Using Graph Theory to Optimize Career Transitions

Abstract

Abstract Grounded in graph theory, this paper proposes and demonstrates a novel methodology to analyze career transitioning. We collect and integrate official U.S. Government data on 35 general job skills and the annual wage data for over 900 standard occupations. Our research can help people move from unemployment, or a current job, to their desired occupation. We use graph theory to determine the most efficient way to hop between intermediate jobs to gain the necessary set of skills required by the targetted occupation. Our analysis assumes that working in a job proffers the skills from that job to the employee. A potential application involves an employee who wishes to transition to a different occupation, perhaps even in a different industry. The employee does not have the necessary skills to transition directly to the desired career because the skill levels are too different between the jobs. Instead, the employee must make a series of smaller job hops to acquire the skills. This type of analysis can provide valuable insights into the most efficient way to change careers. Our study may be especially relevant and helpful because some employees may need to move from languishing careers or industries to ones less impacted by COVID 19 or less threatened by automation.