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.

According to an annual survey about Chinese women's workplace, the average monthly income of the female respondents in 2025 amounted to ***** yuan, about ** percent lower than the monthly salary of the male respondents. In the previous year, the average monthly income of female respondents was 8,958 yuan.

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.

Rising income inequality challenges economic and social stability in developing countries. For China, the fastest-growing global digital economy, it could be an effective tool to promote inclusive development, narrowing urban–rural income disparity. It investigates the role of digital financial inclusion (DFI) in narrowing the urban–rural income gap. The study uses panel data from 52 counties in Zhejiang Province, China, from 2014 to 2020. The results show that the development of DFI significantly reduces rural–urban and rural income inequality. The development of DFI helps optimize industrial structure and upgrade the internal structure of agriculture, facilitating income growth for people in rural areas. Such effects are greater in poorer counties. Our findings provide insights into why rapid DFI and the narrowing of the rural–urban income disparity exist in China. Moreover, our results provide clear policy implications on how to reduce the disparity. The most compelling suggestion is that promoting the optimization of industrial structure through DFI is crucial for narrowing the urban–rural income gap.

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 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 China town. 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 China town, the median income for all workers aged 15 years and older, regardless of work hours, was $54,295 for males and $31,032 for females.

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

- Full-time workers, aged 15 years and older: In China town, among full-time, year-round workers aged 15 years and older, males earned a median income of $58,056, while females earned $43,750, leading to a 25% gender pay gap among full-time workers. This illustrates that women earn 75 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 China town.

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 China town median household income by race. 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 have studied the impacts of industrial agglomeration on income inequality, and even fewer have studied the spatial correlation of income inequality. 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 non-linear changes. As the degree of industrial agglomeration increases, income inequality will rise; after it reaches a certain value, income inequality will drop. Therefore, the Chinese government and enterprises had better pay attention to the spatial distribution of industrial agglomeration, thereby reducing China's region..., These data originate from the 2004–2021 China Statistical Yearbook, China Labour Statistical Yearbook and China Population and Employment Statistics Yearbook. Using data on China's 31 provinces from 2003 to 2020, the spatial panel Durbin model is adopted to explore the impact of industrial agglomeration on income inequality in China.,

This paper evaluates income tax reforms in China and India. The combination of fast income growth and under-indexed tax schedule in China implies the fraction of the Chinese population subject to income tax has increased from less than 0.1 percent in 1986 to about 20 percent in 2008, while it has stagnated around 2-3 percent in India. Chinese income tax revenues, as a share of GDP, increased from less than 0.1 percent in 1986 to about 1.5 percent in 2005 and 2.5 percent in 2008, while the constant adaptation of exemption levels and income brackets in India have caused them to stagnate around 0.5 percent of GDP. (JEL D31, H24, 015, 023, P23, P35)

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 China Grove. 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 China Grove, the median income for all workers aged 15 years and older, regardless of work hours, was $69,583 for males and $44,851 for females.

These income figures highlight a substantial gender-based income gap in China Grove. Women, regardless of work hours, earn 64 cents for each dollar earned by men. This significant gender pay gap, approximately 36%, underscores concerning gender-based income inequality in the town of China Grove.

- Full-time workers, aged 15 years and older: In China Grove, among full-time, year-round workers aged 15 years and older, males earned a median income of $71,500, while females earned $53,571, leading to a 25% gender pay gap among full-time workers. This illustrates that women earn 75 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 China Grove.

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

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.

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

China Proportion of People Living Below 50 Percent Of Median Income: % data was reported at 11.600 % in 2021. This records a decrease from the previous number of 11.900 % for 2020. China Proportion of People Living Below 50 Percent Of Median Income: % data is updated yearly, averaging 15.100 % from Dec 1990 (Median) to 2021, with 19 observations. The data reached an all-time high of 19.500 % in 2010 and a record low of 8.900 % in 1990. China 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 China – Table CN.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).

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.

Income disparity, spatial inequality, and urban-rural inequality are three fundamental issues affecting human social equality. The Chinese government considers addressing these three inequalities as crucial for achieving common prosperity. Therefore, in response to these inequalities, the income Gini coefficient, the population-weighted CV of nighttime light, and the urban-rural income ratio were integrated to construct a social equality index (SEI). This index reflects the overall degree of social inequality in China.The specific calculation method can be found in the related paper.

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

DFI, RIS and urban–rural income disparity in different regions.

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 East China 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 East China township, the median income for all workers aged 15 years and older, regardless of work hours, was $53,875 for males and $30,115 for females.

These income figures highlight a substantial gender-based income gap in East China township. Women, regardless of work hours, earn 56 cents for each dollar earned by men. This significant gender pay gap, approximately 44%, underscores concerning gender-based income inequality in the township of East China township.

- Full-time workers, aged 15 years and older: In East China township, among full-time, year-round workers aged 15 years and older, males earned a median income of $75,250, while females earned $46,731, leading to a 38% gender pay gap among full-time workers. This illustrates that women earn 62 cents for each dollar earned by men in full-time roles. This level of income gap emphasizes the urgency to address and rectify this ongoing disparity, where women, despite working full-time, face a more significant wage discrepancy compared to men in the same employment roles.

Remarkably, across all roles, including non-full-time employment, women displayed a similar gender pay gap percentage. This indicates a consistent gender pay gap scenario across various employment types in East China township, showcasing a consistent income pattern irrespective of employment status.

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

China % of Household grouped by Annual Income: Urban:RMB30000-35000 data was reported at 6.140 % in 2011. This records a decrease from the previous number of 7.450 % for 2010. China % of Household grouped by Annual Income: Urban:RMB30000-35000 data is updated yearly, averaging 9.310 % from Dec 2005 (Median) to 2011, with 7 observations. The data reached an all-time high of 10.810 % in 2006 and a record low of 6.140 % in 2011. China % of Household grouped by Annual Income: Urban:RMB30000-35000 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.

BackgroundSince the reform and opening up, China’s urban and rural economic development has exhibited characteristics of imbalance, with the urban-rural income gap being the largest and most noticeable issue facing China’s socio-economic landscape. Alleviating and effectively resolving the urban-rural income disparity is crucial for achieving overall common prosperity. Therefore, this study provides insights for strategically narrowing the urban-rural income gap from the perspective of higher education investment.MethodsWe employ a panel fixed effects model to examine the basic regression, heterogeneity, mediating effects, and threshold effects. Simultaneously, we address the endogeneity issues in basic regression and mediating effects using the instrumental variable method. Additionally, we adopt the substitution of variables to ensure the robustness of the results.ResultsThis paper selects panel data from China’s eight major comprehensive economic zones from 2003 to 2021 for analysis. The findings reveal that, overall, higher education investment in China’s eight major comprehensive economic zones can narrow the urban-rural income gap. Specifically, higher education investment in 50% of these comprehensive economic zones—namely, the Northern Coastal Comprehensive Economic Zone, Eastern Coastal Comprehensive Economic Zone, Northeast Comprehensive Economic Zone, and Middle Yangtze River Comprehensive Economic Zone—can reduce the urban-rural income disparity. Conversely, higher education investment in the Middle Yellow River Comprehensive Economic Zone, Southern Coastal Comprehensive Economic Zone, Greater Southwest Comprehensive Economic Zone, and Greater Northwest Comprehensive Economic Zone has widened the urban-rural income gap. Additionally, higher education investment can affect the urban-rural income disparity through technological innovation. Overall, the impact of higher education investment on the urban-rural income gap in China’s eight major comprehensive economic zones is also influenced by the level of economic development, exhibiting an “inverted U-shaped” characteristic. This nonlinear impact varies across regions.ConclusionsIn conclusion, to narrow the urban-rural income gap across China’s eight major integrated economic zones, it is necessary to improve the mechanism for higher education investment in these zones. Strategies should be based on regional differences, tailored to local conditions, and implemented with a differentiated and precise approach to higher education development across regions. Emphasis should also be placed on the research and application of innovative technologies to achieve deep integration between urban and rural areas within China’s eight major integrated economic zones.

data used in the article "Convergence effect of the Belt and Road Initiative on income disparity: evidence from China" in Humanities and Social Sciences Communications