CAN WORKERS PERFORMING OCCUPATIONS WITH HIGH RISK OF AUTOMATION BE RETRAINED IN OCCUPATIONS LESS VULNERABLE TO THE AUTOMATION RISK?

Abstract

Modern technology has been changing the manner work is performed in many jobs, leading to wiping out of some jobs and new job creation. Individuals who have lost employment as a result of automation process application at their job positions face the need to change their jobs or professions. The study aims to identify the most viable options for occupational retraining, for transition from occupations with high automation risk as opposed to those of low automation risk, based on the workers’ existing skills, knowledge and experience. The Occupational Information Network database, which classifies and defines various professions, as well as the findings of C.B. Frey and M.A. Osborne, determining the degree of automation risk for each profession, were used for this purpose. The agglomerative method of hierarchical clustering was used to determine the similarity between occupations, in terms of the skills needed to perform them. In this manner, 21 clusters were identified in the population of 579 different occupations. The retraining options were explored within the same industry, in order to enable utilization of employee knowledge and experience. Potential retraining options arising from skill similarity between the occupations were identified for only 33 occupations, within 15 industries. This study findings represent an auxiliary tool in the search for retraining options for workers at risk of losing employment due to their job automation.