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.

In the 2023/24 financial year, various measures of inequality in the United Kingdom are higher than in the late 1970s. The S80/20 ratio increased from ****to ***, the P90/10 ratio from ****to ***, and the Palma ratio from *** to ***.

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

This statistic shows the inequality of income distribution in China from 2005 to 2023 based on the Gini Index. In 2023, China reached a score of ************ points. The Gini Index is a statistical measure that is used to represent unequal distributions, e.g. income distribution. It can take any value between 1 and 100 points (or 0 and 1). The closer the value is to 100 the greater is the inequality. 40 or 0.4 is the warning level set by the United Nations. The Gini Index for South Korea had ranged at about **** in 2022. Income distribution in China The Gini coefficient is used to measure the income inequality of a country. The United States, the World Bank, the US Central Intelligence Agency, and the Organization for Economic Co-operation and Development all provide their own measurement of the Gini coefficient, varying in data collection and survey methods. According to the United Nations Development Programme, countries with the largest income inequality based on the Gini index are mainly located in Africa and Latin America, with South Africa displaying the world's highest value in 2022. The world's most equal countries, on the contrary, are situated mostly in Europe. The United States' Gini for household income has increased by around ten percent since 1990, to **** in 2023. Development of inequality in China Growing inequality counts as one of the biggest social, economic, and political challenges to many countries, especially emerging markets. Over the last 20 years, China has become one of the world's largest economies. As parts of the society have become more and more affluent, the country's Gini coefficient has also grown sharply over the last decades. As shown by the graph at hand, China's Gini coefficient ranged at a level higher than the warning line for increasing risk of social unrest over the last decade. However, the situation has slightly improved since 2008, when the Gini coefficient had reached the highest value of recent times.

Educational resources and lesson plans based on Income Inequality (Gini Coefficients) for Australian regions data collection Lineage: Fleming, David; Measham, Tom (2015): Income Inequality (Gini Coefficients) for Australian regions. v1. CSIRO. Data Collection. https://doi.org/10.4225/08/55093772960E4

This Commentary investigates whether there has been a growing divergence in the consumption of luxury and necessity goods across income classes. The analysis shows that while necessities represent a majority of the consumption basket for lower and middle income quintiles, their consumption of necessities in inflation-adjusted dollars has been declining in the face of higher prices of such goods and stagnant income growth. Higher income quintiles have seen increases in their consumption of luxuries, simultaneous with a decline in their consumption of necessities.

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']

Statistics on income inequality based on the Gini index and the p90/p10 ratio on various household income concepts (market income, total income, after-tax income) for Canada, provinces and territories, census metropolitan areas and census agglomerations.

Census data are frequently used throughout Vital Signs as denominators for normalizing many other indicators and rates. The socioeconomic and demographic indicators are grouped into the following categories: population, race/ethnicity, age, households, and income and poverty.

Income Inequality in Denver County, CO was 17.97779 Ratio in January of 2023, according to the United States Federal Reserve. Historically, Income Inequality in Denver County, CO reached a record high of 20.23338 in January of 2010 and a record low of 17.13318 in January of 2021. Trading Economics provides the current actual value, an historical data chart and related indicators for Income Inequality in Denver County, CO - last updated from the United States Federal Reserve on December of 2025.

Graph and download economic data for Income Inequality in Hood River County, OR (2020RATIO041027) from 2010 to 2023 about Hood River County, OR; inequality; OR; income; and USA.

Slovakia - Inequality of income distribution was 3.28 in December of 2024, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Slovakia - Inequality of income distribution - last updated from the EUROSTAT on December of 2025. Historically, Slovakia - Inequality of income distribution reached a record high of 3.93 in December of 2014 and a record low of 3.03 in December of 2020.

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

Concerns about rising income inequality are based on comparing income distributions over time. It is important to remember that such distributions are snapshots of a single year, and that the same households do not necessarily appear year after year in the same quintile of the distribution. Paying attention to mobility, as well as inequality, gives us a richer picture of the income possibilities for households over time. We document changes in a measure of income mobility over the past 40 years, a period in which income inequality has increased. We find a modest level of movement through the distribution, particularly across generations. Nevertheless, the income quintile of one’s parents still has a sizeable effect on how just how high one is likely to rise or how low one may fall.

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Wood township. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Wood township, the median income for all workers aged 15 years and older, regardless of work hours, was $46,833 for males and $21,827 for females.

These income figures highlight a substantial gender-based income gap in Wood township. Women, regardless of work hours, earn 47 cents for each dollar earned by men. This significant gender pay gap, approximately 53%, underscores concerning gender-based income inequality in the township of Wood township.

- Full-time workers, aged 15 years and older: In Wood township, among full-time, year-round workers aged 15 years and older, males earned a median income of $54,432, while females earned $45,750, leading to a 16% gender pay gap among full-time workers. This illustrates that women earn 84 cents for each dollar earned by men in full-time roles. This analysis indicates a widening gender pay gap, showing a substantial income disparity where women, despite working full-time, face a more significant wage discrepancy compared to men in the same roles.

Surprisingly, the gender pay gap percentage was higher across all roles, including non-full-time employment, for women compared to men. This suggests that full-time employment offers a more equitable income scenario for women compared to other employment patterns in Wood township.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

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 Wood township median household income by race. You can refer the same here

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Between 2010 and 2022, Panama's data on the degree of inequality in income distribution based on the Gini coefficient totaled 50.9. This coefficient represents a deterioration compared to last year. Panama was deemed as the third most unequal country in Latin America.

The Gini coefficient measures the deviation of the distribution of income (or consumption) among individuals or households in a given country from a perfectly equal distribution. A value of 0 represents absolute equality, whereas 100 would be the highest possible degree of inequality.

In 2024, the Gini coefficient for income in India stood at ****. The Gini coefficient, or the Gini index, measures the inequality of income distribution, whereas a higher value closer to one (or 100 percent) represent greater inequality.

Included here are: PDF of the paper PDF of the codebook, mostly from the original paper, with a couple of additions PDF of the original paper data set from the original paper 2 additional data sets