100+ datasets found
  1. Labor Unions: countries with highest share of workforce unionized worldwide

    • statista.com
    Updated Sep 2, 2024
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    Statista (2024). Labor Unions: countries with highest share of workforce unionized worldwide [Dataset]. https://www.statista.com/statistics/1356735/labor-unions-most-unionized-countries-worldwide/
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    Dataset updated
    Sep 2, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Worldwide
    Description

    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.

  2. F

    Employed Persons in Union County, OR

    • fred.stlouisfed.org
    json
    Updated Jul 2, 2025
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    (2025). Employed Persons in Union County, OR [Dataset]. https://fred.stlouisfed.org/series/LAUCN410610000000005
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    jsonAvailable download formats
    Dataset updated
    Jul 2, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Area covered
    Union County
    Description

    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.

  3. d

    Current Population Survey - Union Affiliation Data.

    • datadiscoverystudio.org
    Updated Jun 1, 2017
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    (2017). Current Population Survey - Union Affiliation Data. [Dataset]. http://datadiscoverystudio.org/geoportal/rest/metadata/item/dd9c54149a2f4f5db15e1faf824800af/html
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    Dataset updated
    Jun 1, 2017
    Description

    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.

  4. F

    Civilian Labor Force in Union County, SC

    • fred.stlouisfed.org
    json
    Updated Apr 29, 2025
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    (2025). Civilian Labor Force in Union County, SC [Dataset]. https://fred.stlouisfed.org/series/LAUCN450870000000006A
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    jsonAvailable download formats
    Dataset updated
    Apr 29, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Area covered
    South Carolina, Union County
    Description

    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.

  5. F

    Civilian Labor Force in Union County, NJ

    • fred.stlouisfed.org
    json
    Updated Jul 2, 2025
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    (2025). Civilian Labor Force in Union County, NJ [Dataset]. https://fred.stlouisfed.org/series/NJUNIO9LFN
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    jsonAvailable download formats
    Dataset updated
    Jul 2, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Area covered
    Union County, New Jersey
    Description

    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.

  6. D

    Medical and Dental Enrollment by Union for City & County of SF Employees

    • data.sfgov.org
    application/rdfxml +5
    Updated Jun 6, 2024
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    City & County of San Francisco - Health Service System (2024). Medical and Dental Enrollment by Union for City & County of SF Employees [Dataset]. https://data.sfgov.org/Health-and-Social-Services/Medical-and-Dental-Enrollment-by-Union-for-City-Co/u535-d7q5
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    csv, json, application/rssxml, xml, tsv, application/rdfxmlAvailable download formats
    Dataset updated
    Jun 6, 2024
    Dataset authored and provided by
    City & County of San Francisco - Health Service System
    Area covered
    San Francisco
    Description

    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

  7. c

    Employee Study 1968

    • datacatalogue.cessda.eu
    • search.gesis.org
    • +2more
    Updated Mar 14, 2023
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    INFRATEST (2023). Employee Study 1968 [Dataset]. http://doi.org/10.4232/1.1204
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    Dataset updated
    Mar 14, 2023
    Dataset provided by
    München
    Authors
    INFRATEST
    Time period covered
    Sep 1968 - Oct 1968
    Area covered
    Germany
    Measurement technique
    Oral survey with standardized questionnaire
    Description

    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.

  8. F

    Civilian Labor Force in Union County, AR

    • fred.stlouisfed.org
    json
    Updated Jul 2, 2025
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    (2025). Civilian Labor Force in Union County, AR [Dataset]. https://fred.stlouisfed.org/series/ARUNLFN
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    jsonAvailable download formats
    Dataset updated
    Jul 2, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Area covered
    Union County, Arkansas
    Description

    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.

  9. d

    Employee Study 1968

    • da-ra.de
    Updated 1982
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    INFRATEST, München (1982). Employee Study 1968 [Dataset]. http://doi.org/10.4232/1.1204
    Explore at:
    Dataset updated
    1982
    Dataset provided by
    GESIS Data Archive
    da|ra
    Authors
    INFRATEST, München
    Time period covered
    Sep 1968 - Oct 1968
    Description

    Sampling Procedure Comment: Multi-stage stratified random sample

  10. TIGER/Line Shapefile, Current, County, Union County, SD, All Roads

    • catalog.data.gov
    Updated Dec 15, 2023
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    U.S. Department of Commerce, U.S. Census Bureau, Geography Division, Geospatial Products Branch (Point of Contact) (2023). TIGER/Line Shapefile, Current, County, Union County, SD, All Roads [Dataset]. https://catalog.data.gov/dataset/tiger-line-shapefile-current-county-union-county-sd-all-roads
    Explore at:
    Dataset updated
    Dec 15, 2023
    Dataset provided by
    United States Census Bureauhttp://census.gov/
    Area covered
    Union County
    Description

    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.

  11. N

    Union County, OR Population Breakdown by Gender Dataset: Male and Female...

    • neilsberg.com
    csv, json
    Updated Feb 24, 2025
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    Neilsberg Research (2025). Union County, OR Population Breakdown by Gender Dataset: Male and Female Population Distribution // 2025 Edition [Dataset]. https://www.neilsberg.com/insights/union-county-or-population-by-gender/
    Explore at:
    csv, jsonAvailable download formats
    Dataset updated
    Feb 24, 2025
    Dataset authored and provided by
    Neilsberg Research
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Union County
    Variables measured
    Male Population, Female Population, Male Population as Percent of Total Population, Female Population as Percent of Total Population
    Measurement technique
    The data presented in this dataset is derived from the latest U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. To measure the two variables, namely (a) population and (b) population as a percentage of the total population, we initially analyzed and categorized the data for each of the gender classifications (biological sex) reported by the US Census Bureau. For further information regarding these estimates, please feel free to reach out to us via email at research@neilsberg.com.
    Dataset funded by
    Neilsberg Research
    Description
    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

  12. TIGER/Line Shapefile, Current, County, Union County, TN, All Roads

    • catalog.data.gov
    • datasets.ai
    Updated Dec 15, 2023
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    U.S. Department of Commerce, U.S. Census Bureau, Geography Division, Geospatial Products Branch (Point of Contact) (2023). TIGER/Line Shapefile, Current, County, Union County, TN, All Roads [Dataset]. https://catalog.data.gov/dataset/tiger-line-shapefile-current-county-union-county-tn-all-roads
    Explore at:
    Dataset updated
    Dec 15, 2023
    Dataset provided by
    United States Census Bureauhttp://census.gov/
    Area covered
    Tennessee, Union County
    Description

    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.

  13. TIGER/Line Shapefile, Current, County, Union County, SC, All Roads

    • catalog.data.gov
    Updated Dec 15, 2023
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    U.S. Department of Commerce, U.S. Census Bureau, Geography Division, Geospatial Products Branch (Point of Contact) (2023). TIGER/Line Shapefile, Current, County, Union County, SC, All Roads [Dataset]. https://catalog.data.gov/dataset/tiger-line-shapefile-current-county-union-county-sc-all-roads
    Explore at:
    Dataset updated
    Dec 15, 2023
    Dataset provided by
    United States Census Bureauhttp://census.gov/
    Area covered
    Union County
    Description

    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.

  14. D

    View of Medical and Dental Enrollment by Union for City & County of SF...

    • data.sfgov.org
    application/rdfxml +5
    Updated May 23, 2024
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    City & County of San Francisco - Health Service System (2024). View of Medical and Dental Enrollment by Union for City & County of SF Employees(CCSF) [Dataset]. https://data.sfgov.org/Health-and-Social-Services/View-of-Medical-and-Dental-Enrollment-by-Union-for/42ay-783m
    Explore at:
    json, xml, csv, tsv, application/rssxml, application/rdfxmlAvailable download formats
    Dataset updated
    May 23, 2024
    Dataset authored and provided by
    City & County of San Francisco - Health Service System
    Area covered
    San Francisco
    Description

    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

  15. p

    Distribution of Students Across Grade Levels in Union School Corporation...

    • publicschoolreview.com
    + more versions
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    Public School Review, Distribution of Students Across Grade Levels in Union School Corporation School District and Average Distribution Per School District in Indiana [Dataset]. https://www.publicschoolreview.com/indiana/union-school-corporation-school-district/1811730-school-district
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    Dataset authored and provided by
    Public School Review
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Indiana, Union School Corporation
    Description

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

  16. p

    Union School Corporation School District

    • publicschoolreview.com
    json, xml
    + more versions
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    Public School Review, Union School Corporation School District [Dataset]. https://www.publicschoolreview.com/indiana/union-school-corporation-school-district/1811730-school-district
    Explore at:
    json, xmlAvailable download formats
    Dataset authored and provided by
    Public School Review
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 1, 1990 - Dec 31, 2025
    Area covered
    Union School Corporation
    Description

    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

  17. F

    Employed Persons in Union County, IA

    • fred.stlouisfed.org
    json
    Updated Jul 2, 2025
    + more versions
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    (2025). Employed Persons in Union County, IA [Dataset]. https://fred.stlouisfed.org/series/LAUCN191750000000005
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Jul 2, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Area covered
    Union County
    Description

    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.

  18. TIGER/Line Shapefile, Current, County, Union Parish, LA, All Roads

    • catalog.data.gov
    Updated Dec 15, 2023
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    U.S. Department of Commerce, U.S. Census Bureau, Geography Division, Geospatial Products Branch (Point of Contact) (2023). TIGER/Line Shapefile, Current, County, Union Parish, LA, All Roads [Dataset]. https://catalog.data.gov/dataset/tiger-line-shapefile-current-county-union-parish-la-all-roads
    Explore at:
    Dataset updated
    Dec 15, 2023
    Dataset provided by
    United States Census Bureauhttp://census.gov/
    Area covered
    Union Parish, Louisiana
    Description

    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.

  19. N

    Union township, Fulton County, Pennsylvania Population Breakdown by Gender...

    • neilsberg.com
    csv, json
    Updated Feb 24, 2025
    + more versions
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    Neilsberg Research (2025). Union township, Fulton County, Pennsylvania Population Breakdown by Gender and Age Dataset: Male and Female Population Distribution Across 18 Age Groups // 2025 Edition [Dataset]. https://www.neilsberg.com/insights/union-township-fulton-county-pa-population-by-gender/
    Explore at:
    json, csvAvailable download formats
    Dataset updated
    Feb 24, 2025
    Dataset authored and provided by
    Neilsberg Research
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Union Township, Fulton County, Pennsylvania
    Variables measured
    Male and Female Population Under 5 Years, Male and Female Population over 85 years, Male and Female Population Between 5 and 9 years, Male and Female Population Between 10 and 14 years, Male and Female Population Between 15 and 19 years, Male and Female Population Between 20 and 24 years, Male and Female Population Between 25 and 29 years, Male and Female Population Between 30 and 34 years, Male and Female Population Between 35 and 39 years, Male and Female Population Between 40 and 44 years, and 8 more
    Measurement technique
    The data presented in this dataset is derived from the latest U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. To measure the three variables, namely (a) Population (Male), (b) Population (Female), and (c) Gender Ratio (Males per 100 Females), we initially analyzed and categorized the data for each of the gender classifications (biological sex) reported by the US Census Bureau across 18 age groups, ranging from under 5 years to 85 years and above. These age groups are described above in the variables section. For further information regarding these estimates, please feel free to reach out to us via email at research@neilsberg.com.
    Dataset funded by
    Neilsberg Research
    Description
    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

  20. N

    Union County, FL annual median income by work experience and sex dataset:...

    • neilsberg.com
    csv, json
    Updated Feb 27, 2025
    + more versions
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    Neilsberg Research (2025). Union County, FL annual median income by work experience and sex dataset: Aged 15+, 2010-2023 (in 2023 inflation-adjusted dollars) // 2025 Edition [Dataset]. https://www.neilsberg.com/insights/union-county-fl-income-by-gender/
    Explore at:
    json, csvAvailable download formats
    Dataset updated
    Feb 27, 2025
    Dataset authored and provided by
    Neilsberg Research
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Florida, Union County
    Variables measured
    Income for Male Population, Income for Female Population, Income for Male Population working full time, Income for Male Population working part time, Income for Female Population working full time, Income for Female Population working part time
    Measurement technique
    The data presented in this dataset is derived from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates. The dataset covers the years 2010 to 2023, representing 14 years of data. To analyze income differences between genders (male and female), we conducted an initial data analysis and categorization. Subsequently, we adjusted these figures for inflation using the Consumer Price Index retroactive series (R-CPI-U-RS) based on current methodologies. For additional information about these estimations, please contact us via email at research@neilsberg.com
    Dataset funded by
    Neilsberg Research
    Description
    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

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Statista (2024). Labor Unions: countries with highest share of workforce unionized worldwide [Dataset]. https://www.statista.com/statistics/1356735/labor-unions-most-unionized-countries-worldwide/
Organization logo

Labor Unions: countries with highest share of workforce unionized worldwide

Explore at:
11 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Sep 2, 2024
Dataset authored and provided by
Statistahttp://statista.com/
Area covered
Worldwide
Description

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.

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