The resources in this dataset contain demographic information for the Oklahoma state government workforce. The resources present data from the current fiscal year along with demographic trends over time. The data can be used for workforce planning purposes.

The dataset highlights key OPS workforce demographics extracted from the OPS payroll reporting system (WIN), including: * OPS size * Age and tenure * Annual sick leave credit usage * OPS salaries * OPS compensation data by gender A data dictionary is included to define all workforce demographics, metrics and limitations. This data has been released due to the demand expressed through a public vote to determine which datasets the Government of Ontario should publish. This was the fourth most voted on dataset out of a pool of approximately 1000 entries. The Data in this report is as of March 31, 2024, unless otherwise indicated. *[WIN]: Workforce Information Network *[OPS]: Ontario Public Service

Emergency medical services (EMS) workforce demographics in the United States do not reflect the diversity of the population served. Despite some efforts by professional organizations to create a more representative workforce, little has changed in the last decade. This scoping review aims to summarize existing literature on the demographic composition, recruitment, retention, and workplace experience of underrepresented groups within EMS. Peer-reviewed studies were obtained from a search of PubMed, CINAHL, Web of Science, ProQuest Thesis and Dissertations, and non-peer-reviewed (“gray”) literature from 1960 to present. Abstracts and included full-text articles were screened by two independent reviewers trained on inclusion/exclusion criteria. Studies were included if they pertained to the demographics, training, hiring, retention, promotion, compensation, or workplace experience of underrepresented groups in United States EMS by race, ethnicity, sexual orientation, or gender. Studies of non-EMS fire department activities were excluded. Disputes were resolved by two authors. A single reviewer screened the gray literature. Data extraction was performed using a standardized electronic form. Results were summarized qualitatively. We identified 87 relevant full-text articles from the peer-reviewed literature and 250 items of gray literature. Primary themes emerging from peer-reviewed literature included workplace experience (n = 48), demographics (n = 12), workforce entry and exit (n = 8), education and testing (n = 7), compensation and benefits (n = 5), and leadership, mentorship, and promotion (n = 4). Most articles focused on sex/gender comparisons (65/87, 75%), followed by race/ethnicity comparisons (42/87, 48%). Few articles examined sexual orientation (3/87, 3%). One study focused on telecommunicators and three included EMS physicians. Most studies (n = 60, 69%) were published in the last decade. In the gray literature, media articles (216/250, 86%) demonstrated significant industry discourse surrounding these primary themes. Existing EMS workforce research demonstrates continued underrepresentation of women and nonwhite personnel. Additionally, these studies raise concerns for pervasive negative workplace experiences including sexual harassment and factors that negatively affect recruitment and retention, including bias in candidate testing, a gender pay gap, and unequal promotion opportunities. Additional research is needed to elucidate recruitment and retention program efficacy, the demographic composition of EMS leadership, and the prevalence of racial harassment and discrimination in this workforce.

This data asset was created in response to House Report 117-401, which stated, "The Committee directs the USAID Administrator, in consultation with the Director of the Office of Personnel Management and the Director of the Office of Management and Budget, to submit a report to the appropriate congressional committees, not later than 180 days after enactment of this Act, on USAID's workforce data that includes disaggregated demographic data and other information regarding the diversity of the workforce of USAID. Such report shall include the following data to the maximum extent practicable and permissible by law: 1) demographic data of USAID workforce disaggregated by grade or grade-equivalent; 2) assessment of agency compliance with the Equal Employment Opportunity Commission Management Directive 715; and 3) data on the overall number of individuals who are part of the workforce, including all U.S. Direct Hires, personnel under personal services contracts, and Locally Employed staff at USAID. The report shall also be published on a publicly available website of USAID in a searchable database format." This data asset fulfills the final part of this requirement, to publish the data in a searchable database format. The data are compiled from USAID's 2021 MD-715 report, available at https://www.usaid.gov/who-we-are/organization/independent-offices/office-civil-rights/md-715-reports. The original data source is the system National Finance Center Insight owned by the Treasury Department. This dataset reports demographic data for the USAID workforce for fiscal year 2021.

In 2023, China's labor force amounted to approximately 772.2 million people. The labor force in China indicated a general decreasing trend in recent years. As both the size of the population in working age and the share of the population participating in the labor market are declining, this downward trend will most likely persist in the foreseeable future. A country’s labor force is defined as the total number of employable people and incorporates both the employed and the unemployed population. Population challenges for China One of the reasons for the shrinking labor force is the Chinese one-child policy, which had been in effect for nearly 40 years, until it was revoked in 2016. The controversial policy was intended to improve people’s living standards and optimize resource distribution through controlling the size of China’s expanding population. Nonetheless, the policy also led to negative impacts on the labor market, pension system and other societal aspects. Today, China is becoming an aging society. The increase of elderly people and the lack of young people will become a big challenge for China in this century. Employment in China Despite the slowing down of economic growth, China’s unemployment rate has sustained a relatively low rate. Complete production chains and a well-educated labor force make China’s labor market one of the most attractive in the world. Working conditions and salaries in China have also improved significantly over the past years. Due to China’s leading position in terms of talent in the technology industry, the country is now attracting investment from some of the world’s leading companies in the high-tech sector.

Data updated quarterly.Data Attributes and Definitions -- Department: The department the employee works in.- Department ID: The numeric identifier for the department (typically 4 digits).- Job: The name for the job assigned to the employee.- Category: Grouping of employees in similar jobs/leadership roles.- Sub Category: Secondary grouping of employees within a category.- Race/Ethnicity: The race/ethnicity category which the employee identifies with (self-identified).- Gender: Designates the employee's gender (self-identified).- Age: The chronological number (age) assigned to the employee based on date of birth.- Age Group: Grouping of employees having approximately the same age or age range.- Original Hire Date: Date upon which the employee was originally hired.- Last Hire Date: Date upon which an employee was hired; may be a rehire date.- Pay Class: Defines how the employee gets paid for hours worked based on defined rules (full-time, part-time, hourly, etc.)- Data As of: The date to which the given data applies to.

Statewide VA data on the demographic and economic characteristics of the labor force are published on an annual-average basis from the Current Population Survey (CPS), the sample survey of households used to calculate the U.S. unemployment rate. For VA state ,employment status data are tabulated for 67 sex, race, Hispanic or Latino ethnicity, marital status, and detailed age categories and evaluated against a minimum base, calculated to reflect an expected maximum coefficient of variation (CV) of 50 percent, to determine reliability for publication

Status of employment for people aged 15+. Number of people who are in/out of the labour force, employed or unemployed. The "employed" category is disaggregated by Status in employment (for the main job). Status of employment is divided into 5 categories: employees, employers, own-account workers, contributing family workers and workers not classified by status. "Employees" comprises all individuals working in the public and private sector, "Contributing family workers" contains all individuals working to sell their products, producing goods for family use and those working to help a family business.

Find more Pacific data on PDH.stat.

County workforce job data, demographics, and job categories as defined by the Equal Employment Opportunity Commission. More information about the job categories can be found in Appendix 2 at the following link: https://eeocdata.org/EEO4/howto/instructionbooklet

Users can download data or view data tables on topics related to the labor force of the United States. Background Current Population Survey is a joint effort between the Bureau of Labor Statistics and the Census Bureau. It provides information and data on the labor force of the United States, such as: employment, unemployment, earnings, hours of work, school enrollment, health, employee benefits and income. The CPS is conducted monthly and has a sample of approximately 50,000 households. It is representative of the non-institutionalized US population. The sample provides estimates for the nation as a whole and serves as part of model-based estimates for individual states and other geographic areas. User Functionality Users can download data sets or view data tables on their topic of interest. Data can be organized by a variety of demographic variables, including: sex, age, race, marital status and educational attainment. Data is available on a national or state level. Data Notes The CPS is conducted monthly and has a sample of approximately 50,000 households. It is representative of the non-institutionalized US population. The sample provides estimates for th e nation as a whole and serves as part of model-based estimates for individual states and other geographic areas.

This dataset is provided by the department of Human Resources (HR) which details each City of Saint Paul department's or office's total workforce by race, gender, disability within E.E.O job groups.

This data asset was created in response to House Report 117-401, which stated, "The Committee directs the USAID Administrator, in consultation with the Director of the Office of Personnel Management and the Director of the Office of Management and Budget, to submit a report to the appropriate congressional committees, not later than 180 days after enactment of this Act, on USAID's workforce data that includes disaggregated demographic data and other information regarding the diversity of the workforce of USAID. Such report shall include the following data to the maximum extent practicable and permissible by law: 1) demographic data of USAID workforce disaggregated by grade or grade-equivalent; 2) assessment of agency compliance with the Equal Employment Opportunity Commission Management Directive 715; and 3) data on the overall number of individuals who are part of the workforce, including all U.S. Direct Hires, personnel under personal services contracts, and Locally Employed staff at USAID. The report shall also be published on a publicly available website of USAID in a searchable database format." This data asset fulfills the final part of this requirement, to publish the data in a searchable database format. The data are compiled from USAID's 2021 MD-715 report, available at https://www.usaid.gov/reports/md-715. The original data source is the system National Finance Center Insight owned by the Treasury Department.

Graph and download economic data for Infra-Annual Labor Statistics: Working-Age Population Total: From 25 to 54 Years for United States (LFWA25TTUSM647N) from Jan 1955 to Apr 2025 about 25 to 54 years, working-age, population, and USA.

In the fourth quarter of 2024, there were around 40.5 million people in Thailand's workforce, slightly increased compared to the previous quarter. The number of population in the workforce in the country has fluctuated over the observed period.

In fiscal year 2023, more than half of the professional workforce at Deloitte in the United States were white. Percentage-wise, this is a decrease in the number of white employees from 2022. Asian employees made up the next largest demographic in between 2022 and 2023. Representation of all non-white demographics increased between 2021 and 2023.

This dataset represents data beginning from 2010 to current date. The values represent the entire City of Austin workforce.

The goal of the City of Austin’s Employee Demographic data site is to provide information that is transparent and available to the public in a format that can be easily researched, filtered, analyzed and consumed. The Human Resource Department believes that by providing data sets to the public that are key to setting City priorities and assisting in making better informed decisions, it will enhance the collaboration among City departments and their external partners that will help bring a higher level of civic engagement with the public on local civic issues and concerns.

Data updated quarterly.Data Attributes and Definitions -- Department: The department the employee works in.- Department ID: The numeric identifier for the department (typically 4 digits).- Job: The name for the job assigned to the employee.- Category: Grouping of employees in similar jobs/leadership roles.- Sub Category: Secondary grouping of employees within a category.- Race/Ethnicity: The race/ethnicity category which the employee identifies with (self-identified).- Gender: Designates the employee's gender (self-identified).- Age: The chronological number (age) assigned to the employee based on date of birth.- Age Group: Grouping of employees having approximately the same age or age range.- Original Hire Date: Date upon which the employee was originally hired.- Last Hire Date: Date upon which an employee was hired; may be a rehire date.- Pay Class: Defines how the employee gets paid for hours worked based on defined rules (full-time, part-time, hourly, etc.)- Data As of: The date to which the given data applies to.

The percent of persons who are not in the labor force out of all persons between the ages of 16 and 64 in the area. There are several reasons why persons may not be included in the labor force. These reasons may include: they are caretakers for children or other family members; they attend school or job training; they may have a disability; and they are discouraged or frustrated and have given up seeking a job or have a history that may include criminal activity. Source: American Community Survey Years Available: 2006-2010, 2007-2011, 2008-2012, 2009-2013, 2010-2014, 2011-2015, 2012-2016, 2013-2017, 2014-2018, 2015-2019, 2016-2020, 2017-2021

Graph and download economic data for Employment-Population Ratio (EMRATIO) from Jan 1948 to Apr 2025 about employment-population ratio, civilian, 16 years +, household survey, employment, population, and USA.

The Labour Force Survey provides estimates of employment and unemployment which are among the timeliest and important measures of performance of the Canadian economy. With the release of the survey results only 10 days after the completion of data collection, the LFS estimates are the first of the major monthly economic data series to be released. The Canadian Labour Force Survey was developed following the Second World War to satisfy a need for reliable and timely data on the labour market. Information was urgently required on the massive labour market changes involved in the transition from a war to a peace-time economy. The main objective of the LFS is to divide the working-age population into three mutually exclusive classifications - employed, unemployed, and not in the labour force - and to provide descriptive and explanatory data on each of these. LFS data are used to produce the well-known unemployment rate as well as other standard labour market indicators such as the employment rate and the participation rate. The LFS also provides employment estimates by industry, occupation, public and private sector, hours worked and much more, all cross-classifiable by a variety of demographic characteristics. Estimates are produced for Canada, the provinces, the territories and a large number of sub-provincial regions. For employees, wage rates, union status, job permanency and workplace size are also produced. These data are used by different levels of government for evaluation and planning of employment programs in Canada. Regional unemployment rates are used by Employment and Social Development Canada to determine eligibility, level and duration of insurance benefits for persons living within a particular employment insurance region. The data are also used by labour market analysts, economists, consultants, planners, forecasters and academics in both the private and public sector. Note: Because missing values are removed from this dataset, any form of non-response (e.g. valid skip, not stated) or don't know/refusal cannot be coded as a missing. The "Sysmiss" label in the Statistics section indicates the number of non-responding records for each variable, and the "Valid" values in the Statistics section indicate the number of responding records for each variable. The total number of records for each variable is comprised of both the sysmiss and valid values. LFS revisions: LFS estimates were previously based on the 2001 Census population estimates. These data have been adjusted to reflect 2006 Census population estimates and were revised back to 1996. The census metropolitan area (CMA) variable has been expanded from the three largest CMAs in Canada to nine. Two occupation variables based on the 2016 National Occupation Classicifcation have been reintroduced: a generic 10- category variable (NOC_10) and a detailed 40-category variable (NOC_40). A new variable on immigrant status (IMMIG) has been introduced, which distingushes between recent immigrants and established immigrants. Fourteen variables related to family and spouse/partner's labour force characteristics have been removed, as well as eight out of date variables which have been removed from the record layout.