Labor unions, or trade unions as they are known in Europe, are organizations formed by workers in order to represent their collective interests, particularly in relation to wages and working conditions. Historically, labor unions emerged during the industrial revolution of the nineteenth century to represent the interests of industrial workers, who flocked to work in factories, mines, and other growing manufacturing enterprises. In most high-income countries, labor unions reached their peak during the post-WWII period, when governments mediated between the interests of labor unions and the owners of capital. With the economic crises of the 1970s, however, the labor movement suffered historic defeats in Europe and North America, with union density declining rapidly in many countries due to a host of pro-market and anti-union policies which have come to be referred to as 'neoliberalism'. Labor unions today In the twenty-first century, labor unions have retreated from their key role in national economic decisions in many countries, as globalization has lowered barriers to movement of labor, enabled 'off-shoring' jobs to lower wage countries, and promoted the lowering of labor standards in order to pursue cost competitiveness. In spite of this trend, certain regions still showcase high levels of union density and retain their traditions of unions being involved in determining economic policy. Notably, the Nordic countries make up five of the top six most unionized countries, with Iceland in first place being followed by Denmark, Sweden, Finland, and then Norway.

Other notable trends among the top placed countries are states which have had a historical relationship with communism (often a key driver of the labor movement), such as Cuba, Vietnam, China, and Kazakhstan. In the wake of the Covid-19 pandemic, labor unions and the wider labor movement has become more prominent, as workers have sought to fight for health & safety conditions in the workplace, as well as to combat high inflation related to the pandemic.

Graph and download economic data for Employed Persons in Union County, OR (LAUCN410610000000005) from Jan 1990 to May 2025 about Union County, OR; OR; household survey; employment; persons; and USA.

description: The Current Population Survey (CPS) is a sample survey of the population 16 years of age and over. The survey is conducted each month by the U.S. Census Bureau for the Bureau of Labor Statistics and provides comprehensive data on the labor force, the employed, and the unemployed, classified by such characteristics as age, sex, race, family relationship, marital status, occupation, and industry attachment. The information is collected by trained interviewers from a sample of about 60,000 households located in 754 sample areas. These areas are chosen to represent all counties and independent cities in the United States, with coverage in 50 States and the District of Columbia. The data collected are based on the activity or status reported for the calendar week including the 12th of the month. Union data are available for all workers, members of unions and represented by unions, with data available by age, race, Hispanic or Latino ethnicity, sex, occupation, industry, state, and full- or part-time status. Median weekly earnings data are also available for members of unions, represented by unions and non-union with data available by age, race, Hispanic or Latino ethnicity, sex, occupation, industry and full- or part-time status.; abstract: The Current Population Survey (CPS) is a sample survey of the population 16 years of age and over. The survey is conducted each month by the U.S. Census Bureau for the Bureau of Labor Statistics and provides comprehensive data on the labor force, the employed, and the unemployed, classified by such characteristics as age, sex, race, family relationship, marital status, occupation, and industry attachment. The information is collected by trained interviewers from a sample of about 60,000 households located in 754 sample areas. These areas are chosen to represent all counties and independent cities in the United States, with coverage in 50 States and the District of Columbia. The data collected are based on the activity or status reported for the calendar week including the 12th of the month. Union data are available for all workers, members of unions and represented by unions, with data available by age, race, Hispanic or Latino ethnicity, sex, occupation, industry, state, and full- or part-time status. Median weekly earnings data are also available for members of unions, represented by unions and non-union with data available by age, race, Hispanic or Latino ethnicity, sex, occupation, industry and full- or part-time status.

Graph and download economic data for Civilian Labor Force in Union County, SC (LAUCN450870000000006A) from 1990 to 2024 about Union County, SC; SC; civilian; labor force; labor; household survey; and USA.

Graph and download economic data for Civilian Labor Force in Union County, NJ (NJUNIO9LFN) from Jan 1990 to May 2025 about Union County, NJ; NJ; New York; civilian; labor force; labor; and USA.

A. SUMMARY This dataset is a count of CCSF Employees enrolled in Medical, Dental Plans.

B. HOW THE DATASET IS CREATED This dataset is an extract from PeopleSoft. It is then aggregated by union, plan, and plan type. For HIPAA reasons all count less than 26 have been masked with a 0.

C. UPDATE PROCESS This dataset will be updated on an annual basis.

D. HOW TO USE THIS DATASET The dataset is sliced by Union and Coverage tier. Coverage tier is segmented into three categories, i.e., Employees Only (EE Only), Employee plus one dependent (EE+1), Employees plus two or more dependents (EE+2).

"Craft Coalitions (CC)" encompass these unions - 007-BrickLayers, Local 3 012-Carpet, Linoleum & Soft Tile 016-Theatrical Stage Emp, Local 16 034-Pile Drivers, Local 34 036-Hod Carriers, LiUNA, Local 261 040-Roofers, Local 40 104-Sheet Metal Workers, Local 104 216-Teamsters, Local 853 236-Carpenters, Local 22 377-Iron Workers, Local 377 580-Cement Masons, Local 300 (580) 718-Glaziers, Local 718
"Consolidated Small Unions (CSU)" are unions consolidated due to small populations per HIPAA restrictions:001-Misc. Unrepresented Employees 220-Law Librarian and Assistant 257-Member, Board Of Sups 302-Indv. Employment Contract-MTA 352-Municipal Exec Assoc, Fire 353-Municipal Exec Assoc, Police 419-SFDA Investigators Assn 556-Elected Officials 965-Sup Probation Ofcr, Op Eng 3 969-SFIPOA, Op Eng, Local 3 990-Unrepresented Contract Rte FBP

The company situation of employees in the Federal Republic and their attitude to co-determination.

Topics: characterization of job; length of company employment; change of companies; work satisfaction; judgement on the business and development chances of the area of business; relationship with superiors and colleagues; judgement on employee status in comparison with the status of a worker; attitude to continued payment of wages during sickness for workers; judgement on the job of the works council; characterization of the most important areas of co-determination for employees; satisfaction with co-determination possibilities; knowledge about so-called ´expanded co-determination´ and assessment of the chances of realization of such a demand by the trade unions; attitude to co-determination (scale); achievement-based pay; satisfaction with income; type of long-term savings program; savings goals and extent of savings; right to vacation, judgement on the appropriateness of vacation; union vacation or additional days of vacation; attitude to education leave; membership in a trade union; reasons for possible intent to leave or reasons for not joining; attitude to trade unions; knowledge about trade unions; knowledge about union representatives in the company or government office; attitude to trade unions as representation of all employees; judgement on the importance of selected trade union demands; attitude to strike calls by the trade union during wage negotiation conflicts as well as a political general strike; reading trade union magazines; judgement on the economic situation of the FRG and personal economic situation; judgement on the economic policy of the past few months and assignment of responsibility for these policies; judgement on the issue relevance and issue ability of the parties; judgement on equal opportunities in the FRG; satisfaction with country and society; judgement on political influence of selected special interest groups and media; political interest; sympathy scale for the CDU, SPD, FDP and NPD; behavior at the polls in the Federal Parliament election 1965; party preference and party ties; assessment of the result of the Federal Parliament election 1969; preferred government coalition; party preferences of family and friends; memberships; self-assessment of social class; importance of work and leisure time; athletic activities and organization of leisure time; most important criteria in judging people; topics of conversation within the family; friendships; media usage; knowledge of foreign languages.

For the survey groups pensioners, self-employed and dependents of employees extensive supplemental questions were posed.

Demography: age; sex; marital status; religious denomination; frequency of church attendance; school education; occupational training; occupation; employment; area of business of company; company size; income; household income; number of recipients of income; possession of durable economic goods; household size; household composition; respondent is head of household; characteristics of head of household; residential status; local residency; social origins; degree of urbanization; refugee status; union membership.

Interviewer rating: willingness of respondent to cooperate; interest in interview; reliability of respondent.

Graph and download economic data for Civilian Labor Force in Union County, AR (ARUNLFN) from Jan 1990 to May 2025 about Union County, AR; AR; civilian; labor force; labor; and USA.

Sampling Procedure Comment: Multi-stage stratified random sample

This resource is a member of a series. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The All Roads Shapefile includes all features within the MTDB Super Class "Road/Path Features" distinguished where the MAF/TIGER Feature Classification Code (MTFCC) for the feature in MTDB that begins with "S". This includes all primary, secondary, local neighborhood, and rural roads, city streets, vehicular trails (4wd), ramps, service drives, alleys, parking lot roads, private roads for service vehicles (logging, oil fields, ranches, etc.), bike paths or trails, bridle/horse paths, walkways/pedestrian trails, and stairways.

About this dataset

Context

The dataset tabulates the population of Union County by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Union County across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of female population, with 50.52% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Union County is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Union County total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Union County Population by Race & Ethnicity. You can refer the same here

This resource is a member of a series. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The All Roads Shapefile includes all features within the MTDB Super Class "Road/Path Features" distinguished where the MAF/TIGER Feature Classification Code (MTFCC) for the feature in MTDB that begins with "S". This includes all primary, secondary, local neighborhood, and rural roads, city streets, vehicular trails (4wd), ramps, service drives, alleys, parking lot roads, private roads for service vehicles (logging, oil fields, ranches, etc.), bike paths or trails, bridle/horse paths, walkways/pedestrian trails, and stairways.

This resource is a member of a series. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The All Roads Shapefile includes all features within the MTDB Super Class "Road/Path Features" distinguished where the MAF/TIGER Feature Classification Code (MTFCC) for the feature in MTDB that begins with "S". This includes all primary, secondary, local neighborhood, and rural roads, city streets, vehicular trails (4wd), ramps, service drives, alleys, parking lot roads, private roads for service vehicles (logging, oil fields, ranches, etc.), bike paths or trails, bridle/horse paths, walkways/pedestrian trails, and stairways.

This is a count of CCSF Employees enrolled in Medical and Dental Plans This is sliced by Union and Coverage tier. The coverage tier is segmented into three categories, i.e., Employees Only (EE Only), Employee plus one dependent (EE+1), Employees plus two or more dependents (EE+2).

"Craft Coalitions (CC)" encompass these unions -
007-BrickLayers, Local 3 012-Carpet, Linoleum & Soft Tile 016-Theatrical Stage Emp, Local 16 034-Pile Drivers, Local 34 036-Hod Carriers, LiUNA, Local 261 040-Roofers, Local 40 104-Sheet Metal Workers, Local 104 216-Teamsters, Local 853 236-Carpenters, Local 22 377-Iron Workers, Local 377 580-Cement Masons, Local 300 (580) 718-Glaziers, Local 718

"Consolidated Small Unions (CSU)" are unions consolidated due to small populations per HIPAA restrictions:001-Misc. Unrepresented Employees 220-Law Librarian and Assistant 257-Member, Board Of Sups 302-Indv. Employment Contract-MTA 352-Municipal Exec Assoc, Fire 353-Municipal Exec Assoc, Police 419-SFDA Investigators Assn 556-Elected Officials 965-Sup Probation Ofcr, Op Eng 3 969-SFIPOA, Op Eng, Local 3 990-Unrepresented Contract Rte FBP

This dataset tracks annual distribution of students across grade levels in Union School Corporation School District and average distribution per school district in Indiana

Historical Dataset of Union School Corporation School District is provided by PublicSchoolReview and contain statistics on metrics:Comparison of Diversity Score Trends,Total Revenues Trends,Total Expenditure Trends,Average Revenue Per Student Trends,Average Expenditure Per Student Trends,Reading and Language Arts Proficiency Trends,Math Proficiency Trends,Science Proficiency Trends,Graduation Rate Trends,Overall School District Rank Trends,Asian Student Percentage Comparison Over Years (2011-2023),Hispanic Student Percentage Comparison Over Years (1991-2023),Black Student Percentage Comparison Over Years (2019-2023),White Student Percentage Comparison Over Years (1991-2023),Two or More Races Student Percentage Comparison Over Years (2011-2023),Comparison of Students By Grade Trends

Graph and download economic data for Employed Persons in Union County, IA (LAUCN191750000000005) from Jan 1990 to May 2025 about Union County, IA; IA; household survey; employment; persons; and USA.

This resource is a member of a series. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The All Roads Shapefile includes all features within the MTDB Super Class "Road/Path Features" distinguished where the MAF/TIGER Feature Classification Code (MTFCC) for the feature in MTDB that begins with "S". This includes all primary, secondary, local neighborhood, and rural roads, city streets, vehicular trails (4wd), ramps, service drives, alleys, parking lot roads, private roads for service vehicles (logging, oil fields, ranches, etc.), bike paths or trails, bridle/horse paths, walkways/pedestrian trails, and stairways.

About this dataset

Context

The dataset tabulates the population of Union township by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Union township. The dataset can be utilized to understand the population distribution of Union township by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Union township. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Union township.

Key observations

Largest age group (population): Male # 50-54 years (59) | Female # 50-54 years (63). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Union township population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Union township is shown in the following column.
• Population (Female): The female population in the Union township is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Union township for each age group.

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 Union township Population by Gender. 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 Union County. 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 Union County, the median income for all workers aged 15 years and older, regardless of work hours, was $34,081 for males and $26,728 for females.

These income figures indicate a substantial gender-based pay disparity, showcasing a gap of approximately 22% between the median incomes of males and females in Union County. With women, regardless of work hours, earning 78 cents to each dollar earned by men, this income disparity reveals a concerning trend toward wage inequality that demands attention in thecounty of Union County.

- Full-time workers, aged 15 years and older: In Union County, among full-time, year-round workers aged 15 years and older, males earned a median income of $53,024, while females earned $46,979, resulting in a 11% gender pay gap among full-time workers. This illustrates that women earn 89 cents for each dollar earned by men in full-time positions. While this gap shows a trend where women are inching closer to wage parity with men, it also exhibits a noticeable income difference for women working full-time in the county of Union County.

Interestingly, when analyzing income across all roles, including non-full-time employment, the gender pay gap percentage was higher for women compared to men. It appears that full-time employment presents a more favorable income scenario for women compared to other employment patterns in Union County.

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