Identifying Alternative Occupations for Truck Drivers Displaced Due to Autonomous Vehicles by Leveraging the O*NET Database

Abstract

Automation continues to be a disruptive force in the workforce. In particular, new automated technologies are projected to replace many mid-skill jobs, potentially displacing millions of workers. Career planning agencies and other organizations can help support workers if they are able to effectively identify optimal transition occupations for displaced workers. We drew upon the 24.2 Occupational Information Network (O*NET) Database to conduct two related studies that identify alternate occupations for truck drivers, who are at risk of job loss due to the adoption of autonomous vehicles. In Study 1, we statistically compared the jobs that we identified based on different search methods using O*NET classifications based on their similarity to the knowledge, skills, values, and interests held by truck drivers. In Study 2, we conducted a survey of truck drivers to evaluate their perceptions of the occupations identified as objectively similar to their occupation. Results indicate that optimal transition occupations may be identified by searching for occupations that share skills as well as the same work activities/industry as a given occupation. These findings hold further implications for career planning organizations and policymakers to ease workforce disruption due to automation.