The Role of Occupational Automation Exposure Scores
Simply, occupational scores of automation exposure answer “what jobs have tasks that are the most exposed to automation technologies?”. The LMI Institute, in partnership with the Council for Community and Economic Research (C2ER), produces Automation Exposure Scores derived from intensity scores of selected O*Net Abilities, Work Activities, and Work Context characteristics associated with technological substitution. These scores are intended to help education, labor market information, and economic and workforce development officials identify the occupations and tasks that are the most likely to be most complemented, or substituted, by automation technologies. As Industry 4.0 technologies like artificial intelligence and robotics become more widely adopted by employers, it will become increasingly important that the workforce of the future can operate and cooperate with the technologies of the 21st century.
The Automation Exposure Scores allow analysts to identify industries and occupations that are changing or likely to change due to automation and to identify opportunities for skill and career transitions for threatened occupations. Industry, educators, and the civic sector can collaborate on delivering solutions that provide at-risk industries and occupations with the business services, training, and career transition-services necessary to support their resiliency and growth. While automation threatens to replace workers, it also presents opportunities for regions to embrace new and emerging occupational skillsets that provide workers with family-supporting wages and increase the resiliency of key regional industries.
The LMI Institute’s Automation Exposure Scores measure the intensity score of selected O*Net characteristics with potential for technological substitution. Scores derived from the 18,300 tasks associated with the 800+ O*Net occupations would be too burdensome to accurately review and characterize on an ongoing basis, especially as tasks are constantly evolving due to technological changes. Instead, the sixteen work characteristics utilized in these scores are significantly statistically associated with changes in occupational employment and skill-composition resulting from the adoption of automation technologies. This method was developed to produce Automation Exposure Scores that are easily updateable and transparent.
A score is calculated for each work characteristic and averaged across five categories: Cognitive Analytical and Cognitive Interpersonal scores indicate reduced exposure to automation; Routine Cognitive, Routine Manual, and Nonroutine Manual scores indicate increased exposure to, and potential substitution by, automation technologies. The routine/manual category scores are subtracted from the cognitive/abstract score to get the difference between the two. The range of abstract-routine differences across all occupations are then divided into 10 evenly spaced intervals, and the occupations are then categorized with a score from 1 to 10, with 1 being the least exposed to automation and 10 being the most exposed.
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