The O*NET Program periodically highlights groups of occupations or taxonomies to support initiatives from the U.S. Department of Labor. These projects are described below.
The National Center for O*NET Development has identified "Bright Outlook" occupations, where new job opportunities are likely in the next several years. Bright Outlook occupations are expected to grow rapidly in the next several years, will have large numbers of job openings, or are new and emerging occupations.
Every Bright Outlook occupation matches at least one of the following criteria:
Bright Outlook occupations were initially identified in 2010, using the BLS 2008-2018 employment projections. The list was last revised in 2021, using 2020-2030 projections. These projections do not include impacts of the COVID-19 pandemic and response efforts. For more information, see the BLS summary
Bright Outlook occupations are indicated throughout O*NET OnLine, My Next Move, Mi Próximo Paso, and My Next Move for Veterans. Look for the sun icon () to find occupations where job opportunities are likely in the next several years.
The Browse Bright Outlook feature within O*NET OnLine allows the user to focus in on occupations with high growth or a high number of projected job openings. For each Bright Outlook occupation, the user can view a Summary Report with key details. My Next Move, Mi Próximo Paso, and My Next Move for Veterans also provide the user with easy access to the Bright Outlook careers, so that new job seekers, students, and other career explorers can learn more about promising career opportunities.
My Next Move, Mi Próximo Paso, and My Next Move for Veterans include a career outlook designation for all careers. Each career is listed as having a “Bright,” “Average,” or “Below Average” outlook, based on the Bright Outlook criteria and projected growth from the Bureau of Labor Statistics. Full details are available at About My Next Move.
The O*NET Content Model includes worker- and job-oriented hierarchical taxonomies that can effectively serve as frameworks for organizing workforce competencies, credentials, and other work-relevant information. See below to discover a variety of easy-to-use competency frameworks, including Technology Skills, Abilities, Cross-Functional Skills, Basic Skills, and Knowledge.
The frameworks are available in Excel format and also as JSON-LD: machine-readable Linked Data
external site described using the CTDL-ASN
external site schema (Credential Transparency Description Language Profile of Achievement Standards Network Description Language) developed by the Credential Engine
external site project.
This file contains the hierarchy of Knowledge competencies from the O*NET Content Model.
Includes the framework from the Content Model Reference file and data from the Knowledge file.
This file contains the hierarchy of Basic Skills competencies from the O*NET Content Model.
Includes the framework from the Content Model Reference file and data from the Skills file.
This file contains the hierarchy of Cross-Functional Skills competencies from the O*NET Content Model.
This file contains the hierarchy of Abilities competencies from the O*NET Content Model.
Includes the framework from the Content Model Reference file and data from the Abilities file.
This file contains Technology Skills associated with O*NET-SOC occupations, organized by the United Nations Standard Products and Services Code (UNSPSC).
Includes the framework from the UNSPSC Reference file and data from the Technology Skills file.
This file contains the hierarchy of Work Activities competencies, including generalized, intermediate, and detailed work activities. Linked occupation-specific tasks from across occupations are provided as illustrative or “task examples” related to the activities.
Includes the framework from the Content Model Reference, IWA Reference, and DWA Reference files and data from the Work Activities and Tasks to DWAs files.
During the current COVID pandemic, schools, teachers, students, families, and businesses often rely on a wide variety of emerging technologies to navigate the challenges of decentralized education and work environments. The National Center for O*NET Development has identified over 50 technology skills related to distance learning and remote training.
Related technology skills have been identified in the following categories:
All of these technology skills, and their corresponding occupation linkages, are incorporated within the occupation and career reports included in the O*NET websites (O*NET OnLine; My Next Move; My Next Move for Veterans; Mi Próximo Paso). They may also be found in the Technology Skills file in the O*NET Database, or accessed via O*NET Web Services.
The availability and use of distance learning and remote training technologies will likely continue to expand. To suggest additions to the initial 50-plus identified skills, please contact O*NET Customer Service (email@example.com).
There is a great deal of interest in changing technological, social, and environmental factors and the effect they may have on the future of work. In all likelihood, in the future, new types of jobs will be added, some existing jobs will be lost, and the nature of work in other jobs will change.
For a listing of related studies and articles, see Future of Work: Bibliography of Papers. 1 This listing is developed and maintained by the U.S. Department of Labor, Employment & Training Administration. Last updated February 2022.
Future automation is the focus of many of these studies. Various researchers around the world are examining the potential for automation to impact the world of work using different assumptions and approaches. A number of them use O*NET data on occupational tasks, work activities, and other descriptors as one input.
One research approach developed automation probability levels that group occupations with a similar predicted probability of having parts of the job (such as certain tasks) transition to computer-controlled equipment or software in the next 20 years. These levels serve as one source of information to consider for individuals interested in workforce related research, long-term economic planning, education, program development, or worker preparedness initiatives.
Occupations are grouped into three automation levels:
The automation level information displayed on those linked pages is developed and maintained by Burning Glass Technologies: Labor Insight
external site. Labor Insight’s “Risk of Automation Scores” are based on the seminal Oxford University study on automation, The Future of Employment: How Susceptible are Jobs to Computerisation?
external site That study leveraged O*NET information to assign probability scores to a listing of occupations. Burning Glass Technologies updated and enhanced the study’s initial analysis based on its more granular understanding of skills and occupations. Burning Glass Technologies uses up-to-date, incoming transactional data, as well as O*NET information, to maintain the currency of the automation probability scores.
1 To suggest additions to the listing of Future of Work articles, contact O*NET Customer Service (firstname.lastname@example.org).
The National Center for O*NET Development, as part of its efforts to keep up with the changing world of work, investigated the impact of green economy activities and technologies on occupational requirements and the development of New and Emerging (N&E) occupations. Results of the research led to the identification of green economic sectors, green increased demand occupations, green enhanced skills occupations, and green new and emerging (N&E) occupations.
Major work activities of the green economy cover a broad spectrum. To efficiently and effectively determine the potential occupational implications of green technology, workplace activities were categorized under different green economy sectors.
The impact of green economy activities and technologies is rapidly changing the world of work by affecting worker requirements and occupational demand. A multi-stage research and screening process identified occupations in three general categories, each describing different consequences of green economy activities and technologies on occupational performance:
If you use Green occupations in your own work, you can identify and illustrate them with these images, provided in a variety of sizes for print or electronic media.