reported in the zip code. (Integer) | | Taxable income amount | The total amount of taxable income reported in the zip code. (Integer) | | Avg taxable income | The average amount of taxable income reported in the zip code. (Integer) |

File: IRSIncomeByZipCode_NoStateTotalsNoSmallZips.csv | Column name | Description | |:------------------------------------------|:-------------------------------------------------------------------------------------| | STATE | The two-letter abb...

Upvote if its helpful for you Thank You Dive into the intricate relationship between happiness and income inequality with our comprehensive dataset sourced from the World Bank. Uncover key insights into how nations' happiness levels may be influenced by economic disparities. Explore the nuances of global well-being and socioeconomic factors, shedding light on the intricate connections between happiness and income distribution on a worldwide scale. Harness the power of data to gain valuable insights into the factors that contribute to societal contentment and address the complexities of global happiness. Columns in dataset are: Column Names: ['country', 'adjusted_satisfaction', 'avg_satisfaction', 'std_satisfaction', 'avg_income', 'median_income', 'income_inequality', 'region', 'happyScore', 'GDP', 'country.1']

Switzerland Income Share Held by Lowest 10% data was reported at 3.200 % in 2015. This stayed constant from the previous number of 3.200 % for 2014. Switzerland Income Share Held by Lowest 10% data is updated yearly, averaging 3.150 % from Dec 2006 (Median) to 2015, with 10 observations. The data reached an all-time high of 3.300 % in 2013 and a record low of 2.900 % in 2007. Switzerland Income Share Held by Lowest 10% data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Switzerland – Table CH.World Bank.WDI: Poverty. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

This dataset includes combined and standardized Gini data from eight original sources: Luxembourg Income Study (LIS), Socio-Economic Database for Latin America (SEDLAC), Survey of Living Conditions (SILC) by Eurostat, World Income Distribution (WYD; the full data set is available here), World Bank Europe and Central Asia dataset, World Institute for Development Research (WIDER), World Bank Povcal, and Ginis from individual long-term inequality studies (just introduced in this version).

Dataset used in World Bank Policy Research Working Paper #2876, published in World Bank Economic Review, No. 1, 2005, pp. 21-44. The effects of globalization on income distribution in rich and poor countries are a matter of controversy. While international trade theory in its most abstract formulation implies that increased trade and foreign investment should make income distribution more equal in poor countries and less equal in rich countries, finding these effects has proved elusive. The author presents another attempt to discern the effects of globalization by using data from household budget surveys and looking at the impact of openness and foreign direct investment on relative income shares of low and high deciles. The author finds some evidence that at very low average income levels, it is the rich who benefit from openness. As income levels rise to those of countries such as Chile, Colombia, or Czech Republic, for example, the situation changes, and it is the relative income of the poor and the middle class that rises compared with the rich. It seems that openness makes income distribution worse before making it better-or differently in that the effect of openness on a country's income distribution depends on the country's initial income level.

Abstract

Survey based Harmonized Indicators (SHIP) files are harmonized data files from household surveys that are conducted by countries in Africa. To ensure the quality and transparency of the data, it is critical to document the procedures of compiling consumption aggregation and other indicators so that the results can be duplicated with ease. This process enables consistency and continuity that make temporal and cross-country comparisons consistent and more reliable.

Four harmonized data files are prepared for each survey to generate a set of harmonized variables that have the same variable names. Invariably, in each survey, questions are asked in a slightly different way, which poses challenges on consistent definition of harmonized variables. The harmonized household survey data present the best available variables with harmonized definitions, but not identical variables. The four harmonized data files are

a) Individual level file (Labor force indicators in a separate file): This file has information on basic characteristics of individuals such as age and sex, literacy, education, health, anthropometry and child survival. b) Labor force file: This file has information on labor force including employment/unemployment, earnings, sectors of employment, etc. c) Household level file: This file has information on household expenditure, household head characteristics (age and sex, level of education, employment), housing amenities, assets, and access to infrastructure and services. d) Household Expenditure file: This file has consumption/expenditure aggregates by consumption groups according to Purpose (COICOP) of Household Consumption of the UN.

Geographic coverage

National

Analysis unit

• Individual level for datasets with suffix _I and _L
• Household level for datasets with suffix _H and _E

Universe

The survey covered all de jure household members (usual residents).

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Frame The list of households obtained from the 2001/2 Ethiopian Agricultural Sample Enumeration (EASE) was used as a frame to select EAs from the rural part of the country. On the other hand, the list consisting of households by EA, which was obtained from the 2004 Ethiopian Urban Economic Establishment Census, (EUEEC), was used as a frame in order to select sample enumeration areas for the urban HICE survey. A fresh list of households from each urban and rural EA was prepared at the beginning of the survey period. This list was, thus, used as a frame in order to select households from sample EAs.

Sample Design For the purpose of the survey the country was divided into three broad categories. That is; rural, major urban center and other urban center categories.

Category I: Rural: - This category consists of the rural areas of eight regional states and two administrative councils (Addis Ababa and Dire Dawa) of the country, except Gambella region. Each region was considered to be a domain (Reporting Level) for which major findings of the survey are reported. This category comprises 10 reporting levels. A stratified two-stage cluster sample design was used to select samples in which the primary sampling units (PSUs) were EAs. Twelve households per sample EA were selected as a Second Stage Sampling Unit (SSU) to which the survey questionnaire were administered.

Category II:- Major urban centers:- In this category all regional capitals (except Gambella region) and four additional urban centers having higher population sizes as compared to other urban centers were included. Each urban center in this category was considered as a reporting level. However, each sub-city of Addis Ababa was considered to be a domain (reporting levels). Since there is a high variation in the standards of living of the residents of these urban centers (that may have a significant impact on the final results of the survey), each urban center was further stratified into the following three sub-strata. Sub-stratum 1:- Households having a relatively high standards of living Sub-stratum 2:- Households having a relatively medium standards of living and Sub-stratum 3:- Households having a relatively low standards of living. The category has a total of 14 reporting levels. A stratified two-stage cluster sample design was also adopted in this instance. The primary sampling units were EAs of each urban center. Allocation of sample EAs of a reporting level among the above mentioned strata were accomplished in proportion to the number of EAs each stratum consists of. Sixteen households from each sample EA were inally selected as a Secondary Sampling Unit (SSU).

Category III: - Other urban centers: - Urban centers in the country other than those under category II were grouped into this category. Excluding Gambella region a domain of "other urban centers" is formed for each region. Consequently, 7 reporting levels were formed in this category. Harari, Addis Ababa and Dire Dawa do not have urban centers other than that grouped in category II. Hence, no domain was formed for these regions under this category. Unlike the above two categories a stratified three-stage cluster sample design was adopted to select samples from this category. The primary sampling units were urban centers and the second stage sampling units were EAs. Sixteen households from each EA were lastly selected at the third stage and the survey questionnaires administered for all of them.

Mode of data collection

Face-to-face [f2f]

Jordan JO: Income Share Held by Highest 10% data was reported at 27.500 % in 2010. This records an increase from the previous number of 26.600 % for 2008. Jordan JO: Income Share Held by Highest 10% data is updated yearly, averaging 28.100 % from Dec 1986 (Median) to 2010, with 7 observations. The data reached an all-time high of 35.000 % in 1992 and a record low of 26.600 % in 2008. Jordan JO: Income Share Held by Highest 10% data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Jordan – Table JO.World Bank.WDI: Poverty. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

Global Income Inequality Dataset (2000–2023)

Overview

This dataset provides a comprehensive look at global income inequality from the year 2000 to 2023. It includes key indicators such as Gini index, average income, income distribution across different population percentiles, and income group classifications for 30 countries worldwide. The dataset offers insights into how income is distributed within nations and highlights disparities across different economic groups.

Data Features

• Country: The name of the country.
• Year: The year of the data point (2000–2023).
• Population: The estimated population for the given year.
• Gini Index: A measure of income inequality, where 0 represents perfect equality and 1 represents maximum inequality.
• Average Income (USD): The average income in USD for the country in the given year.
• Top 10% Income Share (%): The percentage of total income held by the top 10% of the population.
• Bottom 10% Income Share (%): The percentage of total income held by the bottom 10% of the population.
• Income Group: Categorization of the country’s income group (Low Income, Lower Middle Income, Upper Middle Income, High Income).

Potential Uses

• Economic Analysis: Understand global income inequality trends and how they vary by country and region.
• Predictive Modeling: Use the dataset to build machine learning models predicting future income inequality based on historical data.
• Policy Research: Study the impact of income distribution on policy decisions and economic growth in different nations.
• Visualization: Create heatmaps, time series charts, and more to visualize the income inequality across various countries and years.

Source

The data has been generated to simulate realistic income inequality patterns based on publicly available data on global economic trends.

Belgium BE: Proportion of People Living Below 50 Percent Of Median Income: % data was reported at 8.100 % in 2021. This records an increase from the previous number of 7.900 % for 2020. Belgium BE: Proportion of People Living Below 50 Percent Of Median Income: % data is updated yearly, averaging 9.100 % from Dec 1985 (Median) to 2021, with 25 observations. The data reached an all-time high of 10.500 % in 2015 and a record low of 6.000 % in 1985. Belgium BE: Proportion of People Living Below 50 Percent Of Median Income: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Belgium – Table BE.World Bank.WDI: Social: Poverty and Inequality. The percentage of people in the population who live in households whose per capita income or consumption is below half of the median income or consumption per capita. The median is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries, medians are not reported due to grouped and/or confidential data. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

Netherlands NL: Income Share Held by Lowest 10% data was reported at 3.500 % in 2015. This stayed constant from the previous number of 3.500 % for 2014. Netherlands NL: Income Share Held by Lowest 10% data is updated yearly, averaging 3.500 % from Dec 2004 (Median) to 2015, with 12 observations. The data reached an all-time high of 3.700 % in 2012 and a record low of 3.300 % in 2004. Netherlands NL: Income Share Held by Lowest 10% data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Netherlands – Table NL.World Bank.WDI: Poverty. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

Is the Gini Coefficient Enough? A Microeconomic Data Decomposition StudyIvan Skliarov, Lukasz Goczek (2023).List of data files:1. theil_raw.csv - data obtained from LISSY using the lis_theil.R script.*2. scv_raw.csv - data obtained from LISSY using the scv_theil.R script.*3. hdi.csv - Human Development Index and its components.4. gini.csv - Gini coefficient from SWIID 9.4.5. wdi.csv - World Development Indicators from the World Bank.6. wgi.csv - World Governance Indicators from the World Bank.7. govcon.csv - government consumption (% of GDP) from UNCTAD.8. theil_fin.csv - final dataset (1, 3-7 combined), which is used in lis_analysis.do.9. scv_fin.csv - final dataset (2-7 combined), which is used in lis_analysis.do.10. indexes.csv - only within and between-cohort components of the Theil index and SCV with imputed values (see lis_analysis.do) for Georgia and Lithuania, which is used in lis_plot.R. * LISSY is the remote-execution system allowing access to the Luxembourg Income Study database: https://www.lisdatacenter.org/data-access/lissy/.For questions about this research please contact:Ivan Skliarov, MA: Faculty of Economic Sciences, University of Warsaw, Poland, Długa 44/50, Warsaw 00-241, Poland, i.skliarov@student.uw.edu.pl.Lukasz Goczek, PhD: Faculty of Economic Sciences, University of Warsaw, Poland, Długa 44/50, Warsaw 00-241, Poland, lgoczek@wne.uw.edu.pl.

The UNU-WIDER World Income Inequality Database (WIID) collects and stores information on income inequality for developed, developing, and transition countries.

In 2024, Massachusetts recorded the highest median household income in the United States, at 113,900 U.S. dollars. On the other hand, Mississippi, recorded the lowest, at 55,980 U.S. dollars. Overall, the median income for households in the U.S. was at 83,730 U.S. dollars that year.

In 2024, the median household income in the United States was 83,730 U.S. dollars. This reflected an increase from the previous year. Household income The median household income depicts the income of households, including the income of the householder and all other individuals aged 15 years or over living in the household. Income includes wages and salaries, unemployment insurance, disability payments, child support payments received, regular rental receipts, as well as any personal business, investment, or other kinds of income received routinely. The median household income in the United States varied from state to state. In 2024, Massachusetts recorded the highest median household income in the country, at 113,900 U.S. dollars. On the other hand, Mississippi, recorded the lowest, at 55,980 U.S. dollars.Household income is also used to determine the poverty rate in the United States. In 2024, 10.6 percent of the U.S. population was living below the national poverty line. This was the lowest level since 2019. Similarly, the child poverty rate, which represents people under the age of 18 living in poverty, reached a three-decade low of 14.3 percent of the children. The state with the widest gap between the rich and the poor was New York, with a Gini coefficient score of 0.52 in 2024. The Gini coefficient is calculated by looking at average income rates. A score of zero would reflect perfect income equality, while a score of one indicates complete inequality.

Abstract

This file contains data on Gini coefficients, cumulative quintile shares, explanations regarding the basis on which the Gini coefficient was computed, and the source of the information. There are two data-sets, one containing the "high quality" sample and the other one including all the information (of lower quality) that had been collected.

The database was constructed for the production of the following paper:

Deininger, Klaus and Lyn Squire, "A New Data Set Measuring Income Inequality", The World Bank Economic Review, 10(3): 565-91, 1996.

This article presents a new data set on inequality in the distribution of income. The authors explain the criteria they applied in selecting data on Gini coefficients and on individual quintile groups’ income shares. Comparison of the new data set with existing compilations reveals that the data assembled here represent an improvement in quality and a significant expansion in coverage, although differences in the definition of the underlying data might still affect intertemporal and international comparability. Based on this new data set, the authors do not find a systematic link between growth and changes in aggregate inequality. They do find a strong positive relationship between growth and reduction of poverty.

Geographic coverage

In what follows, we provide brief descriptions of main features for individual countries that are included in the data-base. Without being comprehensive, these notes are intended to indicate some of the considerations underlying our decision to include or exclude certain observations.

Argentina Various permanent household surveys, all covering urban centers only, have been regularly conducted since 1972 and are quoted in a wide variety of sources and years, e.g., for 1980 (World Bank 1992), 1985 (Altimir 1994), and 1989 (World Bank 1992). Estimates for 1963, 1965, 1969/70, 1970/71, 1974, 1975, 1980, and 1981 (Altimir 1987) are based only on Greater Buenos Aires. Estimates for 1961, 1963, 1970 (Jain 1975) and for 1970 (van Ginneken 1984) have only limited geographic coverage and do not satisfy our minimum criteria.

Despite the many urban surveys, there are no income distribution data that are representative of the population as a whole. References to national income distribution for the years 1953, 1959, and 1961(CEPAL 1968 in Altimir 1986 ) are based on extrapolation from national accounts and have therefore not been included. Data for 1953 and 1961 from Weisskoff (1970) , from Lecaillon (1984) , and from Cromwell (1977) are also excluded.

Australia Household surveys, the result of which is reported in the statistical yearbook, have been conducted in 1968/9, 1975/6, 1978/9, 1981, 1985, 1986, 1989, and 1990.

Data for 1962 (Cromwell, 1977) and 1966/67 (Sawyer 1976) were excluded as they covered only tax payers. Jain's data for 1970 was excluded because it covered income recipients only. Data from Podder (1972) for 1967/68, from Jain (1975) for the same year, from UN (1985) for 78/79, from Sunders and Hobbes (1993) for 1986 and for 1989 were excluded given the availability of the primary sources. Data from Bishop (1991) for 1981/82, from Buhman (1988) for 1981/82, from Kakwani (1986) for 1975/76, and from Sunders and Hobbes (1993) for 1986 were utilized to test for the effect of different definitions. The values for 1967 used by Persson and Tabellini and Alesina and Rodrik (based on Paukert and Jain) are close to the ones reported in the Statistical Yearbook for 1969.

Austria: In addition to data referring to the employed population (Guger 1989), national household surveys for 1987 and 1991 are included in the LIS data base. As these data do not include income from self-employment, we do not report them in our high quality data-set.

Bahamas Data for Ginis and shares are available for 1973, 1977, 1979, 1986, 1988, 1989, 1991, 1992, and 1993 in government reports on population censuses and household budget surveys, and for 1973 and 1975 from UN (1981). Estimates for 1970 (Jain 1975), 1973, 1975, 1977, and 1979 (Fields 1989) have been excluded given the availability of primary sources.

Bangladesh Data from household surveys for 1973/74, 1976/77, 1977/78, 1981/82, and 1985/86 are available from the Statistical Yearbook, complemented by household-survey based information from Chen (1995) and the World Development Report. Household surveys with rural coverage for 1959, 1960, 1963/64, 1965, 1966/67 and 1968/69, and with urban coverage for 1963/64, 1965, 1966/67, and 1968/69 are also available from the Statistical yearbook. Data for 1963/64 ,1964 and 1966/67, (Jain 1975) are not included due to limited geographic coverage, We also excluded secondary sources for 1973/74, 1976/77, 1981/82 (Fields 1989), 1977 (UN 1981), 1983 (Milanovic 1994), and 1985/86 due to availability of the primary source.

Barbados National household surveys have been conducted in 1951/52 and 1978/79 (Downs, 1988). Estimates based on personal tax returns, reported consistently for 1951-1981 (Holder and Prescott, 1989), had to be excluded as they exclude the non-wage earning population. Jain's figure (used by Alesina and Rodrik) is based on the same source.

Belgium Household surveys with national coverage are available for 1978/79 (UN 1985), and for 1985, 1988, and 1992 (LIS 1995). Earlier data for 1969, 1973, 1975, 1976 and 1977 (UN 1981) refer to taxable households only and are not included.

Bolivia The only survey with national coverage is the 1990 LSMS (World Development Report). Surveys for 1986 and 1989 cover the main cities only (Psacharopoulos et al. 1992) and are therefore not included. Data for 1968 (Cromwell 1977) do not refer to a clear definition and is therefore excluded.

Botswana The only survey with national coverage was conducted in 1985-1986 (Chen et al 1993); surveys in 74/75 and 85/86 included rural areas only (UN 1981). We excluded Gini estimates for 1971/72 that refer to the economically active population only (Jain 1975), as well as 1974/75 and 1985/86 (Valentine 1993) due to lack of national coverage or consistency in definition.

Brazil Data from 1960, 1970, 1974/75, 1976, 1977, 1978, 1980, 1982, 1983, 1985, 1987 and 1989 are available from the statistical yearbook, in addition to data for 1978 (Fields 1987) and for 1979 (Psacharopoulos et al. 1992). Other sources have been excluded as they were either not of national coverage, based on wage earners only, or because a more consistent source was available.

Bulgaria: Data from household surveys are available for 1963-69 (in two year intervals), for 1970-90 (on an annual basis) from the Statistical yearbook and for 1991 - 93 from household surveys by the World Bank (Milanovic and Ying).

Burkina Faso A priority survey has been undertaken in 1995.

Central African Republic: Except for a household survey conducted in 1992, no information was available.

Cameroon The only data are from a 1983/4 household budget survey (World Bank Poverty Assessment).

Canada Gini- and share data for the 1950-61 (in irregular intervals), 1961-81 (biennially), and 1981-91 (annually) are available from official sources (Statistical Yearbook for years before 1971 and Income Distributions by Size in Canada for years since 1973, various issues). All other references seem to be based on these primary sources.

Chad: An estimate for 1958 is available in the literature, and used by Alesina and Rodrik and Persson and Tabellini but was not included due to lack of primary sources.

Chile The first nation-wide survey that included not only employment income was carried out in 1968 (UN 1981). This is complemented by household survey-based data for 1971 (Fields 1989), 1989, and 1994. Other data that refer either only to part of the population or -as in the case of a long series available from World Bank country operations- are not clearly based on primary sources, are excluded.

China Annual household surveys from 1980 to 1992, conducted separately in rural and urban areas, were consolidated by Ying (1995), based on the statistical yearbook. Data from other secondary sources are excluded due to limited geographic and population coverage and data from Chen et al (1993) for 1985 and 1990 have not been included, to maintain consistency of sources..

Colombia The first household survey with national coverage was conducted in 1970 (DANE 1970). In addition, there are data for 1971, 1972, 1974 CEPAL (1986), and for 1978, 1988/89, and 1991 (World Bank Poverty Assessment 1992 and Chen et al. 1995). Data referring to years before 1970 -including the 1964 estimate used in Persson and Tabellini were excluded, as were estimates for the wage earning population only.

Costa Rica Data on Gini coefficients and quintile shares are available for 1961, 1971 (Cespedes 1973),1977 (OPNPE 1982), 1979 (Fields 1989), 1981 (Chen et al 1993), 1983 (Bourguignon and Morrison 1989), 1986 (Sauma-Fiatt 1990), and 1989 (Chen et al 1993). Gini coefficients for 1971 (Gonzalez-Vega and Cespedes in Rottenberg 1993), 1973 and 1985 (Bourguignon and Morrison 1989) cover urban areas only and were excluded.

Cote d'Ivoire: Data based on national-level household surveys (LSMS) are available for 1985, 1986, 1987, 1988, and 1995. Information for the 1970s (Schneider 1991) is based on national accounting information and therefore excluded

Cuba Official information on income distribution is limited. Data from secondary sources are available for 1953, 1962, 1973, and 1978, relying on personal wage income, i.e. excluding the population that is not economically active (Brundenius 1984).

Czech Republic Household surveys for 1993 and 1994 were obtained from Milanovic and Ying. While it is in principle possible to go back further, splitting national level surveys for the former Czechoslovakia into their independent parts, we decided not to do so as the same argument could be used to

This is a data record which corresponds to the paper "A consistent dataset for the net income distribution for 184 countries, aggregated to 32 geographical regions and the world from 1958-2015" (Narayan et al. 2023, in prep) https://essd.copernicus.org/preprints/essd-2023-137/

Description/Abstract- Data on the income distribution within and across countries are increasingly becoming important to inform analysis on income inequality and human welfare. While datasets on the income distribution collected from household surveys are available for multiple countries, these datasets often do not represent the same income concept and therefore make comparisons across countries and across datasets difficult. Here, we present a consistent dataset on the income distribution across 184 countries which all represent a single income concept namely net-income. We complement the observed values in this dataset with values of the income distribution imputed from summary measures such as the GINI coefficient to generate a consistent time series across countries from 1958 to 2015. For the imputation, we use a recently developed PCA based approach which shows an excellent fit to the latest data on income distributions. We also present another version of this dataset which is aggregated from the country level to 32 geographical regions and the world as a whole. Our aggregation method takes into account both within country and cross- country income inequality when aggregating to the regional level. This dataset will enable more robust analysis of the income distribution at multiple scales.

Historical dataset showing World income inequality - gini coefficient by year from N/A to N/A.

Brazil BR: Income Share Held by Highest 10% data was reported at 41.000 % in 2022. This records a decrease from the previous number of 41.600 % for 2021. Brazil BR: Income Share Held by Highest 10% data is updated yearly, averaging 44.550 % from Dec 1981 (Median) to 2022, with 38 observations. The data reached an all-time high of 51.100 % in 1989 and a record low of 39.500 % in 2020. Brazil BR: Income Share Held by Highest 10% data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Brazil – Table BR.World Bank.WDI: Social: Poverty and Inequality. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).