United States - Employed full time: Wage and salary workers: Database administrators occupations: 16 years and over was 136.00000 Thous. of Persons in January of 2024, according to the United States Federal Reserve. Historically, United States - Employed full time: Wage and salary workers: Database administrators occupations: 16 years and over reached a record high of 144.00000 in January of 2022 and a record low of 40.00000 in January of 2000. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Employed full time: Wage and salary workers: Database administrators occupations: 16 years and over - last updated from the United States Federal Reserve on July of 2025.

The O*NET Database contains hundreds of standardized and occupation-specific descriptors on almost 1,000 occupations covering the entire U.S. economy. The database, which is available to the public at no cost, is continually updated by a multi-method data collection program. Sources of data include: job incumbents, occupational experts, occupational analysts, employer job postings, and customer/professional association input.

Data content areas include:

• Worker Characteristics (e.g., Abilities, Interests, Work Styles)
• Worker Requirements (e.g., Education, Knowledge, Skills)
• Experience Requirements (e.g., On-the-Job Training, Work Experience)
• Occupational Requirements (e.g., Detailed Work Activities, Work Context)
• Occupation-Specific Information (e.g., Job Titles, Tasks, Technology Skills)

United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Database administrators occupations: 16 years and over was 2492.00000 $ in January of 2024, according to the United States Federal Reserve. Historically, United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Database administrators occupations: 16 years and over reached a record high of 2492.00000 in January of 2024 and a record low of 1018.00000 in January of 2001. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Database administrators occupations: 16 years and over - last updated from the United States Federal Reserve on July of 2025.

Oregon workers' compensation data about insurers and self-insured employers. The data is presented in the Department of Consumer and Business Services report at https://www.oregon.gov/dcbs/reports/compensation/Pages/index.aspx. The attached pdf provides definitions of the data.

This dataset includes Baltimore City employee salaries and gross pay from fiscal year 2011 through last fiscal year and includes employees who were employed on June 30 of the last fiscal year. For fiscal years 2020 and prior, data are extracted from the ADP payroll system. For fiscal year 2023, the data are combined from the ADP system and the Workday enterprise resource planning system which now includes payroll.Change Log- Added FY2023 data- Metadata added- Columns renamed to a standard format- Youth workers not employed by City removed- Agency names reformatted with Workday conventions-7/25/24: Added FY2024 data. To leave feedback or ask a question about this dataset, please fill out the following form: Baltimore City Employee Salaries feedback form.

A large body of research has demonstrated that land use and urban form can have a significant effect on transportation outcomes. People who live and/or work in compact neighborhoods with a walkable street grid and easy access to public transit, jobs, stores, and services are more likely to have several transportation options to meet their everyday needs. As a result, they can choose to drive less, which reduces their emissions of greenhouse gases and other pollutants compared to people who live and work in places that are not location efficient. Walking, biking, and taking public transit can also save people money and improve their health by encouraging physical activity.

The Smart Location Database summarizes several demographic, employment, and built environment variables for every census block group (CBG) in the United States. The database includes indicators of the commonly cited “D” variables shown in the transportation research literature to be related to travel behavior. The Ds include residential and employment density, land use diversity, design of the built environment, access to destinations, and distance to transit. SLD variables can be used as inputs to travel demand models, baseline data for scenario planning studies, and combined into composite indicators characterizing the relative location efficiency of CBG within U.S. metropolitan regions.

This update features the most recent geographic boundaries (2019 Census Block Groups) and new and expanded sources of data used to calculate variables. Entirely new variables have been added and the methods used to calculate some of the SLD variables have changed.

See https://www.epa.gov/smartgrowth/smart-location-mapping for more information.

United States Private Employee: ARP: Establishments: => 100 Workers data was reported at 67.000 % in 2018. This records an increase from the previous number of 65.000 % for 2017. United States Private Employee: ARP: Establishments: => 100 Workers data is updated yearly, averaging 66.000 % from Mar 1999 (Median) to 2018, with 18 observations. The data reached an all-time high of 68.000 % in 2009 and a record low of 64.000 % in 2014. United States Private Employee: ARP: Establishments: => 100 Workers data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G077: Employee Benefits Survey: Private Industry.

United States Private Employee: DCB: Workers in Service Producing Industries data was reported at 30.000 % in 2017. This records a decrease from the previous number of 31.000 % for 2016. United States Private Employee: DCB: Workers in Service Producing Industries data is updated yearly, averaging 33.000 % from Mar 1999 (Median) to 2017, with 17 observations. The data reached an all-time high of 34.000 % in 2010 and a record low of 28.000 % in 2000. United States Private Employee: DCB: Workers in Service Producing Industries data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.G076: Employee Benefits Survey: Private Industry.

Graph and download economic data for Federal Minimum Hourly Wage for Nonfarm Workers for the United States (FEDMINNFRWG) from Oct 1938 to Jun 2025 about per hour, minimum wage, nonfarm, workers, hours, federal, wages, and USA.

This dataset includes annual salary, regular pay, incentive pay, and gross pay for employees under the County Executive and independently elected County officials for the years 2016 to the present, and is updated quarterly.

For December files, Annual Salary is the employee's annual salary or annualized wage as of the last pay period for the year, and the pay data fields (Regular Pay, Incentive Pay and Gross Pay) are payments made to the employee through the last pay period of the year. The June file contains the Annual Salary, and the pay data as of the last pay period in June. Note that the June file is replaced by the December file each year.

Union contracted salaries and wages which were not settled during the calendar year reflect the wage as of the end of the year. Regular Pay for these positions includes salaries and wages for the year it was paid, not the year it was earned.

In addition to salary or wages for days worked and retroactively settled contract payments, Regular Pay also includes pay for days such as holidays, sick days, and vacation days. Overtime Pay includes pay for extra work typically at a wage rate different from regular wages as set forth in a collective bargaining agreement. Incentive Pay includes such things as a wellness incentive and longevity pay.

Employee names are included in the dataset with the following exceptions permitted by the Pennsylvania Right to Know Law:

• The names of individuals who were active sworn law enforcement officers during the year; and
• Information that would disclose individually identifiable health information.

Additionally, records related to Court of Common Pleas employees would need to be requested from the Courts.

In March 2022, the salary data files prior to 2021 were updated so that all columns matched for consistent presentation.

Introducing Job Posting Datasets: Uncover labor market insights!

Elevate your recruitment strategies, forecast future labor industry trends, and unearth investment opportunities with Job Posting Datasets.

Job Posting Datasets Source:

1. Indeed: Access datasets from Indeed, a leading employment website known for its comprehensive job listings.

2. Glassdoor: Receive ready-to-use employee reviews, salary ranges, and job openings from Glassdoor.

3. StackShare: Access StackShare datasets to make data-driven technology decisions.

Job Posting Datasets provide meticulously acquired and parsed data, freeing you to focus on analysis. You'll receive clean, structured, ready-to-use job posting data, including job titles, company names, seniority levels, industries, locations, salaries, and employment types.

Choose your preferred dataset delivery options for convenience:

Receive datasets in various formats, including CSV, JSON, and more. Opt for storage solutions such as AWS S3, Google Cloud Storage, and more. Customize data delivery frequencies, whether one-time or per your agreed schedule.

Why Choose Oxylabs Job Posting Datasets:

1. Fresh and accurate data: Access clean and structured job posting datasets collected by our seasoned web scraping professionals, enabling you to dive into analysis.

2. Time and resource savings: Focus on data analysis and your core business objectives while we efficiently handle the data extraction process cost-effectively.

3. Customized solutions: Tailor our approach to your business needs, ensuring your goals are met.

4. Legal compliance: Partner with a trusted leader in ethical data collection. Oxylabs is a founding member of the Ethical Web Data Collection Initiative, aligning with GDPR and CCPA best practices.

Pricing Options:

Standard Datasets: choose from various ready-to-use datasets with standardized data schemas, priced from $1,000/month.

Custom Datasets: Tailor datasets from any public web domain to your unique business needs. Contact our sales team for custom pricing.

Experience a seamless journey with Oxylabs:

• Understanding your data needs: We work closely to understand your business nature and daily operations, defining your unique data requirements.
• Developing a customized solution: Our experts create a custom framework to extract public data using our in-house web scraping infrastructure.
• Delivering data sample: We provide a sample for your feedback on data quality and the entire delivery process.
• Continuous data delivery: We continuously collect public data and deliver custom datasets per the agreed frequency.

Effortlessly access fresh job posting data with Oxylabs Job Posting Datasets.

Comprehensive dataset of 103,073 Social workers in United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

This data set contains DOT employee workers compensation claim data for current and past DOT employees. Types of data include claim data consisting of PII data (SSN, Name, Date of Birth, Home Address, Financial Institution, medical, etc.) and claim data from the Department of Labor

United States Private Employee: DBP: Union Workers data was reported at 66.000 % in 2017. This records an increase from the previous number of 65.000 % for 2016. United States Private Employee: DBP: Union Workers data is updated yearly, averaging 67.000 % from Mar 1999 (Median) to 2017, with 17 observations. The data reached an all-time high of 72.000 % in 2005 and a record low of 65.000 % in 2016. United States Private Employee: DBP: Union Workers data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.G076: Employee Benefits Survey: Private Industry.

Overview: Each quarter, the Temporary Foreign Worker Program (TFWP) publishes Labour Market Impact Assessment (LMIA) statistics on Open Government Data Portal, including quarterly and annual LMIA data related to, but not limited to, requested and approved TFW positions, employment location, employment occupations, sectors, TFWP stream and temporary foreign workers by country of origin. The TFWP does not collect data on the number of TFWs who are hired by an employer and have arrived in Canada. The decision to issue a work permit rests with Immigration, Refugees and Citizenship Canada (IRCC) and not all positions on a positive LMIA result in a work permit. For these reasons, data provided in the LMIA statistics cannot be used to calculate the number of TFWs that have entered or will enter Canada. IRCC publishes annual statistics on the number of foreign workers who are issued a work permit: https://open.canada.ca/data/en/dataset/360024f2-17e9-4558-bfc1-3616485d65b9. Please note that all quarterly tables have been updated to NOC 2021 (5 digit and training, education, experience and responsibilities (TEER) based). As such, Table 5, 8, 17, and 24 will no longer be updated but will remain as archived tables. Frequency of Publication: Quarterly LMIA statistics cover data for the four quarters of the previous calendar year and the quarter(s) of the current calendar year. Quarterly data is released within two to three months of the most recent quarter. The release dates for quarterly data are as follows: Q1 (January to March) will be published by early June of the current year; Q2 (April to June) will be published by early September of the current year; Q3 (July to September) will be published by early December of the current year; and Q4 (October to December) will be published by early March of the next year. Annual statistics cover eight consecutive years of LMIA data and are scheduled to be released in March of the next year. Published Data: As part of the quarterly release, the TFWP updates LMIA data for 28 tables broken down by: TFW positions: Tables 1 to 10, 12, 13, and 22 to 24; LMIA applications: Tables 14 to 18; Employers: Tables 11, and 19 to 21; and Seasonal Agricultural Worker Program (SAWP): Tables 25 to 28. In addition, the TFWP publishes 2 lists of employers who were issued a positive or negative LMIA: Employers who were issued a positive LMIA by Program Stream, NOC, and Business Location (https://open.canada.ca/data/en/dataset/90fed587-1364-4f33-a9ee-208181dc0b97/resource/b369ae20-0c7e-4d10-93ca-07c86c91e6fe); and Employers who were issued a negative LMIA by Program Stream, NOC, and Business Location (https://open.canada.ca/data/en/dataset/f82f66f2-a22b-4511-bccf-e1d74db39ae5/resource/94a0dbee-e9d9-4492-ab52-07f0f0fb255b). Things to Remember: 1. When data are presented on positive or negative LMIAs, the decision date is used to allocate which quarter the data falls into. However, when data are presented on when LMIAs are requested, it is based on the date when the LMIA is received by ESDC. 2. As of the publication of 2022Q1- 2023Q4 data (published in April 2024) and going forward, all LMIAs in support of 'Permanent Residence (PR) Only' are included in TFWP statistics, unless indicated otherwise. All quarterly data in this report includes PR Only LMIAs. Dual-intent LMIAs and corresponding positions are included under their respective TFWP stream (e.g., low-wage, high-wage, etc.) This may impact program reporting over time. 3. Attention should be given for data that are presented by ‘Unique Employers’ when it comes to manipulating the data within that specific table. One employer could be counted towards multiple groups if they have multiple positive LMIAs across categories such as program stream, province or territory, or economic region. For example, an employer could request TFWs for two different business locations, and this employer would be counted in the statistics of both economic regions. As such, the sum of the rows within these ‘Unique Employer’ tables will not add up to the aggregate total.

When using this resource please cite the article for which it was developed:

Matysiak, A., Hardy, W. and van der Velde Lucas (2024). Structural Labour Market Change and Gender Inequality in Earnings. Work, Employment and Society, vol. (), pp. - (to be filled in upon publication).

The dataset contributes a categorisation of tasks conducted across occupations, with a distinction between social tasks directed "inward" (e.g. towards members of own organisation, co-workers, employees, etc.) and those directed "outward" (e.g. towards students, clients, patients, etc.). This provides more depth to the discussion on technology, labour market changes and gender differences in how these trends are experienced. The dataset builds on the ESCO database v1.0.8 found here.

The following task categories are available at occupation levels:

Social

Social Inward

Social Outward

Analytical*

Routine**

Manual

• Additionally, a distinction between technical and creative/artistic tasks is provided although it is not used in Matysiak et al. (2024).

** In the initial files, some task items are categorised as Routine, while some are categorised as Non-Routine. In the subsequent steps for occupation-level information, the Routine task score consists of a difference between the Routine score and the Non-Routine score (see the paper for more information).

The repository contains four data files at different stages of task development. For the codes, please see the accompanying GitHub repository. The ESCO database covers, i.a., skills/competences and attitudes, to which we jointly refer as task items (as is standard in the literature using other databases such as ONET). For detailed methodology and interpretation see Matysiak et al. (2024).

1) esco_tasks.csv - encompasses all ESCO occupations and all task items with tags on task categorisation into broader categories. It also includes the split between the "essential" and "optional" task items and the variant "management-focused" and "care-focused" measures of social tasks as used in the robustness checks in the Matysiak et al. (2024) paper.

2) esco_onet_tasks.csv - additionally includes pre-prepped task items from the ONET database, traditionally used to describe the task content of occupations. These data can be used to validate the ESCO measures.

3) esco_onet_matysiaketal2024.csv - contains a subset of the variables from esco_onet_tasks.csv used for the Matysiak et al. (2024) paper.

4) tasks_isco08_2018_stdlfs.csv - contains the final task measures after the standardisation and derivation procedures described in Matysiak et al. (2024).

For all details on the procedures, applied crosswalks, methods, etc. please refer to the GitHub repository and the Matysiak et al. (2024) paper.

Integrates early data to give as full a picture as possible of the economic conditions faced by Canadian workers from 1919 to 1945. During the period in question, four censuses provided labour information; in addition, monthly surveys were conducted by the Department of Labour, which gathered employment information from large employers and unemployment statistics from trade unions. The data series run from the start of 1919 to the end of 1944. The first date represents the beginning of monthly unemployment data, and is the earliest reasonable data for back-casting the employment series. The second date was chosen as the final year for two reasons: the Labour Force Survey (LFS) began the following year, and unemployment was essentially zero, owing to wartime conditions.

The Texas Department of Insurance, Division of Workers’ Compensation (DWC) publishes a quarterly report of employers with active Texas workers’ compensation insurance coverage. Employers with coverage are called “subscribers.” Texas does not require most private employers to have workers' compensation insurance coverage. Insurance carriers report coverage data to DWC using the International Association of Industrial Accident Boards and Commissions’ (IAIABC) IAIABC Proof of Coverage (POC) Release 2.1 electronic data interchange (EDI) standard. The National Council on Workers’ Compensation Insurance (NCCI) collects the POC data for DWC. POC filings are the source of this data set. Visit the DWC Employer Coverage Page for more information.

This dataset has been published by the Human Resources Department of the City of Virginia Beach and data.virginiabeach.gov. The mission of data.virginiabeach.gov is to provide timely and accurate City information to increase government transparency and access to useful and well organized data by the general public, non-governmental organizations, and City of Virginia Beach employees.Distributed bydata.virginiabeach.gov2405 Courthouse Dr.Virginia Beach, VA 23456EntityEmployee SalariesPoint of ContactHuman ResourcesSherri Arnold, Human Resources Business Partner IIIsharnold@vbgov.com757-385-8804Elda Soriano, HRIS Analystesoriano@vbgov.com757-385-8597AttributesColumn: DepartmentDescription: 3-letter department codeColumn: Department DivisionDescription: This is the City Division that the position is assigned to.Column: PCNDescription: Tracking number used to reference each unique position within the City.Column: Position TitleDescription: This is the title of the position (per the City’s pay plan).Column: FLSA Status Description: Represents the position’s status with regards to the Fair Labor Standards Act (FLSA) “Exempt” - These positions do not qualify for overtime compensation – Generally, a position is classified as FLSA exempt if all three of the following criteria are met: 1) Paid at least $47,476 per year ($913 per week); 2) Paid on a salary basis - generally, salary basis is defined as having a guaranteed minimum amount of pay for any work week in which the employee performs any work; 3) Perform exempt job duties - Job duties are split between three classifications: executive, professional, and administrative. All three have specific job functions which, if present in the employee’s regular work, would exempt the individual from FLSA. Employees may also be exempt from overtime compensation if they are a “highly compensated employee” as defined by the FLSA or the position meets the criteria for other enumerated exemptions in the FLSA.“Non-exempt” – These positions are eligible for overtime compensation - positions classified as FLSA non-exempt if they fail to meet any of exempt categories specified in the FLSA. Column: Initial Hire DateDescription: This is the date that the full-time employee first began employment with the City.Column: Date in TitleDescription: This is the date that the full-time employee first began employment in their current position.Column: SalaryDescription: This is the annual salary of the full-time employee or the hourly rate of the part-time employee.Frequency of dataset updateMonthly

Graph and download economic data for Average Hourly Earnings of Production and Nonsupervisory Employees, Total Private (AHETPI) from Jan 1964 to May 2025 about nonsupervisory, headline figure, earnings, average, establishment survey, hours, wages, production, private, employment, and USA.