This transformed view of Employee Demographics - Public dataset counts the number of and percentage of city employees by race as self-reported by employee based on EEOC classification. This information is used by "City Employee vs. Community Demographics dataset" at https://citydata.mesaaz.gov/Economic-Development/Chart-Data-for-City-Employee-vs-Community-Demograp/bt2n-zimw

Dataset provides the public with a snapshot of the County of Los Angeles workforce including the count of full-time permanent employees by department, employee demographics (i.e., ethnicity and gender) EEO Job Categories and, EEO Functions.

Filtered view of current employees as of dataset refresh date and includes employee job title, department name, EEO Category and Sworn status. This view redacts age group, race/ethnicity, gender and date of hire. For race/ethnicity and gender employee demographics see https://citydata.mesaaz.gov/Human-Resources/Employee-Demographics-Race-Ethnicity-Public/ty4p-25y2/about_data.

Explore demographic data on the Massachusetts executive branch workforce. Track our progress toward our goals to reflect the diversity of the people we serve, and to stand out as an employer of choice.

Graph and download economic data for All Employees, Federal (CES9091000001) from Jan 1939 to Sep 2025 about establishment survey, federal, government, employment, and USA.

Employee demographic data produced by City of Long Beach Human Resources Department.

As of June 2022, 57.6 percent of employees in leadership roles at Meta were white, whilst 28.6 percent were Asian. Overall, 11.7 percent of employees in non-technical roles were Hispanic, and 11.2 percent were Black. Moreover, Asian employees accounted for the majority of employees in technical roles, making up 55.8 percent of employees in these positions.

Employee demographics (Experiment 1).

The county and region of the workers are determined by the office to which they are assigned. Adult Protective Services (APS): APS Investigations employees protect people age 65 and older and adults with disabilities from abuse, neglect, and financial exploitation by investigating and providing or arranging for services necessary to alleviate or prevent further maltreatment. Child Protective Investigations (CPI/CCI): Counts the number of active CPI and CPS staff on the last day of the fiscal year by staff type and demographics. Child Care Investigations (CCI), which is a part of CPI and include Day Care Investigations (DCI) and Residential Child Care Investigations (RCCI) are only available from 2018 onward. This is due to the split of those job functions from Child Care Licensing, which was a part of DFPS until 2017, when it was transferred to the Health and Human Services Commission (HHSC). Statewide Intake (SWI): Statewide Intake (SWI) serves as the “front door to the front line” for all DFPS programs. As the central point of contact for reports of abuse, neglect and exploitation of vulnerable Texans. SWI staff are available 24 hours a day, 7 days per week, 365 days per year. Prior to FY2018, all SWI staff were located in the Austin area. Visit dfps.texas.gov for information on all DFPS programs

The Agency Report Table aggregates pay and employment characteristics in accordance with the requirements of Local Law 18 of 2019. The Table is a point-in-time snapshot of employees who were either active or on temporary leave (parental leave, military leave, illness, etc.) as of December 31st of each year the data is available (see Column "Data Year"). In addition, the Table contains snapshot data of active employees in seasonal titles as of June 30th. To protect the privacy of employees, the sign “<5” is used instead of the actual number for groups of less than five (5) employees, in accordance with the Citywide Privacy Protection Policies and Protocols. The Pay and Demographics Report, and the list of agencies included is available on the MODA Open Source Analytics Library: https://modaprojects.cityofnewyork.us/local-law-18/

Each row represents a group of employees with a common agency, EEO-4 Job Category, pay band, employee status and demographic attributes, which include race, ethnicity and gender.

Context

demographics and employment in the united states In the wake of the Great Recession of 2009, there has been a good deal of focus on employment statistics, one of the most important metrics policymakers use to gauge the overall strength of the economy. In the United States, the government measures unemployment using the Current Population Survey (CPS), which collects demographic and employment information from a wide range of Americans each month.

Content

The observations in the dataset represent people surveyed in the September 2013 CPS who actually completed a survey. While the full dataset has 385 variables, in this exercise we will use a more compact version of the dataset, CPSData.csv, which has the following variables:

PeopleInHousehold: The number of people in the interviewee's household.

Region: The census region where the interviewee lives.

State: The state where the interviewee lives.

MetroAreaCode: A code that identifies the metropolitan area in which the interviewee lives (missing if the interviewee does not live in a metropolitan area). The mapping from codes to names of metropolitan areas is provided in the file MetroAreaCodes.csv.

Age: The age, in years, of the interviewee. 80 represents people aged 80-84, and 85 represents people aged 85 and higher.

Married: The marriage status of the interviewee.

Sex: The sex of the interviewee.

Education: The maximum level of education obtained by the interviewee.

Race: The race of the interviewee.

Hispanic: Whether the interviewee is of Hispanic ethnicity.

CountryOfBirthCode: A code identifying the country of birth of the interviewee. The mapping from codes to names of countries is provided in the file CountryCodes.csv.

Citizenship: The United States citizenship status of the interviewee.

EmploymentStatus: The status of employment of the interviewee.

Industry: The industry of employment of the interviewee (only available if they are employed).

Acknowledgements

MITx ANALYTIX

As of January 2024, the majority of Google employees worldwide, almost 66 percent, were male. The distribution of male and female employees at Google hasn’t seen a big change over the recent years. In 2014 the share of female employees at Google was 30.6 percent. In 2021 this number has increased by only 3 percent. Considering that the total number of Google employees increased greatly between the years 2007 and 2020, the female quota among the employees had seen rather a small increase. Google as a company Google is a diverse internet company that provides a wide range of digital products and services. In 2022, the company’s global revenue was over 279 billion U.S. dollars. Most of its revenue, around 305 billion U.S. dollars, was from advertising. Among its services, the most popular ones are YouTube and Google Play. Male and female employees at tech companies Google is not the only tech company with a lower number of female employees. This pattern can be seen in other big tech companies too. In 2019, in a ranking of 20 leading tech companies worldwide, only 23andMe had more than a 50 percent share of female employees. The majority of tech companies in the ranking have far more male than female employees.

This table contains demographics information for employees of the Chapel Hill Police Department. Current as of October 2016.

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

This dataset contains annual average CES data for California statewide and areas from 1990 to 2024.

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.

In 2024, the employment rate of the workforce of 55 years and older decreased to 37.3 percent. Employment rate among young adults (age 16-24) was at 50.9 percent in 2024. For monthly updates on employment in the United States visit the annual national employment rate here.

This is a cleaned and analyzed version of the City of Houston Employee Payroll dataset, specifically focused on withdrawn employees and their financial impact on city departments. This dataset was prepared in response to a City Council Finance Committee request for January 2025 withdrawal analysis.

This dataset transforms the raw payroll data into actionable insights by: - Aggregating data by department and analysis categories - Calculating total financial impact across all compensation types - Computing average salaries, pay grades, and tenure metrics - Providing headcount loss by department - Breaking down impacts by employment type, FLSA status, and pay grade categories

Key Metrics Included: - Headcount_Lost: Number of withdrawn employees per department - Total_Base_Salary_Impact: Cumulative base salary of withdrawn employees - Total_Gross_Pay_Impact: Total gross compensation impact - Total_Overtime_Impact: Overtime pay associated with withdrawn positions - Total_Other_Pay_Impact: Additional compensation impacts - Avg_Annual_Salary: Average salary of withdrawn employees - Avg_Tenure_Years: Average years of service before withdrawal - Pct_Of_Total_Financial_Impact: Percentage contribution to overall fiscal impact

Analysis Sections: 1. OVERALL SUMMARY: City-wide totals and averages 2. DEPARTMENT ANALYSIS: Department-by-department breakdown showing Houston Public Works was most impacted (15 withdrawals, $637,550 base salary impact) 3. Category breakdowns by Employment Type, FLSA Status, and Pay Grade

Use Cases: - Budget planning and reallocation decisions - Workforce retention strategy development - Department-level resource planning - Understanding compensation patterns in workforce attrition - City Council presentations and policy discussions

Data Processing: - Filtered for "Withdrawn" status employees only - Calculated financial impacts across multiple compensation categories - Aggregated by relevant categorical dimensions - Computed tenure and demographic statistics - Anonymized per City of Houston data protection policies

Context: Prepared for City Council Finance Committee presentation (November 2025) analyzing the fiscal and operational impact of January 2025 employee withdrawals across City of Houston departments.

Data Source: City of Houston Open Data Portal - Employee Payroll Database Analysis Date: November 2025

Comparing the percentage of employee race to the percentage of city resident race. Employee information comes from Employee Demographics: Ethnicity https://citydata.mesaaz.gov/Human-Resources/Employee-Demographics-Ethnicity/6kd3-uaks. Community information comes from Community Demographics: Race: https://citydata.mesaaz.gov/Diversity/Community-Demographics-Race/xaqj-9vxh/data

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.

Current Employment by Industry (CES) data reflect jobs by "place of work." It does not include the self-employed, unpaid family workers, and private household employees. Jobs located in the county or the metropolitan area that pay wages and salaries are counted although workers may live outside the area. Jobs are counted regardless of the number of hours worked. Individuals who hold more than one job (i.e. multiple job holders) may be counted more than once. The employment figure is an estimate of the number of jobs in the area (regardless of the place of residence of the workers) rather than a count of jobs held by the residents of the area.