Wages in China increased to 120698 CNY/Year in 2023 from 114029 CNY/Year in 2022. This dataset provides - China Average Yearly Wages - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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. 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, the median income for all workers aged 15 years and older, regardless of work hours, was $58,750 for males and $30,313 for females.

These income figures highlight a substantial gender-based income gap in China. Women, regardless of work hours, earn 52 cents for each dollar earned by men. This significant gender pay gap, approximately 48%, underscores concerning gender-based income inequality in the city of China.

- Full-time workers, aged 15 years and older: In China, among full-time, year-round workers aged 15 years and older, males earned a median income of $62,188, while females earned $69,375

Surprisingly, within the subset of full-time workers, women earn a higher income than men, earning 1.12 dollars for every dollar earned by men. This suggests that within full-time roles, womens median incomes significantly surpass mens, contrary to broader workforce trends.

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

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

This dataset provides values for WAGES IN MANUFACTURING reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for china in the U.S.

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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 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 China township, the median income for all workers aged 15 years and older, regardless of work hours, was $59,101 for males and $27,440 for females.

These income figures highlight a substantial gender-based income gap in China township. Women, regardless of work hours, earn 46 cents for each dollar earned by men. This significant gender pay gap, approximately 54%, underscores concerning gender-based income inequality in the township of China township.

- Full-time workers, aged 15 years and older: In China township, among full-time, year-round workers aged 15 years and older, males earned a median income of $82,611, while females earned $46,667, leading to a 44% gender pay gap among full-time workers. This illustrates that women earn 56 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 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 China township median household income by race. You can refer the same here

Time series data for the data Current Account and Its Components - Current USD, TTM for the country China. The Current Account and Its Components The current account is a component of a country's balance of payments that records the transactions of goods, services, income, and current transfers between residents of the country and the rest of the world. It consists of four main components:

a. Trade in Goods Balance

b. Trade in Services Balance

c. Primary Income Balance

d. Secondary Income Balance

1. Trade in Goods Balance Definition: This includes the export and import of physical items such as machinery, food, clothing, etc.

Credit Example: A German car manufacturer exports cars to the United States (value of exported cars).

Debit Example: A German electronics retailer imports smartphones from South Korea (value of imported smartphones).

1. Trade in Services Balance Definition: This includes the export and import of services such as tourism, financial services, consulting, transportation, etc.

Credit Example: A German IT company provides software development services to a client in Japan (value of exported services).

Debit Example: A German tourist books a hotel room in France (value of imported tourism services).

1. Primary Income Balance Definition: This includes earnings from the provision of factors of production such as labor, financial assets, land, and natural resources. It covers income from interest, profits, and dividends.

Credit Example: A German investor receives dividends from shares held in a U.S. company (value of received dividends).

Debit Example: Foreign investors receive interest payments on bonds issued by a German company (value of interest payments).

1. Secondary Income Balance Definition: This includes current transfers such as foreign aid, remittances, and other one-way payments that do not involve an exchange of goods or services.

Credit Example: Remittances sent by German residents working abroad to their families in Germany (value of received remittances).

Debit Example: Germany sends humanitarian aid to a developing country (value of sent aid). Secondary Income Balance (USD)The indicator "Secondary Income Balance (USD)" stands at 14.19 Billion United States Dollars as of 3/31/2025. Regarding the One-Year-Change of the series, the current value constitutes an increase of 0.6144 Billion United States Dollars compared to the value the year prior.The 1 year change is 0.6144 Billion United States Dollars.The 3 year change is -2.95 Billion United States Dollars.The 5 year change is 5.75 Billion United States Dollars.The 10 year change is 17.47 Billion United States Dollars.The Serie's long term average value is 13.68 Billion United States Dollars. It's latest available value, on 3/31/2025, is 0.52 Billion United States Dollars higher, compared to it's long term average value.The Serie's change in United States Dollars from it's minimum value, on 9/30/2017, to it's latest available value, on 3/31/2025, is +27.83 Billion.The Serie's change in United States Dollars from it's maximum value, on 9/30/2008, to it's latest available value, on 3/31/2025, is -29.66 Billion.Trade in Services Balance (USD)The indicator "Trade in Services Balance (USD)" stands at -227.06 Billion United States Dollars as of 3/31/2025. Regarding the One-Year-Change of the series, the current value constitutes an decrease of -0.3885 Billion United States Dollars compared to the value the year prior.The 1 year change is -0.3885 Billion United States Dollars.The 3 year change is -141.70 Billion United States Dollars.The 5 year change is 23.05 Billion United States Dollars.The 10 year change is -17.70 Billion United States Dollars.The Serie's long term average value is -103.37 Billion United States Dollars. It's latest available value, on 3/31/2025, is -123.69 Billion United States Dollars lower, compared to it's long term average value.The Serie's change in United States Dollars from it's minimum value, on 12/31/2018, to it's latest available value, on 3/31/2025, is +65.52 Billion.The Serie's change in United States Dollars from it's maximum value, on 9/30/2007, to it's latest available value, on 3/31/2025, is -234.92 Billion.Trade in Goods Balance (USD)The indicator "Trade in Goods Balance (USD)" stands at 885.97 Billion United States Dollars as of 3/31/2025, the highest value at least since 6/30/2001, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes an increase of 288.50 Billion United States Dollars compared to the value the year prior.The 1 year change is 288.50 Billion United States Dollars.The 3 year change is 301.62 Billion United States Dollars.The 5 year change is 553.89 Billion United States Dollars.The 10 year change is 376.42 Billion United States Dollars.The Serie's long term average value is 339.77 Billion United States Dollars. It's latest available value, on ...

The Gross Domestic Product per capita in China was last recorded at 13121.68 US dollars in 2024. The GDP per Capita in China is equivalent to 104 percent of the world's average. This dataset provides - China GDP per capita - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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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

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for asian studies focusing on teaching chinese and chinese culture in the U.S.

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. The dataset can be utilized to gain insights into gender-based income distribution within the China population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within China, among individuals aged 15 years and older with income, there were 220 men and 398 women in the workforce. Among them, 113 men were engaged in full-time, year-round employment, while 137 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 10.62% fell within the income range of under $24,999, while 1.46% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 22.12% of men in full-time roles earned incomes exceeding $100,000, while 2.19% 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 median household income by race. You can refer the same here

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