Following the inauguration of Franklin D. Roosevelt, government relief spending increased drastically. In his first year in office, workers in major cities were receiving benefits equal to just over one-fifth of average manufacturing wages. By 1936, relief benefits had risen to over two-fifths of the value of manufacturing wages - this also coincided with a wage increase from around 17 U.S. dollars per week in 1933 to 23 U.S. dollars in 1936, which means that the total value of relief benefits more than doubled in these years.

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)

PERIOD: 1930-1934. NOTE: Average wages at or near the locations of 13 chambers of commerce in major cities. (In yen). SOURCE: [Monthly Statistics on Wages].

PERIOD: Japan proper. 1924-1930. By occupation in 1930. SOURCE: [Monthly Statistics on Wages and Prices].

Graph and download economic data for Real Gross National Income (A023RL1A225NBEA) from 1930 to 2024 about GNI, income, real, GDP, rate, and USA.

PERIOD: 1925-1929. NOTE: Average wages at or near the locations of 13 chambers of commerce in major cities. SOURCE: [Monthly Statistics on Wages].

Graph and download economic data for Real average of GDP and GDI (PB0000091A225NBEA) from 1930 to 2024 about GDI, average, income, real, GDP, rate, and USA.

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

Key observations

• Employment patterns: Within Big Stone County, among individuals aged 15 years and older with income, there were 2,007 men and 1,930 women in the workforce. Among them, 892 men were engaged in full-time, year-round employment, while 542 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 5.94% fell within the income range of under $24,999, while 10.52% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 19.39% of men in full-time roles earned incomes exceeding $100,000, while 9.59% 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 Big Stone County median household income by race. 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 Pickett County. The dataset can be utilized to gain insights into gender-based income distribution within the Pickett County population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Pickett County, among individuals aged 15 years and older with income, there were 1,930 men and 1,754 women in the workforce. Among them, 822 men were engaged in full-time, year-round employment, while 633 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 8.15% fell within the income range of under $24,999, while 15.80% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 15.94% of men in full-time roles earned incomes exceeding $100,000, while 1.90% 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 Pickett County median household income by race. 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 West Bend town. The dataset can be utilized to gain insights into gender-based income distribution within the West Bend town population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within West Bend town, among individuals aged 15 years and older with income, there were 1,930 men and 1,764 women in the workforce. Among them, 1,095 men were engaged in full-time, year-round employment, while 642 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 10.96% fell within the income range of under $24,999, while 16.67% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 22.19% of men in full-time roles earned incomes exceeding $100,000, while 25.70% 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 West Bend town median household income by race. You can refer the same here

Graph and download economic data for Real Income Receipts from the Rest of the World (B645RL1A225NBEA) from 1930 to 2024 about receipts, income, real, GDP, rate, and USA.

Graph and download economic data for Real Gross Domestic Income (A261RL1A225NBEA) from 1930 to 2024 about GDI, income, real, rate, and USA.

Graph and download economic data for Disposable Personal Income (A067RP1A027NBEA) from 1930 to 2024 about disposable, personal income, personal, income, GDP, rate, and USA.

PERIOD: 1926-1930. NOTE: Average wages at or near the locations of 13 chambers of commerce in major cities. SOURCE: [Monthly Statistics on Wages].

Graph and download economic data for Wage and salary accruals per full-time equivalent employee: Domestic industries: State and local general government: Work relief (B4497C0A052NBEA) from 1930 to 1942 about accruals, social assistance, state & local, full-time, salaries, domestic, wages, government, employment, industry, GDP, and USA.

Graph and download economic data for Real Net Domestic Income (W256RL1A225NBEA) from 1930 to 2024 about domestic, Net, income, real, GDP, rate, and USA.

PERIOD: For factory workers, as of October 10, 1927. For miners, as of October 10, 1930. SOURCE: [Survey by the Statistics Bureau, Imperial Cabinet].

PERIOD: 1921-1930. By government office at 1930 year-end. SOURCE: [Reports by various departments].

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

Key observations

• Employment patterns: Within Mingo County, among individuals aged 15 years and older with income, there were 7,621 men and 7,341 women in the workforce. Among them, 2,679 men were engaged in full-time, year-round employment, while 1,930 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 15.30% fell within the income range of under $24,999, while 19.59% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 18.78% of men in full-time roles earned incomes exceeding $100,000, while 9.53% 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 Mingo County median household income by race. You can refer the same here

This table contains 11 series, with data for years 1926 - 1960 (not all combinations necessarily have data for all years), and was last released on 2009-01-21. This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...), Income-based estimates (11 items: Gross domestic product (GDP) at market prices; Net domestic income at factor cost; Wages; salaries and supplementary labour income; Corporation profits before taxes ...).