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.
Criteria
Every Bright Outlook occupation matches at least one of the following criteria:
-
Projected to grow rapidly —
These occupations are projected to grow faster than average (employment increase of 6% or more) over the period 2023-2033 for the US nationwide. Projected growth represents the estimated change in total employment over the projections period, as published by the Bureau of Labor Statistics
external site. The "faster than average" designation comes from the Occupational Outlook Handbook
external site.
-
Projected to have large numbers of openings —
These occupations are projected to have 100,000 or more job openings over the period 2023-2033 for the US nationwide. Projected job openings represent openings due to growth and replacement, as estimated by the Bureau of Labor Statistics
external site.
-
New and Emerging occupations —
These four occupations in the cybersecurity industry were introduced in the O*NET-SOC 2019 taxonomy. For more information, see Updating the O*NET-SOC Taxonomy: Incorporating the 2018 SOC Structure.
Bright Outlook occupations were initially identified in 2010, using the BLS 2008-2018 employment projections. The list was last revised in 2024, using 2023-2033 projections.
Bright Outlook in O*NET websites
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.
Occupational Listings
- Current:
- Past:
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.
Knowledge Competency Framework
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.
Basic Skills Competency Framework
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.
Cross-Functional Skills Competency Framework
This file contains the hierarchy of Cross-Functional Skills competencies from the O*NET Content Model.
Includes the framework from the Content Model Reference file and data from the Skills file.
Abilities Competency Framework
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.
Technology Skills Competency Framework
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.
Work Activities Competency Framework
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.
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:
- High — These occupations are in the upper quartile of automation probability.
- Medium — These occupations are in the third quartile of automation probability.
- Low — These occupations are in the bottom half of automation probability.
The automation level information displayed on those linked pages was developed by Burning Glass Technologies: Labor Insight, now part of Lightcast
external site. Labor Insight’s “Risk of Automation Scores” were 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.
Growing emphasis on “green” or environmentally friendly activities has a widespread impact on the world-of-work. This goes beyond a specific subset of “green jobs.” Instead, concepts such as sustainability, climate adaptation, conservation, energy efficiency, and transportation touch on a broad range of occupations across the U.S. economy.
The National Center for O*NET Development implemented a new, topical approach to the greening of the world-of-work. Occupations and education programs linked to “green topics” are identified. The initial phase of this work takes advantage of previous O*NET green research, while primarily using a linguistic approach to identify connections between green topics and 1) occupations, and 2) instructional programs. For more information, see the report Green Topics: Identifying Linkages to Occupations and Education Programs Using a Linguistic Approach.
In the future, additional research will be conducted to refine and extend the green-related information.
Explore a listing of Green Topics now!
