Graph and download economic data for Labor Force Participation Rate - 20 Yrs. & over, White Men (LNS11300028) from Jan 1954 to Jun 2025 about 20 years +, males, participation, white, labor force, labor, household survey, rate, and USA.

Graph and download economic data for Civilian Labor Force Level - 20 Yrs. & over, White Men (LNS11000028) from Jan 1954 to Jun 2025 about 20 years +, males, civilian, white, labor force, labor, household survey, and USA.

United States - Labor Force Participation Rate - 20 Yrs. & over, White Men was 69.70% in April of 2025, according to the United States Federal Reserve. Historically, United States - Labor Force Participation Rate - 20 Yrs. & over, White Men reached a record high of 88.40 in August of 1954 and a record low of 69.00 in April of 2020. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Labor Force Participation Rate - 20 Yrs. & over, White Men - last updated from the United States Federal Reserve on July of 2025.

United States - Civilian Labor Force Level - 20 Yrs. & over, White Men was 66593.00000 Thous. of Persons in May of 2025, according to the United States Federal Reserve. Historically, United States - Civilian Labor Force Level - 20 Yrs. & over, White Men reached a record high of 66794.00000 in January of 2025 and a record low of 37657.00000 in July of 1954. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Civilian Labor Force Level - 20 Yrs. & over, White Men - last updated from the United States Federal Reserve on July of 2025.

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

Key observations

• Employment patterns: Within White, among individuals aged 15 years and older with income, there were 287 men and 235 women in the workforce. Among them, 161 men were engaged in full-time, year-round employment, while 133 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 4.35% fell within the income range of under $24,999, while 4.51% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 2.48% of men in full-time roles earned incomes exceeding $100,000, while none 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 White median household income by race. You can refer the same here

39.8% of workers from the Indian ethnic group were in 'professional' jobs in 2021 – the highest percentage out of all ethnic groups in this role.

According to a 2023 study, around ** percent of cybersecurity professionals aged 60 or older in the United States, Canada, the United Kingdom, and Ireland were white men. The proportion of non-white men in this age group reached ** percent, while the share of white and non-white women aged 60 or older was around ** and two percent, respectively. On the other hand, non-white men constituted ** percent of the cybersecurity workforce under **, and the share of non-white women in this age group reached ** percent.

Proportion of women and men employed in the National Occupational Classification (NOC) broad occupational categories, current year.

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 White Oak 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 White Oak township, the median income for all workers aged 15 years and older, regardless of work hours, was $43,750 for males and $42,250 for females.

Based on these incomes, we observe a gender gap percentage of approximately 3%, indicating a significant disparity between the median incomes of males and females in White Oak township. Women, regardless of work hours, still earn 97 cents to each dollar earned by men, highlighting an ongoing gender-based wage gap.

Surprisingly, within the subset of full-time workers, women earn a higher income than men, earning 1.07 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 White Oak township median household income by race. You can refer the same here

Graph and download economic data for Labor Force Participation Rate - Men (LNS11300001) from Jan 1948 to Jun 2025 about males, participation, 16 years +, labor force, labor, household survey, rate, and USA.

Diversity in Tech Statistics: In today's tech-driven world, discussions about diversity in the technology sector have gained significant traction. Recent statistics shed light on the disparities and opportunities within this industry. According to data from various sources, including reports from leading tech companies and diversity advocacy groups, the lack of diversity remains a prominent issue. For example, studies reveal that only 25% of computing jobs in the United States are held by women, while Black and Hispanic individuals make up just 9% of the tech workforce combined. Additionally, research indicates that LGBTQ+ individuals are underrepresented in tech, with only 2.3% of tech workers identifying as LGBTQ+. Despite these challenges, there are promising signs of progress. Companies are increasingly recognizing the importance of diversity and inclusion initiatives, with some allocating significant resources to address these issues. For instance, tech giants like Google and Microsoft have committed millions of USD to diversity programs aimed at recruiting and retaining underrepresented talent. As discussions surrounding diversity in tech continue to evolve, understanding the statistical landscape is crucial in fostering meaningful change and creating a more inclusive industry for all. Editor’s Choice In 2021, 7.9% of the US labor force was employed in technology. Women hold only 26.7% of tech employment, while men hold 73.3% of these positions. White Americans hold 62.5% of the positions in the US tech sector. Asian Americans account for 20% of jobs, Latinx Americans 8%, and Black Americans 7%. 83.3% of tech executives in the US are white. Black Americans comprised 14% of the population in 2019 but held only 7% of tech employment. For the same position, at the same business, and with the same experience, women in tech are typically paid 3% less than men. The high-tech sector employs more men (64% against 52%), Asian Americans (14% compared to 5.8%), and white people (68.5% versus 63.5%) compared to other industries. The tech industry is urged to prioritize inclusion when hiring, mentoring, and retaining employees to bridge the digital skills gap. Black professionals only account for 4% of all tech workers despite being 13% of the US workforce. Hispanic professionals hold just 8% of all STEM jobs despite being 17% of the national workforce. Only 22% of workers in tech are ethnic minorities. Gender diversity in tech is low, with just 26% of jobs in computer-related sectors occupied by women. Companies with diverse teams have higher profitability, with those in the top quartile for gender diversity being 25% more likely to have above-average profitability. Every month, the tech industry adds about 9,600 jobs to the U.S. economy. Between May 2009 and May 2015, over 800,000 net STEM jobs were added to the U.S. economy. STEM jobs are expected to grow by another 8.9% between 2015 and 2024. The percentage of black and Hispanic employees at major tech companies is very low, making up just one to three percent of the tech workforce. Tech hiring relies heavily on poaching and incentives, creating an unsustainable ecosystem ripe for disruption. Recruiters have a significant role in disrupting the hiring process to support diversity and inclusion. You May Also Like To Read Outsourcing Statistics Digital Transformation Statistics Internet of Things Statistics Computer Vision Statistics

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

Key observations

• Employment patterns: Within White Pine County, among individuals aged 15 years and older with income, there were 2,969 men and 2,932 women in the workforce. Among them, 1,534 men were engaged in full-time, year-round employment, while 1,289 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 9.52% fell within the income range of under $24,999, while 10.86% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 15.71% of men in full-time roles earned incomes exceeding $100,000, while 12.26% 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 White Pine County median household income by race. You can refer the same here

In 2025, there were estimated to be approximately 3.6 billion people employed worldwide, compared to 2.23 billion people in 1991 - an increase of around 1.4 billion people. There was a noticeable fall in global employment between 2019 and 2020, when the number of employed people fell from due to the sudden economic shock caused by the COVID-19 pandemic. Formal vs. Informal employment globally Worldwide, there is a large gap between the informally and formally employed. Most informally employed workers reside in the Global South, especially Africa and Southeast Asia. Moreover, men are slightly more likely to be informally employed than women. The majority of informal work, nearly 90 percent, is within the agricultural sector, with domestic work and construction following behind. Women’s employment As the number of employees has risen globally, so has the number of employed women. Overall, care roles such as nursing and midwifery have the highest shares of female employees globally. Moreover, while the gender pay gap has shrunk over time, it still exists. As of 2024, the uncontrolled gender pay gap was 0.83, meaning women made, on average, 83 cents per every dollar earned by men.

Graph and download economic data for Infra-Annual Labor Statistics: Labor Force Participation Rate Male: From 25 to 54 Years for United States (LRAC25MAUSM156S) from Jan 1955 to May 2025 about 25 to 54 years, males, participation, labor force, labor, rate, and USA.

Overall, the participation rate in White Rock, BC is declining at a rate of 0.31% per year over the past 15 years from 2001 to 2016. In the last two census, its participation rates declined by 5.5%, an average growth rate of -1.1% per year from 2011 to 2016. A decrease in participation rate means the proportion of the working population in White Rock, BC is lower than in the past.

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

Key observations

• Employment patterns: Within Fort White, among individuals aged 15 years and older with income, there were 209 men and 222 women in the workforce. Among them, 113 men were engaged in full-time, year-round employment, while 114 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 7.96% fell within the income range of under $24,999, while none of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 6.19% of men in full-time roles earned incomes exceeding $100,000, while 1.75% 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 Fort White median household income by race. You can refer the same here

Austria Employment: Male: Employees: White Collar Workers data was reported at 1,022.482 Person th in Dec 2021. This records an increase from the previous number of 1,022.278 Person th for Sep 2021. Austria Employment: Male: Employees: White Collar Workers data is updated quarterly, averaging 1,016.840 Person th from Mar 2021 (Median) to Dec 2021, with 4 observations. The data reached an all-time high of 1,022.482 Person th in Dec 2021 and a record low of 998.223 Person th in Jun 2021. Austria Employment: Male: Employees: White Collar Workers data remains active status in CEIC and is reported by Statistics Austria. The data is categorized under Global Database’s Austria – Table AT.G010: Labour Force Survey: New Questionnaire: Employment.

According to a 2023 study, the average cybersecurity salary of white men in the United States was around 150 thousand U.S. dollars, approximately five thousand U.S. dollars more than the salary of men of color, and 13 thousand U.S. dollars more than the salary of women of color.

White people made up 83.4% of civil servants in March 2024 – they made up 80.7% of the working age population (16 to 64 year olds) in the 2021 Census.

In 2022, the highest and lowest rates of economic inactivity were in the combined Pakistani and Bangladeshi (33%) and white 'other’ (15%) ethnic groups.