New Earnings Survey (NES) and Annual Survey of Hours and Earnings (ASHE) percentile and median time series by full-time employees, full-time males and full-time females.

In 2021, the minimum wage in the United States would be 22.88 U.S. dollars an hour if it grew with productivity. However, the current federal minimum wage in the United States 7.25 U.S. dollars an hour and has remained unchanged since 2009.

The analysis of real wages has a long tradition in Germany. The focus of the acquisition is on company wages, on wages of certain branches or for categories of workers as well as on the investigation of long term aggregated nominal and real wages. The study of Ashok V. Desai on the development of real wages in the German Reich between 1871 and 1913 is an important contribution to historical research on wages. The study is innovative and methodically on an exemplary level. But mainly responsible for the upswing in the historical research on wages in the 50s and 60s is an extraordinary publication by Jürgen Kuczynski. “The new historical research on wages in Germany is insolubly connected with Jürgen Kuczynski. In his broad researches the history of wages is only one section among many other themes but it is a very important one can be seen as the core piece of his work.” (Kaufhold, K.H., 1987: Forschungen zur deutschen Preis- und Lohngeschichte (seit 1930). In: Historia Socialis et Oeconomica. Festschrift für Wolfgang Zorn zum 65. Geburtstag. Stuttgart: Franz Steiner Verlag, S, 83). In his first study on long series on nominal and real wages in Germany he used a broad empirical basis and encouraged more research in this area. His weaknesses are methodological inconsistencies and a restricted representativeness. For example he includes tariff wages but also actually paid wages. Some important industries like food or textile industry are not taken into account. Wages in agriculture were often estimated but without enough material necessary for a good estimation. Wages for work at home are not regraded in the calculation of the index. The weight of cities in the calculation of the index is relatively too high compared to rural regions and therefor it leaks regional representativeness.In his study Desai uses the reports of trade associations for the Reich´s insurance office on the persons who are insured in the accident insurance and their wages as a basis for the calculation of annual nominal average wages. Desais focusses on industrial wages because only for them long term series are available. As the insurance premiums are calculated according to the income level the documents of the trade associations can be used for the calculation of an index for wages development. Desais study is also very useful regarding the calculation of a new index for costs of living based the model of a typical worker family. „F. Grumbach and H. König have used the same sources to derive indices of industrial earnings. The main differences between their series and ours are: (a) we have adopted the industrial classification followed by the Reichsversicherungsamt, while Grumbach and König have made larger industrial groups, (b) we have calculated average annual earnings, while they claim to have calculated average daily earnings (i.e. to have adjusted the annual figures for the average number of days worked per year per worker), and (c) they have failed to correct distortions in the original data” (Desai, A.V., 1968: Real Wages in Germany 1871–1913. Oxford. Clarendon Press, S. 4). Register of tables in HISTAT:A. OverviewsA.1 Overview: Different estimations of the real and nominal gross wages in the German Reich, index 1913 = 100 (1871-1913)A.2 Overview: Development of costs of living, index 1913 = 100 (1871-1913)A.3 Overview: Development of nominal and real wages, index 1913=100 (1844-1937) D. Study by Ashok V. DesaiD.01 Different estimations of real wages in the German Reich, index 1895 = 100 (1871-1913)D.02 Annual average wage (1871-1886)D.03 Annual gross wages in chosen production segments (1887-1913)D.04 Annual average wage in industry, transportation and trade (1871-1913)D.05 Construction of an index for costs of living, 1895 = 100 (1871-1913)D.06 Real wages, in constant prices from 1895 (1871-1913)D.07 Wheat prices and prices for wheat bread (1872-1913)D.08 Rye prices and prices for rye bread (1872-1913)D.09 Average export prices by product groups, index 1895 = 100 (1872-1913)D.10 Average import prices by product groups, index 1895 = 100 (1872-1913)D.11 Average export prices, import prices and terms of trade, index 1895 = 100 (1872-1913) O. Study by Thomas J. OrsaghO. Adjusted indices for costs of living and real wages after Orsgah, index 1913 = 100 (1871-1913)

Graph and download economic data for Federal Minimum Wage Rate under the Federal Fair Labor Standards Act (STTMINWGFG) from 1968 to 2025 about minimum wage, federal, wages, labor, rate, and USA.

When adjusted for inflation, the 2024 federal minimum wage in the United States is over 40 percent lower than the minimum wage in 1970. Although the real dollar minimum wage in 1970 was only 1.60 U.S. dollars, when expressed in nominal 2024 dollars this increases to 13.05 U.S. dollars. This is a significant difference from the federal minimum wage in 2024 of 7.25 U.S. dollars.

Graph and download economic data for State Minimum Wage Rate for Ohio (STTMINWGOH) from 1968 to 2025 about minimum wage, OH, wages, rate, and USA.

United States Household Income: $200,000 & Over data was reported at 7.700 % in 2017. This records an increase from the previous number of 7.200 % for 2016. United States Household Income: $200,000 & Over data is updated yearly, averaging 3.400 % from Mar 1967 (Median) to 2017, with 51 observations. The data reached an all-time high of 7.700 % in 2017 and a record low of 1.000 % in 1968. United States Household Income: $200,000 & Over data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.H049: Household Income: by Income Level.

Abstract copyright UK Data Service and data collection copyright owner.

This machine-readable version of John Williams' Digest of Welsh Historical Statistics is the result of a collaboration between the Statistical Directorate of the National Assembly for Wales, the History Data Service and the Centre for Data Digitisation and Analysis at Queen's University Belfast.
John Williams' Digest of Welsh Historical Statistics is intended to provide a service for those working on the history of modern Wales. It arises from a belief that the quantitative element is a necessary and important part of the historical record; from an awareness that it was an aspect that was particularly inaccessible for scholars of Welsh history; and from a conviction that some encouragement in the use of quantitative material was necessary. It is modelled on the two volumes dedicated to British historical statistics: Mitchell, B.R. and Deane, P. (1962) Abstract of British historical statistics and Mitchell, B.R. and Jones, H.G. (1971) Second abstract of British historical statistics.
Main Topics:
The main tables are:


Coal. Percentage changes in wage rates of miners, South Wales, 1848-1939.
Coal. Average earnings, 1840-1974
Agriculture. Nominal weekly wages 1788-1882; average weekly earnings by county 1867-1917; minimum rates by county 1918-1939; weekly earnings and hours 1967-1957.
Police. Maximum and minimum rates of pay of police constables by county, 1902-1914.
Building. Hours and rates per hour in the building trade, main towns, 186-1914 and 1936.
Engineering and ship-repairing. Rates for different trades, main ports, 1873-1968.
Rates of pay and hours for printing trades, main towns, 1850-1968; and for cabinet makers 1901-1914.
Average weekly and hourly earnings, and average hours for manual workers over 20 in manufacturing, all industries an d selected individual industries, 1960-1975.
Household income and expenditure, Wales and UK, 1953-1954 and 1961-1973.
Proportion of households possessing certain amenities, Wales and UK, 1964-1973.

Please note: this study does not include information on named individuals and would therefore not be useful for personal family history research.

Graph and download economic data for State Minimum Wage Rate for Minnesota (STTMINWGMN) from 1968 to 2025 about minimum wage, MN, wages, rate, and USA.

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 Bunker Hill. 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 Bunker Hill, the median income for all workers aged 15 years and older, regardless of work hours, was $59,231 for males and $18,942 for females.

These income figures highlight a substantial gender-based income gap in Bunker Hill. Women, regardless of work hours, earn 32 cents for each dollar earned by men. This significant gender pay gap, approximately 68%, underscores concerning gender-based income inequality in the city of Bunker Hill.

- Full-time workers, aged 15 years and older: In Bunker Hill, among full-time, year-round workers aged 15 years and older, males earned a median income of $59,712, while females earned $19,375, leading to a 68% gender pay gap among full-time workers. This illustrates that women earn 32 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 Bunker Hill, 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 Bunker Hill median household income by race. You can refer the same here

Graph and download economic data for State Minimum Wage Rate for Colorado (STTMINWGCO) from 1968 to 2025 about minimum wage, CO, wages, rate, and USA.

The weekly average income of seventeen-year-olds was four times higher than that of 8-year-olds in the United Kingdom (UK) between March 2023 and February 2024. There was a clear correlation between their age and the value of their income, with children aged six having an income of 5,68 British pounds per week. That income was composed of the value of pocket money per week, the money obtained from doing chores, and other sources of income for children.

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 Waite Hill. 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 Waite Hill, the median income for all workers aged 15 years and older, regardless of work hours, was $127,917 for males and $40,625 for females.

These income figures highlight a substantial gender-based income gap in Waite Hill. Women, regardless of work hours, earn 32 cents for each dollar earned by men. This significant gender pay gap, approximately 68%, underscores concerning gender-based income inequality in the village of Waite Hill.

- Full-time workers, aged 15 years and older: In Waite Hill, among full-time, year-round workers aged 15 years and older, males earned a median income of $154,896, while females earned $106,071, leading to a 32% gender pay gap among full-time workers. This illustrates that women earn 68 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 Waite Hill, 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 Waite Hill median household income by race. You can refer the same here