In the U.S., median household income rose from 51,570 U.S. dollars in 1967 to 80,610 dollars in 2023. In terms of broad ethnic groups, Black Americans have consistently had the lowest median income in the given years, while Asian Americans have the highest; median income in Asian American households has typically been around double that of Black Americans.

The average for 2021 based on 5 countries was 36.92 index points. The highest value was in Malaysia: 40.7 index points and the lowest value was in India: 32.8 index points. The indicator is available from 1963 to 2023. Below is a chart for all countries where data are available.

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 46.5 (0.465) 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 0.32 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 0.47 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.

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 Oriental. 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 Oriental, the median income for all workers aged 15 years and older, regardless of work hours, was $61,818 for males and $26,675 for females.

These income figures highlight a substantial gender-based income gap in Oriental. Women, regardless of work hours, earn 43 cents for each dollar earned by men. This significant gender pay gap, approximately 57%, underscores concerning gender-based income inequality in the town of Oriental.

- Full-time workers, aged 15 years and older: In Oriental, among full-time, year-round workers aged 15 years and older, males earned a median income of $74,250, while females earned $56,250, leading to a 24% gender pay gap among full-time workers. This illustrates that women earn 76 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 Oriental.

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

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Oriental, NC, 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 12,196, while the mean income for the highest quintile (20% of households with the highest income) is 174,033. This indicates that the top earners earn 14 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 238,820, which is 137.23% higher compared to the highest quintile, and 1958.18% 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 Oriental median household income. You can refer the same here

An average of 79% of Bangladeshi households were in the 2 lowest income quintiles (after housing costs were deducted) between April 2019 and March 2022

This paper aimed to ascertain if there is a statistically significant difference in linearity or directional effect of urbanisation on income inequality in Asia and Sub-Saharan Africa. To explore the research question it uses panel estimations to introduce into the literature a bi-regional analysis of the relationship between urbanisation and income inequality over the period 1990-2020. This was conducted using panel estimations and dynamic panel Generalized Method of Moments techniques. The findings show that Asia and Sub-Saharan Africa differ in terms of the directional effect of urbanisation on income inequality, as well as linearity of the relationship. First estimates for Asia indicate a statistically significant inverse-U shaped relationship between urbanisation and income inequality, with an implied turning point at 23% urbanisation. Whereas estimates for Sub-Saharan Africa indicate a statistically significant negative linear relationship between urbanisation and income inequality, with a larger statistically significant negative linear effect in the long run. Future urbanisation in both regions should reduce income inequality on aggregate, ceteris paribus, as 46/48 Asia nations have passed the implied turning point as of 2020.

This paper aimed to ascertain if there is a statistically significant difference in linearity or directional effect of urbanisation on income inequality in Asia and Sub-Saharan Africa. To explore the research question it uses panel estimations to introduce into the literature a bi-regional analysis of the relationship between urbanisation and income inequality over the period 1990-2020. This was conducted using panel estimations and dynamic panel Generalized Method of Moments techniques. The findings show that Asia and Sub-Saharan Africa differ in terms of the directional effect of urbanisation on income inequality, as well as linearity of the relationship. First estimates for Asia indicate a statistically significant inverse-U shaped relationship between urbanisation and income inequality, with an implied turning point at 23% urbanisation. Whereas estimates for Sub-Saharan Africa indicate a statistically significant negative linear relationship between urbanisation and income inequality, with a larger statistically significant negative linear effect in the long run. Future urbanisation in both regions should reduce income inequality on aggregate, ceteris paribus, as 46/48 Asia nations have passed the implied turning point as of 2020.

In 2024, the average annual per capita disposable income of households in China amounted to approximately 41,300 yuan. Annual per capita income in Chinese saw a significant rise over the last decades and is still rising at a high pace. During the last ten years, per capita disposable income roughly doubled in China. Income distribution in China As an emerging economy, China faces a large number of development challenges, one of the most pressing issues being income inequality. The income gap between rural and urban areas has been stirring social unrest in China and poses a serious threat to the dogma of a “harmonious society” proclaimed by the communist party. In contrast to the disposable income of urban households, which reached around 54,200 yuan in 2024, that of rural households only amounted to around 23,100 yuan. Coinciding with the urban-rural income gap, income disparities between coastal and western regions in China have become apparent. As of 2023, households in Shanghai and Beijing displayed the highest average annual income of around 84,800 and 81,900 yuan respectively, followed by Zhejiang province with 63,800 yuan. Gansu, a province located in the West of China, had the lowest average annual per capita household income in China with merely 25,000 yuan. Income inequality in China The Gini coefficient is the most commonly used measure of income inequality. For China, the official Gini coefficient also indicates the astonishing inequality of income distribution in the country. Although the Gini coefficient has dropped from its high in 2008 at 49.1 points, it still ranged at a score of 46.5 points in 2023. The United Nations have set an index value of 40 as a warning level for serious inequality in a society.

Between 2018 and 2022, people in households in the ‘other’, Asian and black ethnic groups were the most likely to be in persistent low income, both before and after housing costs, out of all ethnic groups.

A cross-national data archive located in Luxembourg that contains two primary databases: the Luxembourg Income Study Database (LIS Database) includes income microdata from a large number of countries at multiple points in time. The newer Luxembourg Wealth Study Database(LWS Database) includes wealth microdata from a smaller selection of countries. Both databases include labor market and demographic data as well. Our mission is to enable, facilitate, promote, and conduct cross-national comparative research on socio-economic outcomes and on the institutional factors that shape those outcomes. Since its beginning in 1983, the LIS has grown into a cooperative research project with a membership that includes countries in Europe, North America, and Australia. The database now contains information for more than 30 countries with datasets that span up to three decades. The LIS databank has a total of over 140 datasets covering the period 1968 to 2005. The primary objectives of the LIS are as follows: * Test the feasibility for creating a database containing social and economic data collected in household surveys from different countries; * Provide a method which allows researchers to use the data under restrictions required by the countries providing the data; * Create a system that allows research requests to be received from and returned to users at remote locations; and * Promote comparative research on the social and economic status of various populations and subgroups in different countries. Data Availability: The dataset is accessed globally via electronic mail networks. Extensive documentation concerning technical aspects of the survey data, variables list, and the social institutions of income provision in member countries are also available to users through the project Website. * Dates of Study: 1968-present * Study Features: International * Sample Size: 30+ Countries Link: * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/00150

This file folder contains codes for data construction and analysis. The ReadMe file describes how to replicate the tables and figures from the paper.

In 2023, the Gini index for households of Asian origin in the United States stood at 0.48. The Census Bureau defines the Gini index as “a statistical measure of income inequality ranging from zero to one. A measure of one indicates perfect inequality, i.e., one household having all the income and rest having none. A measure of zero indicates perfect equality, i.e., all households having an equal share of income.”

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Oriental, NC, 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 12,252, while the mean income for the highest quintile (20% of households with the highest income) is 178,881. This indicates that the top earners earn 15 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 246,892, which is 138.02% higher compared to the highest quintile, and 2015.12% higher compared to the lowest quintile.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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 2022 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 Oriental median household income. You can refer the same here

Income inequality is a good indicator reflecting the quality of people’s livelihood. There are many studies on the determinants of income inequality. However, few studies have been conducted on the impacts of industrial agglomeration on income inequality and their spatial correlation. The goal of this paper is to investigate the impact of China’s industrial agglomeration on income inequality from a spatial perspective. Using data on China’s 31 provinces from 2003 to 2020 and the spatial panel Durbin model, our results show that industrial agglomeration and income inequality present an inverted “U-shape” relationship, proving that they are the non-linear change. As the degree of industrial agglomeration increases, income inequality will rise, after it reaches a certain value, income inequality will drop. Therefore, Chinese government and enterprises had better pay attention to the spatial distribution of industrial agglomeration, thereby reducing China’s regional income inequality.

Income shares, Gini coefficient, and IFC for different income groups %.

In 2023, just over 50 percent of Americans had an annual household income that was less than 75,000 U.S. dollars. The median household income was 80,610 U.S. dollars in 2023. Income and wealth in the United States After the economic recession in 2009, income inequality in the U.S. is more prominent across many metropolitan areas. The Northeast region is regarded as one of the wealthiest in the country. Maryland, New Jersey, and Massachusetts were among the states with the highest median household income in 2020. In terms of income by race and ethnicity, the average income of Asian households was 94,903 U.S. dollars in 2020, while the median income for Black households was around half of that figure. What is the U.S. poverty threshold? The U.S. Census Bureau annually updates its list of poverty levels. Preliminary estimates show that the average poverty threshold for a family of four people was 26,500 U.S. dollars in 2021, which is around 100 U.S. dollars less than the previous year. There were an estimated 37.9 million people in poverty across the United States in 2021, which was around 11.6 percent of the population. Approximately 19.5 percent of those in poverty were Black, while 8.2 percent were white.

Results of measuring fiscal redistribution effects on income inequality.

In 2024, the average annual per capita disposable income of rural households in China was approximately 23,119 yuan, roughly 43 percent of the income of urban households. Although living standards in China’s rural areas have improved significantly over the past 20 years, the income gap between rural and urban households is still large. Income increase of China’s households From 2000 to 2020, disposable income per capita in China increased by around 700 percent. The fast-growing economy has inevitably led to the rapid income increase. Furthermore, inflation has been maintained at a lower rate in recent years compared to other countries. While the number of millionaires in China has increased, many of its population are still living in humble conditions. Consequently, the significant wealth gap between China’s rich and poor has become a social problem across the country. However, in recent years rural areas have been catching up and disposable income has been growing faster than in the cities. This development is also reflected in the Gini coefficient for China, which has decreased since 2008. Urbanization in China The urban population in China surpassed its rural population for the first time in 2011. In fact, the share of the population residing in urban areas is continuing to increase. This is not surprising considering remote, rural areas are among the poorest areas in China. Currently, poverty alleviation has been prioritized by the Chinese government. The measures that the government has taken are related to relocation and job placement. With the transformation and expansion of cities to accommodate the influx of city dwellers, neighboring rural areas are required for the development of infrastructure. Accordingly, land acquisition by the government has resulted in monetary gain by some rural households.

In 2022, ethnic Chinese households had the highest mean monthly household income in Malaysia, at around 10.66 thousand Malaysian ringgit. This was more than three thousand ringgit higher than Bumiputera households. Despite the implementation of affirmative action through Article 153 of the Malaysian constitution, the economic position of the Bumiputera vis-à-vis other ethnicities still left much room for improvement.

Historical policies, ethnicity, and the urban-rural divide The Bumiputera make up the majority of the Malaysian population, yet have one of the lowest average monthly household incomes in Malaysia. This economic disparity could be explained by the effects of colonial policies that kept the Bumiputera largely in the countryside. This resulted in an urban-rural divide that was characterized by ethnicity, with the immigrant Chinese and Indian laborers concentrated in the urban centers, a demographic pattern that is still evident today.

There was a considerable difference in urban and rural household incomes in Malaysia, with urban household income being around 3.6 thousand ringgit more than rural households. This was largely due to the fact that wages in urban areas had to keep up with the higher cost of living there. This thus impacted the average monthly incomes of the largely rural-based Bumiputera and the largely urban-based ethnic Chinese. This visible wealth inequality has led to racial tensions in Malaysia, and it is still one of the problem in the country amidst a new government led by Prime Minister Anwar Ibrahim, who was elected in 2022.

Abstract

The startling drop in incomes and increase in inequality accompanying the transition to market economies in Eastern Europe and the former Soviet Union raise critical questions: Who is most likely to be poor? How well are existing social assistance programs reaching those who most need help? And what kind of programs would be most effective in reducing poverty? As part of a project analyzing poverty and social assistance in the transition economies, a Bank research team created a database of household expenditure and income data from recent surveys - the HEIDE database. (See the book by J. Braithwaite, Ch. Grootaert and B. Milanovic, "Poverty and social assistance in Transition Countries, St. Martin's Press, 1999" and the book by B. Milanovic, Income, inequality, and poverty during the transition from planned to market economy, World Bank, 1998.)

The HEIDE database includes four countries in both Eastern Europe and the Former Soviet Union. Latvia was then added at a later stage.

The four files are: -hhold: Household data consists of the variables in Variable List at household level. -ind: Individual data consists of the variables in Variable List at individual level. -modelh: Household data consists of the variables used in regression models. -modeli: Individual data consists of the variables used in regression models.

Prefixes are used to indicate countries for the data files, i.e. A- Rural Armenia B- Bulgaria E- Estonia H- Hungary K- Kyrgyz P- Poland R- Russia S- Slovak Y- Urban Armenia

The survey data were cleaned for possible inconsistencies and errors and adjusted for missing data and outliers. The compilation of almost 100 variables with similar definitions for the eight countries allows ready cross-country analysis and comparisons. A consistent syntax is used for the variables to enable researchers to use the same macro routines across countries. There are more than 3 million data points.

Geographic coverage

The database includes data from Armenia, Bulgaria, Estonia, Hungary, Kyrgyz Republic, Latvia, Poland, Russia, and the Slovak Republic.

Kind of data

Sample survey data [ssd]

Mode of data collection

Face-to-face [f2f]

Cleaning operations

The survey data were cleaned for possible inconsistencies and errors and adjusted for missing data and outliers. The compilation of almost 100 variables with similar definitions for the eight countries allows ready cross-country analysis and comparisons.

See document "Household Expenditure and Income Data for Transitional Economies (HEIDE): Data Cleaning and Rent Imputation - Appendix 1 of RAD project "Poverty and Targeting of Social Assistance in Eastern Europe and the Former Soviet Union"".