Historical Employment Statistics 1990 - current. The Current Employment Statistics (CES) more information program provides the most current estimates of nonfarm employment, hours, and earnings data by industry (place of work) for the nation as a whole, all states, and most major metropolitan areas. The CES survey is a federal-state cooperative endeavor in which states develop state and sub-state data using concepts, definitions, and technical procedures prescribed by the Bureau of Labor Statistics (BLS). Estimates produced by the CES program include both full- and part-time jobs. Excluded are self-employment, as well as agricultural and domestic positions. In Connecticut, more than 4,000 employers are surveyed each month to determine the number of the jobs in the State. For more information please visit us at http://www1.ctdol.state.ct.us/lmi/ces/default.asp.

This dataset uses seasonally adjusted data from the US Bureau of Labor Statistics to present information on Maryland's labor force participation rate, employment rate, and unemployment rate.

Part Time Employment in the United States decreased to 28478 Thousand in February from 28727 Thousand in January of 2026. This dataset provides - United States Part Time Employment- actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset represents the CHANGE in the number of jobs per industry category and sub-category from the previous month, not the raw counts of actual jobs. The data behind these monthly change values is from the Bureau of Labor Statistics (BLS) Current Employment Statistics (CES) program. CES data represents businesses and government agencies, providing detailed industry data on employment on nonfarm payrolls.

The U.S. Bureau of Economic Analysis’ Total Full-Time and Part-Time Employment data provides one of the most comprehensive, publicly available accountings of average annual employment. Beyond full- and part-time employment types, it includes farm employment and other sectors that aren’t always included in other sources, such as Public Administration (with more detail of federal than state and local employment in this category). It also includes and distinguishes both Wage and Salary employees from Proprietors who own their own unincorporated businesses and handle taxation chiefly as personal income. Proprietors tend to be single-person or small businesses and can include construction or repair workers, babysitters, ride-share drivers, artists, local grocers, housekeepers, various freelancers and consultants, and some attorneys and doctors.

Graph and download economic data for Job Openings: Leisure and Hospitality (JTS7000JOL) from Dec 2000 to Dec 2025 about job openings, leisure, hospitality, vacancy, and USA.

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)

Number of employees by National Occupational Classification (NOC), last 5 months. 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.

Global IT Jobs Analysis

A Detailed Investigation of Salary, Location, and Job Requirements

By [source]

About this dataset

This dataset contains valuable insights into current job opportunities in the information technology (IT) sector all around the world. It offers an overview of available jobs and relevant data such as company, location, salary and links to further information. With this insight, one has the chance to better understand what it takes to land a remote or data-science job in today's global market. The ever increasing demand for IT workforce puts technical skills at a premium, so understanding exactly what employers are searching for can give potential employees an edge in catching the eye of these businesses! Digging through this dataset can provide details on current trends in terms of salary expectations and geographical locations where these roles are most popular. Beyond that, get an idea about which abilities seem most valuable when it comes to remote or data-science positions. Use this arsenal of knowledge to take your career goals into your own hands now!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset provides an opportunity to explore the remote and data-science job opportunities around the world. Using this dataset, you can analyze trends in job requirements, salary packages offered, location of available jobs and more. With the knowledge gained from this data set, individuals and companies can make more informed decisions about pursuing a certain path in their career or hiring for their business.

The dataset includes columns with important information such as Job title, Company offering the job, Location of the position , Salary offered for that position and a Link to its respective posting. Using these columns you can analyze various factors regarding global IT Jobs availability over different locations in alignment with salary offered for positions and any specific skill sets sought out by companies .

To get executable insights from this data set users should first load it into their respective computing environment (Python or R). After loading it in your environment users should start off by exploring Groupby statements along factors like Companies offering jobs ,Salary offered ,Location etc. followed by descriptive statistics like mean & median of Salary Levels per country/region etc. After getting basic insight about summary statistics for various factors belonging all together within “Job” range user could move forward to look over individual cases (specific skill sets) after which they could filter out & generate valueable insights needed .

With our comprehensive understanding of global supply & demand rates individuals/corporations could always use these datasets to help them keep track on talent acquisition landscape when they hire globally or relocating teams as companies who need such information would greatly benefit from versatile tools like this one that offer valuable actionsable insights on an ongoing basis depending upon dayers choosing!

Research Ideas

• Identifying the most in-demand skills and employment requirements for remote data science and IT jobs, across different countries and regions.
• Developing a prediction model to forecast future salary expectations for data science professionals based on location, company, job type, etc.
• Building an interactive dashboard with visualizations showing differences in job requirements (by level of experience or education), salary comparison across geographies as well as potential career paths one can pursue within the IT or Data Science fields

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: Job_listing.csv | Column name | Description | |:--------------|:---------------------------------------------------| | Job | The title of the job listing. (String) | | Company | The name of the company offering the job. (String) | | Location | The geographic location of the job. (String) | | Salary | The salary offere...

Graph and download economic data for Quits: Total Nonfarm (JTSQUR) from Dec 2000 to Jan 2026 about quits, nonfarm, and USA.

Graph and download economic data for Hires: Total Nonfarm (JTSHIL) from Dec 2000 to Jan 2026 about hires, nonfarm, and USA.

The Quarterly Census of Employment and Wages (QCEW) program (also known as ES-202) collects employment and wage data from employers covered by New York State's Unemployment Insurance (UI) Law. This program is a cooperative program with the U.S. Bureau of Labor Statistics. QCEW data encompass approximately 97 percent of New York's nonfarm employment, providing a virtual census of employees and their wages as well as the most complete universe of employment and wage data, by industry, at the State, regional and county levels. "Covered" employment refers broadly to both private-sector employees as well as state, county, and municipal government employees insured under the New York State Unemployment Insurance (UI) Act. Federal employees are insured under separate laws, but are considered covered for the purposes of the program. Employee categories not covered by UI include some agricultural workers, railroad workers, private household workers, student workers, the self-employed, and unpaid family workers. QCEW data are similar to monthly Current Employment Statistics (CES) data in that they reflect jobs by place of work; therefore, if a person holds two jobs, he or she is counted twice. However, since the QCEW program, by definition, only measures employment covered by unemployment insurance laws, its totals will not be the same as CES employment totals due to the employee categories excluded by UI. Industry level data from 1975 to 2000 is reflective of the Standard Industrial Classification (SIC) codes.

The Job Market Dataset (Global) is a large-scale, high-quality synthetic dataset containing 500,000 job-related records generated to reflect realistic employment and salary patterns across countries and cities.

Each record includes information about: Location: country, city Job details: occupation, field Work profile: years of experience, employment type Education: education level Company: company size Demographics: gender Compensation: salary Time: year, month

This dataset is designed for learning and research purposes and is especially useful for:

🔍 Exploratory Data Analysis (EDA)

salary distribution analysis pay gaps between occupations / fields location-based salary comparisons employment type behavior patterns

🤖 Machine Learning Projects

salary prediction (regression) clustering job roles by salary and experience classification of job field/occupation patterns

📊 BI & Dashboarding

workforce salary dashboards by country/city occupation and field-wise insights education vs salary trends

✅ Important: This dataset is entirely synthetic and does not include any real employee, payroll, or confidential company data.

In 2025, the employment rate stood at 59.7 percent in the United States. The employment rate decreased compared to 60.1 percent in the previous year.

Official salary statistics from BLS OEWS May 2024 release for Compensation, Benefits, and Job Analysis Specialists in New Hampshire.

Official salary statistics from BLS OEWS May 2024 release for Compensation, Benefits, and Job Analysis Specialists in Wisconsin.

In 2025, the employment rate of the workforce of 55 years and older decreased to 36.9 percent. The employment rate among young adults (age 16-24) was at 50 percent in the same year.

Official salary statistics from BLS OEWS May 2024 release for Compensation, Benefits, and Job Analysis Specialists in California.

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.

Labour statistics by job category, for Canada, the provinces and territories, annual.