100+ datasets found
  1. Race/Ethnicity Health Workforce Trends

    • healthdata.gov
    • data.ca.gov
    • +3more
    application/rdfxml +5
    Updated Apr 8, 2025
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    chhs.data.ca.gov (2025). Race/Ethnicity Health Workforce Trends [Dataset]. https://healthdata.gov/State/Race-Ethnicity-Health-Workforce-Trends/qn52-3qif
    Explore at:
    json, tsv, csv, xml, application/rdfxml, application/rssxmlAvailable download formats
    Dataset updated
    Apr 8, 2025
    Dataset provided by
    chhs.data.ca.gov
    Description

    The dataset contains estimates for the number of healthcare professionals in 15 different healthcare categories (e.g., Registered Nurse, Dentist, License Clinical Social Worker, etc.) based on completion of license renewal by Race/Ethnicity. There are two timeframes: all current licenses and recent licenses (since 2017). California population estimates are also included to provide a marker for each Race/Ethnicity. Each healthcare professional category can be compared across Race/Ethnicity groups and compared to statewide population estimates, so Race/Ethnicity shortages can be identified for each healthcare professional category. For instance, a notable difference between healthcare professional category and statewide population would indicate either underrepresentation or overrepresentation for that Race/Ethnicity, depending on the direction of the difference.

  2. Annual Population Survey Two-Year Longitudinal Dataset, January 2022 -...

    • beta.ukdataservice.ac.uk
    Updated 2024
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Social Survey Division Office For National Statistics (2024). Annual Population Survey Two-Year Longitudinal Dataset, January 2022 - December 2023 [Dataset]. http://doi.org/10.5255/ukda-sn-9274-2
    Explore at:
    Dataset updated
    2024
    Dataset provided by
    UK Data Servicehttps://ukdataservice.ac.uk/
    DataCitehttps://www.datacite.org/
    Authors
    Social Survey Division Office For National Statistics
    Description

    The Annual Population Survey (APS) is a major survey series, which aims to provide data that can produce reliable estimates at local authority level. Key topics covered in the survey include education, employment, health and ethnicity. The APS comprises key variables from the Labour Force Survey (LFS), all its associated LFS boosts and the APS boost.

    The APS allows for analysis to be carried out on detailed subgroups and below regional level. In recent years (particularly with the sample size of the LFS 5 quarter dataset reducing) there has been some interest in producing a two year APS longitudinal dataset to look at any trends that may occur over a year. The APS Two-Year Longitudinal Datasets, covering 2012/13 onwards, have been deposited as a result of this work. Person- and Household-level APS datasets are also available.

    For further detailed information about methodology, users should consult the Labour Force Survey User Guide, included with the APS documentation.

    Occupation data for 2021 and 2022
    The ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. None of ONS' headline statistics, other than those directly sourced from occupational data, are affected and you can continue to rely on their accuracy. Further information can be found in the ONS article published on 11 July 2023: Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022

    Latest edition information

    For the second edition (August 2024), a revised version of the data was deposited with additional education variables added, including HIQUAL221, HIQUAL222, HIQUL22D1, HIQUL22D2, LEVQUL221 and LEVQUL222.

  3. N

    Hopkinton, New York annual median income by work experience and sex dataset:...

    • neilsberg.com
    csv, json
    Updated Feb 27, 2025
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Neilsberg Research (2025). Hopkinton, New York 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/hopkinton-ny-income-by-gender/
    Explore at:
    csv, jsonAvailable 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
    New York, Hopkinton
    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 Hopkinton town. 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 Hopkinton town, for all workers aged 15 years and older, irrespective of full-time or part-time work, the median income was $36,290 for both males and females.

    This indicates income parity between genders in Hopkinton town, where women and men, regardless of their work hours, earn an equal dollar amount for their efforts, reflecting a balanced income distribution across both sexes.

    - Full-time workers, aged 15 years and older: In Hopkinton town, among full-time, year-round workers aged 15 years and older, males earned a median income of $47,308, while females earned $51,442

    Surprisingly, within the subset of full-time workers, women earn a higher income than men, earning 1.09 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 Hopkinton town median household income by race. You can refer the same here

  4. N

    Port Clinton, PA annual median income by work experience and sex dataset:...

    • neilsberg.com
    csv, json
    Updated Feb 27, 2025
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Neilsberg Research (2025). Port Clinton, PA 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/port-clinton-pa-income-by-gender/
    Explore at:
    csv, jsonAvailable 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
    Pennsylvania, Port Clinton
    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 Port Clinton. 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 Port Clinton, the median income for all workers aged 15 years and older, regardless of work hours, was $33,021 for males and $32,500 for females.

    Based on these incomes, we observe a gender gap percentage of approximately 2%, indicating a significant disparity between the median incomes of males and females in Port Clinton. Women, regardless of work hours, still earn 98 cents to each dollar earned by men, highlighting an ongoing gender-based wage gap.

    - Full-time workers, aged 15 years and older: In Port Clinton, among full-time, year-round workers aged 15 years and older, males earned a median income of $41,500, while females earned $53,571

    Surprisingly, within the subset of full-time workers, women earn a higher income than men, earning 1.29 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 Port Clinton median household income by race. You can refer the same here

  5. T

    United States Labor Force Participation Rate

    • tradingeconomics.com
    • pt.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated Jul 4, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    TRADING ECONOMICS (2025). United States Labor Force Participation Rate [Dataset]. https://tradingeconomics.com/united-states/labor-force-participation-rate
    Explore at:
    json, xml, excel, csvAvailable download formats
    Dataset updated
    Jul 4, 2025
    Dataset authored and provided by
    TRADING ECONOMICS
    License

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

    Time period covered
    Jan 31, 1948 - Jun 30, 2025
    Area covered
    United States
    Description

    Labor Force Participation Rate in the United States decreased to 62.30 percent in June from 62.40 percent in May of 2025. This dataset provides the latest reported value for - United States Labor Force Participation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

  6. Labour force characteristics, monthly, seasonally adjusted and trend-cycle

    • www150.statcan.gc.ca
    • moropho.click
    • +2more
    Updated Jul 11, 2025
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Government of Canada, Statistics Canada (2025). Labour force characteristics, monthly, seasonally adjusted and trend-cycle [Dataset]. http://doi.org/10.25318/1410028701-eng
    Explore at:
    Dataset updated
    Jul 11, 2025
    Dataset provided by
    Statistics Canadahttps://statcan.gc.ca/en
    Area covered
    Canada
    Description

    Number of persons in the labour force (employment and unemployment), unemployment rate, participation rate and employment rate by data type (seasonally adjusted and trend-cycle), gender and age group. Data are also available for the standard error of the estimate, the standard error of the month-to-month change and the standard error of the year-over-year change.

  7. c

    Employee Dataset

    • cubig.ai
    Updated Aug 1, 2024
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    CUBIG (2024). Employee Dataset [Dataset]. https://cubig.ai/store/products/5/employee-dataset
    Explore at:
    Dataset updated
    Aug 1, 2024
    Dataset authored and provided by
    CUBIG
    License

    https://cubig.ai/store/terms-of-servicehttps://cubig.ai/store/terms-of-service

    Measurement technique
    Privacy-preserving data transformation via differential privacy, Synthetic data generation using AI techniques for model training
    Description

    1) Data Introduction • 'Employee dataset: Employee data' aggregates comprehensive information encompassing various aspects of employee data such as training, surveys, performance, recruitment, and attendance. This rich dataset is designed to support in-depth analysis and research in human resources.

    2) Data Utilization (1) Employee dataset: Employee data has characteristics that: • The dataset includes extensive details on employee demographics, job roles, performance ratings, and other HR-related metrics. • It is an invaluable resource for modeling and predicting employee behavior and outcomes based on historical data. (2) Employee dataset: Employee data can be used to: • Human Resources Management: Assists HR professionals in making informed decisions regarding recruitment, training, and employee retention strategies. • Predictive Analysis: Enables companies to forecast trends in employee turnover and performance, aiding in proactive management and planning.

  8. n

    National Survey of Recent College Graduates - Dataset - CKAN

    • nationaldataplatform.org
    Updated Jun 22, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    (2025). National Survey of Recent College Graduates - Dataset - CKAN [Dataset]. https://nationaldataplatform.org/catalog/dataset/national-survey-of-recent-college-graduates
    Explore at:
    Dataset updated
    Jun 22, 2025
    Description

    The National Survey of College Graduates (NSCG) is a biennial dataset sponsored by the U.S. National Science Foundation (NSF) and conducted by the Census Bureau since the 1970s. It captures detailed information on U.S. residents aged under 76 with at least a bachelor’s degree, focusing on demographics, employment, salaries, work activities, and educational backgrounds. The survey emphasizes graduates in science and engineering (S&E) fields, though it includes all degree holders. Its primary purpose is to provide data for policymakers, researchers, and institutions to analyze workforce trends, evaluate educational outcomes, and inform decisions on workforce development and education policy. Key features include longitudinal tracking of degree-field-occupation relationships, detailed labor market insights, and public microdata files dating back to 1993. The dataset is released every two years, with 2023 being the most recent iteration, and includes specialized tools for examining the impact of factors like the COVID-19 pandemic on career trajectories. Unique aspects include its focus on S&E workforce dynamics and granular data on salary ranges and job activities, making it a critical resource for understanding the economic contributions of college-educated professionals.

  9. e

    Annual Population Survey Two-Year Longitudinal Dataset, January 2015 -...

    • b2find.eudat.eu
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Annual Population Survey Two-Year Longitudinal Dataset, January 2015 - December 2016 - Dataset - B2FIND [Dataset]. https://b2find.eudat.eu/dataset/65e43d2e-98b8-534f-b16e-bb8631ac2c9f
    Explore at:
    Description

    Abstract copyright UK Data Service and data collection copyright owner.The Annual Population Survey (APS) is a major survey series, which aims to provide data that can produce reliable estimates at local authority level. Key topics covered in the survey include education, employment, health and ethnicity. The APS comprises key variables from the Labour Force Survey (LFS), all its associated LFS boosts and the APS boost.The APS allows for analysis to be carried out on detailed subgroups and below regional level. In recent years (particularly with the sample size of the LFS 5 quarter dataset reducing) there has been some interest in producing a two year APS longitudinal dataset to look at any trends that may occur over a year. The APS Two-Year Longitudinal Datasets, covering 2012/13 onwards, have been deposited as a result of this work. Person- and Household-level APS datasets are also available. For further detailed information about methodology, users should consult the Labour Force Survey User Guide, included with the APS documentation.Occupation data for 2021 and 2022The ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. None of ONS' headline statistics, other than those directly sourced from occupational data, are affected and you can continue to rely on their accuracy. Further information can be found in the ONS article published on 11 July 2023: Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022 Main Topics:Topics covered include: household composition and relationships, housing tenure, nationality, ethnicity and residential history, employment and training (including government schemes), workplace and location, job hunting, educational background and qualifications. Many of the variables included in the survey are the same as those in the LFS.

  10. Sample HR Dataset - High‑Growth SaaS Company

    • kaggle.com
    Updated Apr 18, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Vic Ako (2025). Sample HR Dataset - High‑Growth SaaS Company [Dataset]. https://www.kaggle.com/datasets/vicako/sample-hr-dataset-highgrowth-saas-simulation
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Apr 18, 2025
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Vic Ako
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Description

    High‑Growth SaaS Workforce Simulation (4‑Year Synthetic HRIS)

    Data Overview

    This synthetic dataset models a high‑growth SaaS startup workforce over 48 months (May 2021–April 2025), starting with 300 employees and growing via a ramped hiring process. It includes detailed employee master records, monthly “snapshot” views, and a transactional leave‑request log. The goal is to provide a realistic sandbox for teaching people‑analytics techniques—headcount and tenure trends, compensation modeling, performance/regression analysis, survival (attrition) modeling, and leave‑impact studies.

    Data Files

    FileRowsDescription
    employees_updated.csv~793 rowsMaster data for each employee (demographics, hire/exit, DEI, org structure, career & development)
    snapshots_updated.csv18,360 rowsMonthly “as‑of” snapshots (one record per active employee per month) with tenure, salary, performance, engagement, risk, FTE, etc.
    leave_requests.csv902 rowsLeave‑request transactions (type, request date, approval status, absence code)

    Key Features & Schema

    1. Master Employee Data (employees_updated.csv)

    ColumnTypeDescription
    employee_idintUnique numeric ID
    first_name, last_namestringName components
    gendercategoricalMale / Female / Non‑binary
    pronounscategoricalhe/him, she/her, they/them, etc.
    age, birth_dateint, dateAge at hire, derived birth date
    DEI
    ethnicity, veteran_status, disability_statuscategorical, boolSelf‑reported demographics and compliance flags
    Org Structure
    department, business_unit, cost_centercategoricalDept‑level org assignments
    fte, exemption_statusfloat, categoricalFTE ratio (1.0, 0.8, 0.5), exempt vs non‑exempt
    Employment Dates
    hire_date, termination_date, employment_statusdate, categoricalHire and exit info
    Compensation
    base_salary, bonus_eligible, bonus_pct, equity_grant, equity_pctnumeric, boolPay components
    Career & Development
    job_level, job_title, training_count, last_training_date, promotion_count, last_promotion_date, high_potential_flag, succession_plan_status, aihr_certifiedmixedPromotion/training metrics and talent‑planning flags

    2. Monthly Snapshots (snapshots_updated.csv)

    In addition to master‑data columns carried forward, each snapshot includes:

    ColumnTypeDescription
    snapshot_datedateLast‑day‑of‑month snapshot
    tenure_monthsintMonths since hire
    Dynamic Metrics
    performance_ratingfloat1–5 scale (random‑walk over time)
    current_salaryintBase salary grown by annual merit increase (~3.5% ± 1%)
    engagement_scorefloat0–100 proxy (scaled from performance + noise)
    `ri...
  11. N

    Tipton, KS annual median income by work experience and sex dataset: Aged...

    • neilsberg.com
    csv, json
    Updated Feb 27, 2025
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Neilsberg Research (2025). Tipton, KS 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/research/datasets/a53b670c-f4ce-11ef-8577-3860777c1fe6/
    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
    Kansas, Tipton
    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 Tipton. 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 Tipton, for all workers aged 15 years and older, irrespective of full-time or part-time work, the median income was $39,167 for both males and females.

    This indicates income parity between genders in Tipton, where women and men, regardless of their work hours, earn an equal dollar amount for their efforts, reflecting a balanced income distribution across both sexes.

    - Full-time workers, aged 15 years and older: In Tipton, among full-time, year-round workers aged 15 years and older, males earned a median income of $39,583, while females earned $42,500

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

  12. d

    LinkedIn Job Postings Data – U.S Skills & Employer Trends • Enriched...

    • datarade.ai
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Canaria Inc., LinkedIn Job Postings Data – U.S Skills & Employer Trends • Enriched LinkedIn Job Postings Data Matchable with LinkedIn Company Data & Google Maps [Dataset]. https://datarade.ai/data-products/canaria-s-linkedin-job-posting-analytics-ai-llm-enhanced-i-canaria-inc
    Explore at:
    .bin, .json, .xml, .csv, .xls, .sql, .txtAvailable download formats
    Dataset authored and provided by
    Canaria Inc.
    Area covered
    United States
    Description

    LinkedIn Job Postings Data - Comprehensive Professional Intelligence for HR Strategy & Market Research

    LinkedIn Job Postings Data represents the most comprehensive professional intelligence dataset available, delivering structured insights across millions of LinkedIn job postings, LinkedIn job listings, and LinkedIn career opportunities. Canaria's enriched LinkedIn Job Postings Data transforms raw LinkedIn job market information into actionable business intelligence—normalized, deduplicated, and enhanced with AI-powered enrichment for deep workforce analytics, talent acquisition, and market research.

    This premium LinkedIn job postings dataset is engineered to help HR professionals, recruiters, analysts, and business strategists answer mission-critical questions: • What LinkedIn job opportunities are available in target companies? • Which skills are trending in LinkedIn job postings across specific industries? • How are companies advertising their LinkedIn career opportunities? • What are the salary expectations across different LinkedIn job listings and regions?

    With real-time updates and comprehensive LinkedIn job posting enrichment, our data provides unparalleled visibility into LinkedIn job market trends, hiring patterns, and workforce dynamics.

    Use Cases: What This LinkedIn Job Postings Data Solves

    Our dataset transforms LinkedIn job advertisements, market information, and career listings into structured, analyzable insights—powering everything from talent acquisition to competitive intelligence and job market research.

    Talent Acquisition & LinkedIn Recruiting Intelligence • LinkedIn job market mapping • LinkedIn career opportunity intelligence • LinkedIn job posting competitive analysis • LinkedIn job skills gap identification

    HR Strategy & Workforce Analytics • Organizational network analysis • Employee mobility tracking • Compensation benchmarking • Diversity & inclusion analytics • Workforce planning intelligence • Skills evolution monitoring

    Market Research & Competitive Intelligence • Company growth analysis • Industry trend identification • Competitive talent mapping • Market entry intelligence • Partnership & business development • Investment due diligence

    LinkedIn Job Market Research & Economic Analysis • Regional LinkedIn job analysis • LinkedIn job skills demand forecasting • LinkedIn job economic impact assessment • LinkedIn job education-industry alignment • LinkedIn remote job trend analysis • LinkedIn career development ROI

    What Makes This LinkedIn Job Postings Data Unique

    AI-Enhanced LinkedIn Job Intelligence • LinkedIn job posting enrichment with advanced NLP • LinkedIn job seniority classification • LinkedIn job industry expertise mapping • LinkedIn job career progression modeling

    Comprehensive LinkedIn Job Market Intelligence • Real-time LinkedIn job postings with salary, requirements, and company insights • LinkedIn recruiting activity tracking • LinkedIn job application analytics • LinkedIn job skills demand analysis • LinkedIn compensation intelligence

    Company & Organizational Intelligence • Company growth indicators • Cultural & values intelligence • Competitive positioning

    LinkedIn Job Data Quality & Normalization • Advanced LinkedIn job deduplication • LinkedIn job skills taxonomy standardization • LinkedIn job geographic normalization • LinkedIn job company matching • LinkedIn job education standardization

    Who Uses Canaria's LinkedIn Data

    HR & Talent Acquisition Teams • Optimize recruiting pipelines • Benchmark compensation • Identify talent pools • Develop data-driven hiring strategies

    Market Research & Intelligence Analysts • Track industry trends • Build competitive intelligence models • Analyze workforce dynamics

    HR Technology & Analytics Platforms • Power recruiting tools and analytics solutions • Fuel compensation engines and dashboards

    Academic & Economic Researchers • Study labor market dynamics • Analyze career mobility trends • Research professional development

    Government & Policy Organizations • Evaluate workforce development programs • Monitor skills gaps • Inform economic initiatives

    Summary

    Canaria's LinkedIn Job Postings Data delivers the most comprehensive LinkedIn job market intelligence available. It combines job posting insights, recruiting intelligence, and organizational data in one unified dataset. With AI-enhanced enrichment, real-time updates, and enterprise-grade data quality, it supports advanced HR analytics, talent acquisition, job market research, and competitive intelligence.

    About Canaria Inc. Canaria Inc. is a leader in alternative data, specializing in job market intelligence, LinkedIn company data, Glassdoor salary analytics, and Google Maps location insights. We deliver clean, structured, and enriched datasets at scale using proprietary data scraping pipelines and advanced AI/LLM-based modeling, all backed by human validation. Our platform also includes Google Maps data, providing verified business location intelligen...

  13. e

    Annual Population Survey Two-Year Longitudinal Dataset, January 2016 -...

    • b2find.eudat.eu
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Annual Population Survey Two-Year Longitudinal Dataset, January 2016 - December 2017 - Dataset - B2FIND [Dataset]. https://b2find.eudat.eu/dataset/05c0efa9-6464-58a0-9e72-a9ff2bf1c1f7
    Explore at:
    Description

    Abstract copyright UK Data Service and data collection copyright owner.The Annual Population Survey (APS) is a major survey series, which aims to provide data that can produce reliable estimates at local authority level. Key topics covered in the survey include education, employment, health and ethnicity. The APS comprises key variables from the Labour Force Survey (LFS), all its associated LFS boosts and the APS boost.The APS allows for analysis to be carried out on detailed subgroups and below regional level. In recent years (particularly with the sample size of the LFS 5 quarter dataset reducing) there has been some interest in producing a two year APS longitudinal dataset to look at any trends that may occur over a year. The APS Two-Year Longitudinal Datasets, covering 2012/13 onwards, have been deposited as a result of this work. Person- and Household-level APS datasets are also available. For further detailed information about methodology, users should consult the Labour Force Survey User Guide, included with the APS documentation.Occupation data for 2021 and 2022The ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. None of ONS' headline statistics, other than those directly sourced from occupational data, are affected and you can continue to rely on their accuracy. Further information can be found in the ONS article published on 11 July 2023: Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022 Main Topics:Topics covered include: household composition and relationships, housing tenure, nationality, ethnicity and residential history, employment and training (including government schemes), workplace and location, job hunting, educational background and qualifications. Many of the variables included in the survey are the same as those in the LFS.

  14. N

    Dugger, IN annual median income by work experience and sex dataset: Aged...

    • neilsberg.com
    csv, json
    Updated Feb 27, 2025
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Neilsberg Research (2025). Dugger, IN 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/dugger-in-income-by-gender/
    Explore at:
    csv, jsonAvailable 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
    Dugger
    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 Dugger. 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 Dugger, for all workers aged 15 years and older, irrespective of full-time or part-time work, the median income was $34,091 for both males and females.

    This indicates income parity between genders in Dugger, where women and men, regardless of their work hours, earn an equal dollar amount for their efforts, reflecting a balanced income distribution across both sexes.

    - Full-time workers, aged 15 years and older: In Dugger, among full-time, year-round workers aged 15 years and older, males earned a median income of $50,833, while females earned $58,049

    Surprisingly, within the subset of full-time workers, women earn a higher income than men, earning 1.14 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 Dugger median household income by race. You can refer the same here

  15. e

    Annual Population Survey Two-Year Longitudinal Dataset, January 2019 -...

    • b2find.eudat.eu
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Annual Population Survey Two-Year Longitudinal Dataset, January 2019 - December 2020 - Dataset - B2FIND [Dataset]. https://b2find.eudat.eu/dataset/60883b17-ad85-5c34-ba0d-8a7990a99c4e
    Explore at:
    Description

    Abstract copyright UK Data Service and data collection copyright owner.The Annual Population Survey (APS) is a major survey series, which aims to provide data that can produce reliable estimates at local authority level. Key topics covered in the survey include education, employment, health and ethnicity. The APS comprises key variables from the Labour Force Survey (LFS), all its associated LFS boosts and the APS boost.The APS allows for analysis to be carried out on detailed subgroups and below regional level. In recent years (particularly with the sample size of the LFS 5 quarter dataset reducing) there has been some interest in producing a two year APS longitudinal dataset to look at any trends that may occur over a year. The APS Two-Year Longitudinal Datasets, covering 2012/13 onwards, have been deposited as a result of this work. Person- and Household-level APS datasets are also available. For further detailed information about methodology, users should consult the Labour Force Survey User Guide, included with the APS documentation.Occupation data for 2021 and 2022The ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. None of ONS' headline statistics, other than those directly sourced from occupational data, are affected and you can continue to rely on their accuracy. Further information can be found in the ONS article published on 11 July 2023: Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022 Main Topics:Topics covered include: household composition and relationships, housing tenure, nationality, ethnicity and residential history, employment and training (including government schemes), workplace and location, job hunting, educational background and qualifications. Many of the variables included in the survey are the same as those in the LFS.

  16. Current Employment Statistics (CES)

    • data.ca.gov
    • catalog.data.gov
    csv
    Updated Jul 18, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    California Employment Development Department (2025). Current Employment Statistics (CES) [Dataset]. https://data.ca.gov/dataset/current-employment-statistics-ces-2
    Explore at:
    csv(72314038), csv(70602263), csv(69329819)Available download formats
    Dataset updated
    Jul 18, 2025
    Dataset provided by
    Employment Development Departmenthttp://www.edd.ca.gov/
    Authors
    California Employment Development Department
    License

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

    Description

    The Current Employment Statistics (CES) program is a Federal-State cooperative effort in which monthly surveys are conducted to provide estimates of employment, hours, and earnings based on payroll records of business establishments. The CES survey is based on approximately 119,000 businesses and government agencies representing approximately 629,000 individual worksites throughout the United States.

    CES data reflect the number of nonfarm, payroll jobs. It includes the total number of persons on establishment payrolls, employed full- or part-time, who received pay (whether they worked or not) for any part of the pay period that includes the 12th day of the month. Temporary and intermittent employees are included, as are any employees who are on paid sick leave or on paid holiday. Persons on the payroll of more than one establishment are counted in each establishment. CES data excludes proprietors, self-employed, unpaid family or volunteer workers, farm workers, and household workers. Government employment covers only civilian employees; it excludes uniformed members of the armed services.

    The Bureau of Labor Statistics (BLS) of the U.S. Department of Labor is responsible for the concepts, definitions, technical procedures, validation, and publication of the estimates that State workforce agencies prepare under agreement with BLS.

  17. a

    Employment Services Program Data by Local Boards

    • hub.arcgis.com
    • communautaire-esrica-apps.hub.arcgis.com
    Updated Jan 23, 2017
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    EO_Analytics (2017). Employment Services Program Data by Local Boards [Dataset]. https://hub.arcgis.com/maps/a1a2149aa4eb453bbcaaa8436feb117c
    Explore at:
    Dataset updated
    Jan 23, 2017
    Dataset authored and provided by
    EO_Analytics
    Area covered
    Description

    This map presents the full data available on the MLTSD GeoHub, and maps several of the key variables reflected by the Employment Services Program of ETD.Employment Services are a suite of services delivered to the public to help Ontarians find sustainable employment. The services are delivered by third-party service providers at service delivery sites (SDS) across Ontario on behalf of the Ministry of Labour, Training and Skills Development (MLTSD). The services are tailored to meet the individual needs of each client and can be provided one-on-one or in a group format. Employment Services fall into two broad categories: unassisted and assisted services.

    Unassisted services include the following components:resources and information on all aspects of employment including detailed facts on the local labour marketresources on how to conduct a job search.assistance in registering for additional schoolinghelp with career planningreference to other Employment and government programs.

    Unassisted services are available to all Ontarians without reference to eligibility criteria. These unassisted services can be delivered through structured orientation or information sessions (on or off site), e-learning sessions, or one-to-one sessions up to two days in duration. Employers can also use unassisted services to access information on post-employment opportunities and supports available for recruitment and workplace training.

    The second category is assisted services, and it includes the following components:assistance with the job search (including individualized assistance in career goal setting, skills assessment, and interview preparation) job matching, placement and incentives (which match client skills and interested with employment opportunities, and include placement into employment, on-the-job training opportunities, and incentives to employers to hire ES clients), and job training/retention (which supports longer-term attachment to or advancement in the labour market or completion of training)For every assisted services client a service plan is maintained by the service provider, which gives details on the types of assisted services the client has accessed. To be eligible for assisted services, clients must be unemployed (defined as working less than twenty hours a week) and not participating in full-time education or training. Clients are also assessed on a number of suitability indicators covering economic, social and other barriers to employment, and service providers are to prioritize serving those clients with multiple suitability indicators.

    About This Dataset

    This dataset contains data on ES clients for each of the twenty-six Local Board (LB) areas in Ontario for the 2015/16 fiscal year, based on data provided to Local Boards and Local Employment Planning Councils (LEPC) in June 2016 (see below for details on Local Boards). This includes all assisted services clients whose service plan was closed in the 2015/16 fiscal year and all unassisted services clients who accessed unassisted services in the 2015/16 fiscal year. These clients have been distributed across Local Board areas based on the address of each client’s service delivery site, not the client’s home address. Note that clients who had multiple service plans close in the 2015/16 fiscal year (i.e. more than one distinct period during which the client was accessing assisted services) will be counted multiple times in this dataset (once for each closed service plan). Assisted services clients who also accessed unassisted services either before or after accessing assisted services would also be included in the count of unassisted clients (in addition to their assisted services data).

    Demographic data on ES assisted services clients, including a client’s suitability indicators and barriers to employment, are collected by the service provider when a client registers for ES (i.e. at intake). Outcomes data on ES assisted services clients is collected through surveys at exit (i.e. when the client has completed accessing ES services and the client’s service plan is closed) and at three, six, and twelve months after exit. As demographic and outcomes data is only collected for assisted services clients, all fields in this dataset contain data only on assisted services clients except for the ‘Number of Clients – Unassisted R&I Clients’ field.

    Note that ES is the gateway for other Employment Ontario programs and services; the majority of Second Career (SC) clients, some apprentices, and some Literacy and Basic Skills (LBS) clients have also accessed ES. It is standard procedure for SC, LBS and apprenticeship client and outcome data to be entered as ES data if the program is part of ES service plan. However, for this dataset, SC client and outcomes data has been separated from ES, which as a result lowers the client and outcome counts for ES.

    About Local Boards

    Local Boards are independent not-for-profit corporations sponsored by the Ministry of Labour, Training and Skills Development to improve the condition of the labour market in their specified region. These organizations are led by business and labour representatives, and include representation from constituencies including educators, trainers, women, Francophones, persons with disabilities, visible minorities, youth, Indigenous community members, and others. For the 2015/16 fiscal year there were twenty-six Local Boards, which collectively covered all of the province of Ontario.

    The primary role of Local Boards is to help improve the conditions of their local labour market by:engaging communities in a locally-driven process to identify and respond to the key trends, opportunities and priorities that prevail in their local labour markets;facilitating a local planning process where community organizations and institutions agree to initiate and/or implement joint actions to address local labour market issues of common interest; creating opportunities for partnership development activities and projects that respond to more complex and/or pressing local labour market challenges; and organizing events and undertaking activities that promote the importance of education, training and skills upgrading to youth, parents, employers, employed and unemployed workers, and the public in general.

    In December 2015, the government of Ontario launched an eighteen-month Local Employment Planning Council pilot program, which established LEPCs in eight regions in the province formerly covered by Local Boards. LEPCs expand on the activities of existing Local Boards, leveraging additional resources and a stronger, more integrated approach to local planning and workforce development to fund community-based projects that support innovative approaches to local labour market issues, provide more accurate and detailed labour market information, and develop detailed knowledge of local service delivery beyond Employment Ontario (EO).

    Eight existing Local Boards were awarded LEPC contracts that were effective as of January 1st, 2016. As such, from January 1st, 2016 to March 31st, 2016, these eight Local Boards were simultaneously Local Employment Planning Councils. The eight Local Boards awarded contracts were:Durham Workforce Authority Peel-Halton Workforce Development GroupWorkforce Development Board - Peterborough, Kawartha Lakes, Northumberland, HaliburtonOttawa Integrated Local Labour Market PlanningFar Northeast Training BoardNorth Superior Workforce Planning Board Elgin Middlesex Oxford Workforce Planning & Development BoardWorkforce Windsor-Essex

    MLTSD has provided Local Boards and LEPCs with demographic and outcome data for clients of Employment Ontario (EO) programs delivered by service providers across the province on an annual basis since June 2013. This was done to assist Local Boards in understanding local labour market conditions. These datasets may be used to facilitate and inform evidence-based discussions about local service issues – gaps, overlaps and under-served populations - with EO service providers and other organizations as appropriate to the local context.

    Data on the following EO programs for the 2015/16 fiscal year was made available to Local Boards and LEPCs in June 2016:Employment Services (ES)Literacy and Basic Skills (LBS) Second Career (SC) Apprenticeship

    This dataset contains the 2015/16 ES data that was sent to Local Boards and LEPCs. Datasets covering past fiscal years will be released in the future.

    Notes and Definitions

    NAICS – The North American Industry Classification System (NAICS) is an industry classification system developed by the statistical agencies of Canada, the United States, and Mexico against the backdrop of the North American Free Trade Agreement. It is a comprehensive system that encompasses all economic activities in a hierarchical structure. At the highest level, it divides economic activity into twenty sectors, each of which has a unique two-digit identifier. These sectors are further divided into subsectors (three-digit codes), industry groups (four-digit codes), and industries (five-digit codes). This dataset uses two-digit NAICS codes from the 2007 edition to identify the sector of the economy an Employment Services client is employed in prior to and after participation in ES.

    NOC – The National Organizational Classification (NOC) is an occupational classification system developed by Statistics Canada and Human Resources and Skills Development Canada to provide a standard lexicon to describe and group occupations in Canada primarily on the basis of the work being performed in the occupation. It is a comprehensive system that encompasses all occupations in Canada in a hierarchical structure. At the highest level are ten broad occupational categories, each of which has a unique one-digit identifier. These broad occupational categories are further divided into forty major groups (two-digit codes), 140 minor groups

  18. e

    Second Career Program Data by Local Boards

    • eo-geohub.com
    • hub.arcgis.com
    • +1more
    Updated Dec 23, 2016
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    EO_Analytics (2016). Second Career Program Data by Local Boards [Dataset]. https://www.eo-geohub.com/maps/ef1421f0586440c7ad931ed2bd9e6143
    Explore at:
    Dataset updated
    Dec 23, 2016
    Dataset authored and provided by
    EO_Analytics
    Area covered
    Description

    This map presents the full data available on the MLTSD GeoHub, and maps several of the key variables reflected by the Second Career Program of ETD.The Second Career program provides training to unemployed or laid-off individuals to help them find employment in high demand occupations in Ontario. The intention of the SC program is to return individuals to employment by the most cost effective path. Second Career provides up to $28,000 to assist laid-off workers with training-related costs such as tuition, books, transportation, and basic living expenses, based on individual need. Additional allowances may be available for people with disabilities, and for clients needing help with the costs of dependent care, living away from home and literacy and basic skills upgrading, also based on individual need. People with disabilities may also be given extensions on training and upgrading durations, to meet their specific needs. Clients may be required to contribute to their skills training, based on the client’s total annual gross household income and the number of household members.About This DatasetThis dataset contains data on SC clients for each of the twenty-six Local Board (LB) areas in Ontario for the 2015/16 fiscal year, based on data provided to Local Boards and Local Employment Planning Councils (LEPC) in June 2016 (see below for details on Local Boards). These clients have been distributed across Local Board areas based on the client’s home address, not the address of their training institution(s).Different variables in this dataset cover different groups of Second Career clients, as follows:Demographic and skills training variables are composed of all SC clients that started in 2015/16.At exit outcome variables are composed of all SC clients that completed their program in 2015/16.12-month outcome variables are composed of all SC clients that completed a 12-month survey in 2015/16.The specific variables that fall into each of the above categories are detailed in the Technical Dictionary. As a result of these differences, not all variables in this dataset are comparable to the other variables in this dataset; for example, the outcomes at exit data is not the outcomes for the clients described by the demographic variables.About Local BoardsLocal Boards are independent not-for-profit corporations sponsored by the Ministry of Labour, Training and Skills Development to improve the condition of the labour market in their specified region. These organizations are led by business and labour representatives, and include representation from constituencies including educators, trainers, women, Francophones, persons with disabilities, visible minorities, youth, Indigenous community members, and others. For the 2015/16 fiscal year there were twenty-six Local Boards, which collectively covered all of the province of Ontario. The primary role of Local Boards is to help improve the conditions of their local labour market by:engaging communities in a locally-driven process to identify and respond to the key trends, opportunities and priorities that prevail in their local labour markets;facilitating a local planning process where community organizations and institutions agree to initiate and/or implement joint actions to address local labour market issues of common interest;creating opportunities for partnership development activities and projects that respond to more complex and/or pressing local labour market challenges; andorganizing events and undertaking activities that promote the importance of education, training and skills upgrading to youth, parents, employers, employed and unemployed workers, and the public in general.In December 2015, the government of Ontario launched an eighteen-month Local Employment Planning Council pilot program, which established LEPCs in eight regions in the province formerly covered by Local Boards. LEPCs expand on the activities of existing Local Boards, leveraging additional resources and a stronger, more integrated approach to local planning and workforce development to fund community-based projects that support innovative approaches to local labour market issues, provide more accurate and detailed labour market information, and develop detailed knowledge of local service delivery beyond Employment Ontario (EO).Eight existing Local Boards were awarded LEPC contracts that were effective as of January 1st, 2016. As such, from January 1st, 2016 to March 31st, 2016, these eight Local Boards were simultaneously Local Employment Planning Councils. The eight Local Boards awarded contracts were:Durham Workforce AuthorityPeel-Halton Workforce Development GroupWorkforce Development Board - Peterborough, Kawartha Lakes, Northumberland, HaliburtonOttawa Integrated Local Labour Market PlanningFar Northeast Training BoardNorth Superior Workforce Planning BoardElgin Middlesex Oxford Workforce Planning & Development BoardWorkforce Windsor-EssexMLTSD has provided Local Boards and LEPCs with demographic and outcome data for clients of Employment Ontario (EO) programs delivered by service providers across the province on an annual basis since June 2013. This was done to assist Local Boards in understanding local labour market conditions. These datasets may be used to facilitate and inform evidence-based discussions about local service issues – gaps, overlaps and under-served populations - with EO service providers and other organizations as appropriate to the local context.Data on the following EO programs for the 2015/16 fiscal year was made available to Local Boards and LEPCs in June 2016: Employment Services (ES)Literacy and Basic Skills (LBS) Second Career (SC) ApprenticeshipThis dataset contains the 2015/16 SC data that was sent to Local Boards and LEPCs. Datasets covering past fiscal years will be released in the future.Terms and Definitions

    NOC – The National Organizational Classification (NOC) is an occupational classification system developed by Statistics Canada and Human Resources and Skills Development Canada to provide a standard lexicon to describe and group occupations in Canada primarily on the basis of the work being performed in the occupation. It is a comprehensive system that encompasses all occupations in Canada in a hierarchical structure. At the highest level are ten broad occupational categories, each of which has a unique one-digit identifier. These broad occupational categories are further divided into forty major groups (two-digit codes), 140 minor groups (three-digit codes), and 500 unit groups (four-digit codes). This dataset uses four-digit NOC codes from the 2011 edition to identify the training programs of Second Career clients.Notes

    Data reporting on 5 individuals or less has been suppressed to protect the privacy of those individuals.Data published: Feb 1, 2017Publisher: Ministry of Labour, Training and Skills Development (MLTSD)Update frequency: Yearly Geographical coverage: Ontario

  19. N

    Neshkoro Town, Wisconsin annual median income by work experience and sex...

    • neilsberg.com
    csv, json
    Updated Feb 27, 2025
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Neilsberg Research (2025). Neshkoro Town, Wisconsin 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/neshkoro-town-wi-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
    Wisconsin, Neshkoro, Neshkoro
    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 Neshkoro town. 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 Neshkoro town, the median income for all workers aged 15 years and older, regardless of work hours, was $29,276 for males and $28,672 for females.

    Based on these incomes, we observe a gender gap percentage of approximately 2%, indicating a significant disparity between the median incomes of males and females in Neshkoro town. Women, regardless of work hours, still earn 98 cents to each dollar earned by men, highlighting an ongoing gender-based wage gap.

    - Full-time workers, aged 15 years and older: In Neshkoro town, among full-time, year-round workers aged 15 years and older, males earned a median income of $40,250, while females earned $49,250

    Surprisingly, within the subset of full-time workers, women earn a higher income than men, earning 1.22 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 Neshkoro town median household income by race. You can refer the same here

  20. p

    San Diego Workforce Innovation High School

    • publicschoolreview.com
    json, xml
    Updated Jul 1, 2021
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Public School Review (2021). San Diego Workforce Innovation High School [Dataset]. https://www.publicschoolreview.com/san-diego-workforce-innovation-high-school-profile/92004
    Explore at:
    json, xmlAvailable download formats
    Dataset updated
    Jul 1, 2021
    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, 2017 - Dec 31, 2025
    Description

    Historical Dataset of San Diego Workforce Innovation High School is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2017-2023),Total Classroom Teachers Trends Over Years (2017-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (2017-2023),American Indian Student Percentage Comparison Over Years (2019-2023),Asian Student Percentage Comparison Over Years (2017-2023),Hispanic Student Percentage Comparison Over Years (2017-2023),Black Student Percentage Comparison Over Years (2017-2023),White Student Percentage Comparison Over Years (2019-2023),Two or More Races Student Percentage Comparison Over Years (2017-2023),Diversity Score Comparison Over Years (2017-2023),Free Lunch Eligibility Comparison Over Years (2017-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2019-2023),Reading and Language Arts Proficiency Comparison Over Years (2017-2018),Math Proficiency Comparison Over Years (2017-2018),Overall School Rank Trends Over Years (2017-2018),Graduation Rate Comparison Over Years (2017-2018)

Share
FacebookFacebook
TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
chhs.data.ca.gov (2025). Race/Ethnicity Health Workforce Trends [Dataset]. https://healthdata.gov/State/Race-Ethnicity-Health-Workforce-Trends/qn52-3qif
Organization logo

Race/Ethnicity Health Workforce Trends

Explore at:
json, tsv, csv, xml, application/rdfxml, application/rssxmlAvailable download formats
Dataset updated
Apr 8, 2025
Dataset provided by
chhs.data.ca.gov
Description

The dataset contains estimates for the number of healthcare professionals in 15 different healthcare categories (e.g., Registered Nurse, Dentist, License Clinical Social Worker, etc.) based on completion of license renewal by Race/Ethnicity. There are two timeframes: all current licenses and recent licenses (since 2017). California population estimates are also included to provide a marker for each Race/Ethnicity. Each healthcare professional category can be compared across Race/Ethnicity groups and compared to statewide population estimates, so Race/Ethnicity shortages can be identified for each healthcare professional category. For instance, a notable difference between healthcare professional category and statewide population would indicate either underrepresentation or overrepresentation for that Race/Ethnicity, depending on the direction of the difference.

Search
Clear search
Close search
Google apps
Main menu