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.

Graph and download economic data for Median Household Income in California (MEHOINUSCAA646N) from 1984 to 2024 about CA, households, median, income, and USA.

Graph and download economic data for Real Median Personal Income in the United States (MEPAINUSA672N) from 1974 to 2024 about personal income, personal, median, income, real, and USA.

Wages in Sweden increased to 208.30 SEK/Hour in June from 207.60 SEK/Hour in May of 2025. This dataset provides - Sweden Average Hourly Wages - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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

Key observations

• Employment patterns: Within Exeter, among individuals aged 15 years and older with income, there were 1,980 men and 2,202 women in the workforce. Among them, 859 men were engaged in full-time, year-round employment, while 773 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 6.17% fell within the income range of under $24,999, while 8.41% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 18.98% of men in full-time roles earned incomes exceeding $100,000, while 5.05% 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 Exeter median household income by race. You can refer the same here

Average hourly and weekly wage rate, and median hourly and weekly wage rate by North American Industry Classification System (NAICS), type of work, gender, and age group.

Income of individuals by age group, sex and income source, Canada, provinces and selected census metropolitan areas, annual.

Wages in Taiwan increased to 65583 TWD/Month in July from 56575 TWD/Month in June of 2025. This dataset provides - Taiwan Wages- actual values, historical data, forecast, chart, statistics, economic calendar and news.

As of 2023, the average annual wage of Germany was 48,301 euros per year, a growth of almost 6,000 Euros when compared with 2000. From 2000 until 2007, wages rose by less than a thousand euros, with wage growth accelerating mainly in the period after 2010. Comparisons with rest of the EU Within the European Union Luxembourg had an average annual salary of almost 80 thousand Euros, with Germany having an annual salary comparable to other large European Countries, such as the United Kingdom and France. In neighboring Poland, the average annual salary was just over 39 thousand U.S dollars, meaning that German’s earned, on average, 20 percent more than what their Polish counterparts did. German economy slowing in 2023 While Germany initially had one of the strongest recoveries from the 2008 financial crash and as of 2020 had the largest economy in Europe its economy has started to slow in recent years. For 2023 the German economy is contracted by 0.26 percent, and while 2024 marked a slight improvement, the expectations are that 2025 remains a year of slow growth.

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

Key observations

• Employment patterns: Within Camden County, among individuals aged 15 years and older with income, there were 3,895 men and 3,843 women in the workforce. Among them, 2,086 men were engaged in full-time, year-round employment, while 1,980 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 3.88% fell within the income range of under $24,999, while 7.37% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 12.75% of men in full-time roles earned incomes exceeding $100,000, while 16.16% 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 Camden County median household income by race. You can refer the same here

This collection provides average scores on selected variables from the Dictionary of Occupational Titles for 1980 Census occupational categories. The dataset was prepared using a sample of individuals from the 1970 Census whose occupations had been coded with both 1970 and 1980 Census detailed occupational codes. This file, known as the Treiman file, was merged with selected variables from the FOURTH EDITION DICTIONARY OF OCCUPATIONAL TITLES, using 1970 occupational categories as the merge variable. A score on each Dictionary of Occupational Titles (DOT) variable was then computed for each 1980 occupational category. Included among the DOT variables were such characteristics as (1) level of complexity at which the worker functions in terms of data, people, and things, (2) training required to do the job, (3) verbal, numerical, spatial, and other aptitudes needed to perform the job, (4) percent of workers in the job with a preference for a specific type of job activity such as abstract and creative activity or activity requiring business contact with people, (5) temperamental attributes such as adaptability to performing under stress and adaptability to performing repetitive work, (6) physical demands of the job, and (7) environmental conditions under which the work is performed. Measures of mean hourly and annual earnings for both men and women in each occupation and number of men and women in each occupation also are included.

South Korea's Occupational Wage Survey (OWS) is an annual business establishment survey conducted since 1970 by South Korea's Ministry of Labor. The dataset contains detailed information on individual workers' earnings, hours worked, educational attainment, actual labor market experience, occupation, industry, and region. The surveyed establishments must employ at least ten workers and were selected by a stratified random sampling method. Because they exclude workers in small enterprises, the self-employed, family workers, temporary workers, and public sector workers, the surveys represent approximately one-half of South Korea's total nonagricultural labor force. The samples for each year are randomly drawn from the original surveys. The surveys cover all industries up through 1986. After 1986, agriculture, forestry, hunting, and fishing are excluded. This change in sampling procedure does not appear to cause a significant change in the types of nonfarm enterprises covered by the survey.

This dataset computes the Benefit Cost Rate and Average Tax Rate based on total wages. UI benefits and contributions are divided by total wages in order to control for employment and wage growth. For example, the highest benefit payout was $772 million in 2009. However, 2009 was the third highest payout when controlled for wage growth. Both 1982 and 1983 had higher Benefit Cost Rates. The highest Benefit Cost Rate was 2.63% in 1982. The highest Average Tax Rate based on total wages was 1.89% in 1985. The lowest Benefit Cost Rate was 0.53% in 1998. The lowest Average Tax Rate based on total wages was 0.49% in 1988. Data excludes reimbursable employers. (Time period: 1980-2018).

This table contains 33 series, with data for years 1983 - 2000 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (not all combinations are available): Unit of measure (1 items: Index ...), Geography (13 items: Canada;Prince Edward Island;Nova Scotia;Newfoundland and Labrador ...), Standard Industrial Classification, 1980 (SIC) (21 items: Logging and forestry industries;Mining (including milling); quarrying and oil well industries;Goods producing industries;Industrial aggregate excluding unclassified establishments ...), Fixed weighted index, average hourly earnings (1 items: Fixed weighted index; average hourly earnings ...), Type of employee (1 items: All employees ...).

This table contains 26 series, with data for years 1926 - 1960 (not all combinations necessarily have data for all years), and was last released on 2000-02-18. This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...), Wages and salaries (2 items: Based on Standard Industrial Classification; 1948 (SIC); Based on Standard Industrial Classification; 1980 (SIC) ...), Sector (13 items: Total labour income; Agriculture; Forestry; Total wages and salaries ...).

This table contains 22 series, with data for years 1926 - 1960 (not all combinations necessarily have data for all years), and was last released on 2000-02-18. This table contains data described by the following dimensions (Not all combinations are available): Geography (11 items: Canada; Newfoundland and Labrador; Nova Scotia; Prince Edward Island ...), Wages and salaries (2 items: Based on Standard Industrial Classification; 1948 (SIC); Based on Standard Industrial Classification; 1980 (SIC) ...).

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

Key observations

• Employment patterns: Within Hancock, among individuals aged 15 years and older with income, there were 1,980 men and 1,747 women in the workforce. Among them, 615 men were engaged in full-time, year-round employment, while 630 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 10.73% fell within the income range of under $24,999, while 16.98% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 13.98% of men in full-time roles earned incomes exceeding $100,000, while 7.78% 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)

https://i.neilsberg.com/ch/hancock-mi-income-distribution-by-gender-and-employment-type.jpeg" alt="Hancock, MI gender and employment-based income distribution analysis (Ages 15+)">

Content

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

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

Key observations

• Employment patterns: Within Lynn County, among individuals aged 15 years and older with income, there were 1,980 men and 1,770 women in the workforce. Among them, 1,095 men were engaged in full-time, year-round employment, while 615 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 8.31% fell within the income range of under $24,999, while 19.67% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 30.23% of men in full-time roles earned incomes exceeding $100,000, while 10.73% 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 Lynn County median household income by race. You can refer the same here

Pakistan Average Monthly Wages data was reported at 24,028.000 PKR in 2021. This records an increase from the previous number of 21,326.000 PKR for 2019. Pakistan Average Monthly Wages data is updated yearly, averaging 12,636.500 PKR from Jun 2008 (Median) to 2021, with 10 observations. The data reached an all-time high of 24,028.000 PKR in 2021 and a record low of 6,612.000 PKR in 2008. Pakistan Average Monthly Wages data remains active status in CEIC and is reported by Pakistan Bureau of Statistics. The data is categorized under Global Database’s Pakistan – Table PK.G004: Average Monthly Wages: by Industry. No data for 2016-2017 as per source. Labour Force Survey has not been conducted for these two years due to Population Census.

This table contains 231 series, with data for years 1961-1980 (not all combinations necessarily have data for all years), and was last released on 2006-10-06. This table contains data described by the following dimensions (Not all combinations are available): Geography (15 items: Canada; Newfoundland and Labrador; Prince Edward Island; Nova Scotia ...), Sources and disposition of personal income (16 items: Equals: personal saving; Wages; salaries and supplementary labour income; Equals: personal income; Equals: personal disposable income ...).