Recommending Career Transitions to Job Seekers Using Earnings Estimates, Skills Similarity, and Occupational Demand

Abstract

This paper describes a career recommendation algorithm that uses government administrative data to help job seekers discover new careers that similar job seekers have successfully switched to in the past. Algorithm development was motivated by workers and contractors who were displaced by the COVID-19 economic crisis and by workers in declining industries seeking new careers in growing ones. Traditional job boards available through state government websites list all available jobs but do little to remove uncertainty associated with moving to a new industry or occupation. Our recommendation algorithm can lower this uncertainty. It uses causal machine learning techniques and administrative data on the universe of individual-level employment histories and earnings to identify career transitions that have resulted in increased earnings and employment for previous job seekers. We combine these estimates with measures of skill similarity across occupations, derived from natural-language processing of millions of full-text job descriptions, and with occupational demand, as measured by nightly job posting volume. The algorithm parses applicant resumes and returns recommended careers that use similar skills, have available jobs, and are estimated to lead to higher earnings and employment. We have implemented our algorithm in production workforce development systems in five U.S. states.

Keywords: Recommendation Systems, Future of Work, COVID-19, Unemployment Insurance