Long-term Occupational Projections for a 10-year time horizon are provided for the State and its labor market regions to provide individuals and organizations with an occupational outlook to make informed decisions on individual career and organizational program development. Long-term projections are revised annually. Data are not available for geographies below the labor market regions. Detail may not add to summary lines due to suppression of data because of confidentiality and/or quality.

The Employment Projections (EP) program develops information about the labor market for the Nation as a whole for 10 years in the future. For more information visit: https://www.bls.gov/emp/

This statistic shows the projected number of workers in the finance and insurance industry in the United States in 2019 and 2026, by size of firm. By 2026, the finance and insurance industry in the U.S. is projected to have more than 2.94 million workers at firms employing 10,000 people or more.

Short-term Occupational Projections for a 2-year time horizon are produced for the State to provide individuals and organizations with an occupational outlook to make informed decisions on individual career and organizational program development. Short-term projections are revised annually. Data are not available for geographies below the state level, including labor market regions. Data is based on second quarter averages and may be subject to seasonality. Detail may not add to summary lines due to suppression of data because of confidentiality and/or quality.

The 3-year Employment Outlooks consist of a rating (very good, good, moderate, limited or very limited) of the employment prospects as well as a narrative text that provides an assessment of the main forecast indicators, recent statistics, and value-added regional observations. Employment Outlooks are developed for each detailed occupation in all provinces, territories and economic regions of Canada, where data permits. They are updated annually. The Employment Outlooks developed until the 2015-2017 period were assessed on the basis of the National Occupational Classification (NOC) 2006, and include up to 520 occupations. Beginning with the 2016-2018 Outlooks, the NOC 2011 is used for the analysis and the Outlooks include up to 500 occupations. Outlooks and trend descriptions for the latest year (currently disseminated on Job Bank) are subject to change as new information becomes available. Every effort will be made to keep the records on the Open Data Portal as up to date as possible, though delays may occur. If you have comments or questions regarding the 3-year Employment Outlooks, please contact the Labour Market Information division at: NC-LMI-IMT-GD@hrsdc-rhdcc.gc.ca

The Employment Sector, a sector attached to the Ministry of Employment and Social Solidarity (MESS), makes forecasts of the labour market every year in the medium (five years) and in the long term (ten years). These forecasts cover employment by industry and occupation, as well as labour market participation and unemployment. They are based on medium and long-term economic forecasts from the Conference Board of Canada (cBDC), including those for household consumption, government spending, private and public investments, exports and imports, and exports, and imports, and exchange rate trends. If the economic growth observed during the forecast period is different from that expected by the cBDC, the evolution of employment could thus differ from what the Employment Sector foresees in its forecasts.

The Australian Government Department of Jobs and Small Business publishes a range of labour market data on its Labour Market Information Portal website (lmip.gov.au).

The link below provides data for the employment projections by industry, occupation, skill level and region for the following five year period. Produced by the Department of Employment, these employment projections are designed to provide a guide to the future direction of the labour market, however, like all such exercises, they are subject to an inherent degree of uncertainty.

United States US: Employment Rate: Age 15-74 data was reported at 67.118 % in 2026. This records an increase from the previous number of 66.828 % for 2025. United States US: Employment Rate: Age 15-74 data is updated yearly, averaging 65.646 % from Dec 1985 (Median) to 2026, with 42 observations. The data reached an all-time high of 69.750 % in 2000 and a record low of 61.414 % in 2010. United States US: Employment Rate: Age 15-74 data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s United States – Table US.OECD.EO: Employment and Unemployment: Forecast: OECD Member: Annual.

Private businesses in the United States hired 37 thousand workers in May of 2025 compared to 60 thousand in April of 2025. This dataset provides the latest reported value for - United States ADP Employment Change - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Graph and download economic data for 12-Month Moving Average of Unweighted Median Hourly Wage Growth: Job Stayer (FRBATLWGT12MMUMHWGJST) from Dec 1997 to Apr 2025 about growth, moving average, 1-year, jobs, average, wages, median, and USA.

This statistic shows the projected number of workers in the real estate, rental and leasing industry in the United States in 2019 and 2026, by size of firm. By 2026, the real estate, rental and leasing industry in the U.S. is projected to have 373,218 workers at firms employing 10,000 people or more.

Employment in Canada increased by 8.80 in May of 2025. This dataset provides the latest reported value for - Canada Employment Change - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Job growth is often used as a measure of economic expansion and health. The city's job growth consistently exceeds local competitors. Future job growth in Henderson is predicted to be 42.1%, higher than the US average of 33.5%. Note: Most current US Census data is 2018.

Dataset Overview

The "AI and ML Job Listings USA" dataset provides a comprehensive collection of job postings in the field of Artificial Intelligence (AI) and Machine Learning (ML) across the United States. The dataset includes job listings from 2022 to 2024, capturing the evolving landscape of AI/ML job opportunities. This dataset is valuable for researchers, job seekers, and data scientists interested in understanding trends, demands, and opportunities in the AI/ML job market.

Data Science Applications

This dataset can be utilized for various data science applications, including: - Trend Analysis: Identifying trends in job titles, locations, and required skills over time. - Demand Forecasting: Predicting future demand for AI/ML roles based on historical data. - Skills Gap Analysis: Analyzing the skills and experience levels in demand versus the available workforce. - Geospatial Analysis: Mapping job opportunities across different regions in the USA. - Salary Prediction: Developing models to predict salaries based on job descriptions and other attributes. Some job descriptions include salary information, which can be identified by exploring the 'description' column for mentions of compensation, pay, or salary-related terms.

Column Descriptors

1. title: The job title (e.g., AI/ML Engineer).
2. location: The location of the job (e.g., New York, NY).
3. publishedAt: The date the job was published (e.g., 2024-05-29).
4. companyName: The name of the company offering the job (e.g., Wesper).
5. description: A detailed description of the job (e.g., responsibilities, qualifications, and sometimes salary information).
6. applicationsCount: The number of applications received (e.g., Over 200 applicants).
7. contractType: The type of contract (e.g., Full-time).
8. experienceLevel: The level of experience required (e.g., Mid-Senior level).
9. workType: The type of work (e.g., Engineering and Information Technology).
10. sector: The industry sector of the job (e.g., Internet Publishing).

Ethically Mined Data

This dataset has been ethically mined using an API, ensuring no private information has been revealed. Sensitive data, such as the recruiter name, has been removed to protect privacy and comply with ethical standards.

Acknowledgments

• LinkedIn: For providing the platform where these job listings were originally posted.
• DALL·E 3: For generating the thumbnail image used for this dataset.

This dataset provides a rich resource for analyzing and understanding the AI and ML job market in the USA, offering insights into job trends, requirements, and opportunities in this rapidly growing field.

As a part of DVRPC’s long-range planning activities, the Commission is required to maintain forecasts with at least a 20-year horizon. DVRPC has updated forecasts through the horizon year of the 2050 Long-Range Plan. The 2050 Version 2.0 Population and Employment Forecasts (2050 Version 2.0, v2.0) were adopted by the DVRPC Board on October 24, 2024, They update the 2050 v1.0 forecasts with a new county-level age-cohort model and new base data from the 2020 Decennial Census, 2020 Bureau of Economic Analysis (BEA), and 2021 National Establishments Time Series (NETS). The age-cohort model calculates future population for five year age-sex cohorts using the 2020 Census base population, and anticipated birth, death, and migration rates. These anticipated rates were developed using historic birth and death records from New Jersey and Pennsylvania state health department data, as well as historic net migration data, calculated from decennial census data. Employment forecasts were developed by multiplying population forecasts by a ratio of working age population to jobs, calculated from 2022 ACS and BEA data. The municipal and TAZ forecasts use the growth factors from the v1.0 forecasts, scaled to the new county and regional population totals from the age-cohort model. While the forecast is not adopted at the transportation analysis zone (TAZ) level, it is allocated to these zones for use in DVRPC’s travel demand model, and conforms to municipal/district level adopted totals. This data provides TAZ-level population and employment. Other travel model attributes are available upon request. DVRPC has prepared regional- and county-level population and employment forecasts in five-year increments for years 2020–2050. 2019 land use model results are also available. A forthcoming Analytical Data Report will document the forecasting process and methodologies.

Job Search Engines Market size was valued at USD 19.14 Billion in 2024 and is projected to reach USD 105.4 Billion by 2031, growing at a CAGR of 14.2% during the forecast period 2024-2031.

Rising Employment Opportunities: As economies around the world expand, businesses scale up operations, subsequently creating more job opportunities. This growth in employment facilitates a greater need for efficient job search engines to match job seekers with potential employers. Certain sectors such as technology, healthcare, and renewable energy are growing rapidly, leading to an increase in job vacancies. Specialized job search engines cater to these niches, driving market growth. Populous countries with large, young workforces contribute to the increased number of job seekers utilizing job search engines. Growing Internet Penetration: As internet access becomes more widespread globally, more individuals can use online platforms, including job search engines. This is particularly notable in developing regions where internet adoption is accelerating. Lower costs of internet services and devices have made it more feasible for a broader audience to go online, boosting the user base for job search engines. The availability of high-speed internet makes the use of job search engines more convenient and effective, supporting features such as real-time notifications and the ability to upload and download large files (e.g., resumes and portfolios). Shift to Digital Recruitment: The integration of data analytics and AI in recruitment processes enables job search engines to offer more personalized and streamlined experiences for both job seekers and employers. Improved algorithms and machine learning facilitate better job-candidate matching, increasing the effectiveness and appeal of digital recruitment platforms. Digital platforms reduce the costs associated with traditional recruitment methods (e.g., print advertising and in-person job fairs). Employers benefit from decreased hiring costs, while job search engines profit from increased business. Increased Mobile Device Usage: With the global proliferation of smartphones, a significant portion of job searches is conducted via mobile apps and mobile-optimized websites. Job search engines that offer robust mobile platforms are experiencing higher engagement. Mobile devices provide unparalleled flexibility and convenience, allowing users to search for jobs, set up alerts, and apply for positions from anywhere, at any time. Innovative mobile apps designed by job search engines offer features such as GPS-based job searches, voice and video interviews, and chat support, which enhance the user experience. Technological Advancements: Innovations in AI, machine learning, and big data analytics enhance the functionality of job search engines, providing personalized job recommendations and improving match accuracy.

Remote Work Trends: The rise of remote work opportunities, especially post-pandemic, has increased the demand for job search engines that specialize in remote and freelance job listings.

Employer Branding: Companies use job search engines to build and promote their employer brand, attracting top talent by showcasing their work culture, benefits, and career opportunities.

Government Initiatives: Supportive government policies and initiatives aimed at reducing unemployment and promoting job creation boost the usage of job search engines.

Gig Economy Growth: The expanding gig economy, characterized by short-term contracts and freelance work, drives the need for specialized job search engines catering to gig workers.

Globalization and Cross-Border Employment: Increasing globalization and the trend of cross-border employment necessitate job search engines that facilitate international job searches and candidate sourcing.

This dataset provides a comprehensive view of the job market in California, highlighting companies and cities with the highest number of job opportunities. Created by JoPilot, it contains valuable information for anyone interested in the employment landscape across different industries and regions. It includes key information such as:

• Company name • City • State • Number of active jobs

For job seekers, employers, and researchers, this resource can be particularly useful in several ways:

1. Identifying hot job markets: The data highlights cities with the highest number of job openings, helping job seekers focus their search on areas with more opportunities.
2. Company targeting: By showing which companies have the most active job listings, the dataset allows job seekers to target their applications to organizations that are actively hiring.
3. Industry trends: The information can reveal which industries or sectors are experiencing growth in California, guiding career decisions and educational pursuits.
4. Regional comparisons: Users can compare job markets across different California cities and regions, which is valuable for those considering relocation or analyzing economic trends.
5. Skill alignment: While the dataset doesn't directly provide skill requirements, it can be used alongside other resources to align job seekers' skills with in-demand positions.

For a more comprehensive job search strategy, consider complementing this dataset with additional resources such as the California Labor Market Information tools, which offer detailed insights into wages, employment projections, and industry-specific data.

Full Time Employment in the United States decreased to 134840 Thousand in May from 135463 Thousand in April of 2025. This dataset provides - United States Full Time Employment- actual values, historical data, forecast, chart, statistics, economic calendar and news.

State Of Utah Employment Projections By County And Multi- County District 1980-2030

Every four years, the Wasatch Front’s two metropolitan planning organizations (MPOs), Wasatch Front Regional Council (WFRC) and Mountainland Association of Governments (MAG), collaborate to update a set of annual small area -- traffic analysis zone and ‘city area’, see descriptions below) -- population and employment projections for the Salt Lake City-West Valley City (WFRC), Ogden-Layton (WFRC), and Provo-Orem (MAG) urbanized areas.

These projections are primarily developed for the purpose of informing long-range transportation infrastructure and services planning done as part of the 4 year Regional Transportation Plan update cycle, as well as Utah’s Unified Transportation Plan, 2023-2050. Accordingly, the foundation for these projections is largely data describing existing conditions for a 2019 base year, the first year of the latest RTP process. The projections are included in the official travel models, which are publicly released at the conclusion of the RTP process.

Projections within the Wasatch Front urban area ( SUBAREAID = 1) were produced with using the Real Estate Market Model as described below. Socioeconomic forecasts produced for Cache MPO (Cache County, SUBAREAID = 2), Dixie MPO (Washington County, SUBAREAID = 3), Summit County (SUBAREAID = 4), and UDOT (other areas of the state, SUBAREAID = 0) all adhere to the University of Utah Gardner Policy Institute's county-level projection controls, but other modeling methods are used to arrive at the TAZ-level forecasts for these areas.

As these projections may be a valuable input to other analyses, this dataset is made available here as a public service for informational purposes only. It is solely the responsibility of the end user to determine the appropriate use of this dataset for other purposes.

Wasatch Front Real Estate Market Model (REMM) Projections

WFRC and MAG have developed a spatial statistical model using the UrbanSim modeling platform to assist in producing these annual projections. This model is called the Real Estate Market Model, or REMM for short. REMM is used for the urban portion of Weber, Davis, Salt Lake, and Utah counties. REMM relies on extensive inputs to simulate future development activity across the greater urbanized region. Key inputs to REMM include:

Demographic data from the decennial census
County-level population and employment projections -- used as REMM control totals -- are produced by the University of Utah’s Kem C. Gardner Policy Institute (GPI) funded by the Utah State Legislature
Current employment locational patterns derived from the Utah Department of Workforce Services
Land use visioning exercises and feedback, especially in regard to planned urban and local center development, with city and county elected officials and staff
Current land use and valuation GIS-based parcel data stewarded by County Assessors
Traffic patterns and transit service from the regional Travel Demand Model that together form the landscape of regional accessibility to workplaces and other destinations
Calibration of model variables to balance the fit of current conditions and dynamics at the county and regional level

‘Traffic Analysis Zone’ Projections

The annual projections are forecasted for each of the Wasatch Front’s 3,546 Traffic Analysis Zone (TAZ) geographic units. TAZ boundaries are set along roads, streams, and other physical features and average about 600 acres (0.94 square miles). TAZ sizes vary, with some TAZs in the densest areas representing only a single city block (25 acres).

‘City Area’ Projections

The TAZ-level output from the model is also available for ‘city areas’ that sum the projections for the TAZ geographies that roughly align with each city’s current boundary. As TAZs do not align perfectly with current city boundaries, the ‘city area’ summaries are not projections specific to a current or future city boundary, but the ‘city area’ summaries may be suitable surrogates or starting points upon which to base city-specific projections.

Summary Variables in the Datasets

Annual projection counts are available for the following variables (please read Key Exclusions note below):

Demographics

Household Population Count (excludes persons living in group quarters) 
Household Count (excludes group quarters) 

Employment

Typical Job Count (includes job types that exhibit typical commuting and other travel/vehicle use patterns)
Retail Job Count (retail, food service, hotels, etc)
Office Job Count (office, health care, government, education, etc)
Industrial Job Count (manufacturing, wholesale, transport, etc)
Non-Typical Job Count* (includes agriculture, construction, mining, and home-based jobs) This can be calculated by subtracting Typical Job Count from All Employment Count 
All Employment Count* (all jobs, this sums jobs from typical and non-typical sectors).

• These variables includes REMM’s attempt to estimate construction jobs in areas that experience new and re-development activity. Areas may see short-term fluctuations in Non-Typical and All Employment counts due to the temporary location of construction jobs.

Key Exclusions from TAZ and ‘City Area’ Projections

As the primary purpose for the development of these population and employment projections is to model future travel in the region, REMM-based projections do not include population or households that reside in group quarters (prisons, senior centers, dormitories, etc), as residents of these facilities typically have a very low impact on regional travel. USTM-based projections also excludes group quarter populations. Group quarters population estimates are available at the county-level from GPI and at various sub-county geographies from the Census Bureau.

Statewide Projections

Population and employment projections for the Wasatch Front area can be combined with those developed by Dixie MPO (St. George area), Cache MPO (Logan area), and the Utah Department of Transportation (for the remainder of the state) into one database for use in the Utah Statewide Travel Model (USTM). While projections for the areas outside of the Wasatch Front use different forecasting methods, they contain the same summary-level population and employment projections making similar TAZ and ‘City Area’ data available statewide. WFRC plans, in the near future, to add additional areas to these projections datasets by including the projections from the USTM model.