This statistic illustrates the distribution of adult population in China in 2022 by wealth range group. That year, approximately **** percent of adults in China had wealth of 100,000 to *********** U.S. dollars. In comparison, around **** percent of adult population in Hong Kong were in this wealth range group.

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.

China % of Household grouped by Annual Income: Urban:RMB80000-85000 data was reported at 3.330 % in 2011. This records an increase from the previous number of 3.010 % for 2010. China % of Household grouped by Annual Income: Urban:RMB80000-85000 data is updated yearly, averaging 2.030 % from Dec 2005 (Median) to 2011, with 7 observations. The data reached an all-time high of 3.330 % in 2011 and a record low of 0.780 % in 2005. China % of Household grouped by Annual Income: Urban:RMB80000-85000 data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Household Survey – Table CN.HD: Household Income Distribution: Urban.

In the first quarter of 2024, almost two-thirds percent of the total wealth in the United States was owned by the top 10 percent of earners. In comparison, the lowest 50 percent of earners only owned 2.5 percent of the total wealth. Income inequality in the U.S. Despite the idea that the United States is a country where hard work and pulling yourself up by your bootstraps will inevitably lead to success, this is often not the case. In 2023, 7.4 percent of U.S. households had an annual income under 15,000 U.S. dollars. With such a small percentage of people in the United States owning such a vast majority of the country’s wealth, the gap between the rich and poor in America remains stark. The top one percent The United States follows closely behind China as the country with the most billionaires in the world. Elon Musk alone held around 219 billion U.S. dollars in 2022. Over the past 50 years, the CEO-to-worker compensation ratio has exploded, causing the gap between rich and poor to grow, with some economists theorizing that this gap is the largest it has been since right before the Great Depression.

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.

About this dataset

Context

The dataset tabulates the median household income in China. It can be utilized to understand the trend in median household income and to analyze the income distribution in China 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).

• China, TX Median Household Income Trends (2010-2021, in 2022 inflation-adjusted dollars)
• Median Household Income Variation by Family Size in China, TX: Comparative analysis across 7 household sizes
• Income Distribution by Quintile: Mean Household Income in China, TX
• China, TX 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 China median household income. You can refer the same here

China % of Household grouped by Annual Income: Urban:>100000 data was reported at 18.080 % in 2011. This records an increase from the previous number of 12.220 % for 2010. China % of Household grouped by Annual Income: Urban:>100000 data is updated yearly, averaging 7.470 % from Dec 2005 (Median) to 2011, with 7 observations. The data reached an all-time high of 18.080 % in 2011 and a record low of 2.070 % in 2005. China % of Household grouped by Annual Income: Urban:>100000 data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Household Survey – Table CN.HD: Household Income Distribution: Urban.

In 2021, almost **** percent of the assets of all wealth management products with non-safeguarded principle in China were bonds, followed by interbank deposits which made up ** percent. As of 2018, the value of bonds accounted for about ***** percent of all wealth management products with non-safeguarded principal in China.

China % of Household grouped by Annual Income: Urban:RMB20000-25000 data was reported at 3.670 % in 2011. This records a decrease from the previous number of 5.130 % for 2010. China % of Household grouped by Annual Income: Urban:RMB20000-25000 data is updated yearly, averaging 8.490 % from Dec 2005 (Median) to 2011, with 7 observations. The data reached an all-time high of 14.090 % in 2005 and a record low of 3.670 % in 2011. China % of Household grouped by Annual Income: Urban:RMB20000-25000 data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Household Survey – Table CN.HD: Household Income Distribution: Urban.

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in China, Maine, 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 21,283, while the mean income for the highest quintile (20% of households with the highest income) is 236,557. This indicates that the top earners earn 11 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 401,757, which is 169.84% higher compared to the highest quintile, and 1887.69% 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 China town median household income. You can refer the same here

About this dataset

Context

The dataset tabulates the China median household income by race. The dataset can be utilized to understand the racial distribution of China income.

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).

• China, TX median household income breakdown by race betwen 2011 and 2021
• Median Household Income by Racial Categories in China, TX (2021, 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 China median household income by race. You can refer the same here

Projections of future income distributions at subnational levels are becoming increasingly important for a variety of analyses and evaluations. However, relevant datasets are currently limited. This study presents a methodological framework that introduces machine learning algorithms to a top-down approach used for generating income distribution datasets. We project per capita disposable income and income inequality for 31 Chinese provinces from 2020 to 2100, considering different scenarios based on China’s local circumstances, and then estimate income distributions based on these. After accounting for necessary consistency between provincial, urban, and rural income datasets, we further generate the same data products at the urban and rural level for each province. We validate our projection results drawing on data from 2007-2023 for China’s disposable income, data from 2007 to 2019 for provincial income inequality in China, as well as national income inequality data for the past 20 to 60 years from select developed countries. The proposed methodology provides flexibility to generate similar data products according to a user’s specific needs. Our resulting datasets have several potential applications and can serve as inputs for research on drivers and impacts across social, economic, and environmental domains.

About this dataset

Context

The dataset tabulates the China household income by age. The dataset can be utilized to understand the age-based income distribution of China income.

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).

• China, TX annual median income by age groups dataset (in 2022 inflation-adjusted dollars)
• Age-wise distribution of China, TX household incomes: Comparative analysis across 16 income brackets

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 China income distribution by age. You can refer the same here

As of January 2022, the largest share of Chinese middle-class families had an annual income of between *** thousand and *** thousand yuan per year. According to the same survey, almost ** percent of respondents have at least one child. Many middle-class families in China face significant financial burdens because not only do living costs continuously increase but they also often have to support their parents. In that case, one family has to care for four elders and least one kid.

We combine national accounts, surveys, and new tax data to study the accumulation and distribution of income and wealth in China from 1978 to 2015. The national wealth-income ratio increased from 350 percent in 1978 to 700 percent in 2015, while the share of public property in national wealth declined from 70 percent to 30 percent. We provide sharp upward revision of official inequality estimates. The top 10 percent income share rose from 27 percent to 41 percent between 1978 and 2015; the bottom 50 percent share dropped from 27 percent to 15 percent. China's inequality levels used to be close to Nordic countries and are now approaching US levels.

The purpose of this project was to measure and estimate the distribution of income in both rural and urban areas of the People's Republic of China. The principal investigators based their definition of income on cash payments and on a broad range of additional components: payments in kind valued at market prices, agricultural output produced for self-consumption valued at market prices, the value of ration coupons and other direct subsidies, and the imputed value of housing. The rural component of this collection consists of two data files, one in which the individual is the unit of analysis and a second in which the household is the unit of analysis. Individual rural respondents reported on their employment status, level of education, Communist Party membership, type of employer (e.g., public, private, or foreign), type of economic sector in which employed, occupation, whether they held a second job, retirement status, monthly pension, monthly wage, and other sources of income. Demographic variables include relationship to householder, gender, age, and student status. Rural households reported extensively on the character of the household and residence. Information was elicited on type of terrain surrounding the house, geographic position, type of house, and availability of electricity. Also reported were sources of household income (e.g., farming, industry, government, rents, and interest), taxes paid, value of farm, total amount and type of cultivated land, financial assets and debts, quantity and value of various crops (e.g., grains, cotton, flax, sugar, tobacco, fruits and vegetables, tea, seeds, nuts, lumber, livestock and poultry, eggs, fish and shrimp, wool, honey, and silkworm cocoons), amount of grain purchased or provided by a collective, use of chemical fertilizers, gasoline, and oil, quantity and value of agricultural machinery, and all household expenditures (e.g., food, fuel, medicine, education, transportation, and electricity). The urban component of this collection also consists of two data files, one in which the individual is the unit of analysis and a second in which the household is the unit of analysis. Individual urban respondents reported on their economic status within the household, Communist Party membership, sex, age, nature of employment, and relationship to the household head. Information was collected on all types and sources of income from each member of the household whether working, nonworking, or retired, all revenue received by owners of private or individual enterprises, and all in-kind payments (e.g., food and durable and non-durable goods). Urban households reported total income (including salaries, interest on savings and bonds, dividends, rent, leases, alimony, gifts, and boarding fees), all types and values of food rations received, and total debt. Information was also gathered on household accommodations and living conditions, including number of rooms, total living area in square meters, availability and cost of running water, sanitary facilities, heating and air-conditioning equipment, kitchen availability, location of residence, ownership of home, and availability of electricity and telephone. Households reported on all of their expenditures including amounts spent on food items such as wheat, rice, edible oils, pork, beef and mutton, poultry, fish and seafood, sugar, and vegetables by means of both coupons in state-owned stores and at free market prices. Information was also collected on rents paid by the households, fuel available, type of transportation used, and availability and use of medical and child care. The Chinese Household Income Project collected data in 1988, 1995, 2002, and 2007. ICPSR holds data from the first three collections, and information about these can be found on the series description page. Data collected in 2007 are available through the China Institute for Income Distribution.

This statistic shows the distribution of China's internet wealth management users in 2018, broken down by education level. According to a survey, about ** percent of the surveyed internet wealth management users in China earned at least a bachelor degree.

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within China town. The dataset can be utilized to gain insights into gender-based income distribution within the China town population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within China town, among individuals aged 15 years and older with income, there were 1,732 men and 1,570 women in the workforce. Among them, 1,152 men were engaged in full-time, year-round employment, while 727 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 0.78% fell within the income range of under $24,999, while 9.77% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 14.06% of men in full-time roles earned incomes exceeding $100,000, while 7.29% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

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

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

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.

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

This paper combines national accounts, survey, wealth, and fiscal data (including recently released tax data on high-income taxpayers) in order to provide consistent series on the accumulation and distribution of income and wealth in China and Russia over the 1978–2015 period. We contrast the different privatization strategies implemented in the two countries and observe their impacts on the evolution of inequality.

Since the 1990s, global income and wealth inequality has increased significantly, especially in developing countries, where the imbalance in wealth distribution has become increasingly prominent. This study seeks to thoroughly investigate the effects of expansionary monetary policy on income and wealth inequality, using China as a case study and employing extensive household survey microdata for empirical analysis. The findings indicate that expansionary monetary policy has significantly enhanced overall income and wealth levels. However, when considering the extent of wealth growth, it appears that affluent households have benefited more than their low- and middle-income counterparts, thereby widening the wealth gap. In addition, the real estate market boom played an amplifying role in this process, further deepening the impact of monetary policy on wealth inequality. The findings of this paper provide an important empirical basis for understanding the complex relationship between monetary policy and socio-economic inequality, and provide practical references for policymakers to consider the fairness of income and wealth distribution when formulating relevant monetary policies.