Equalising the Effects of Automation? The Role of Task Overlap for Job Finding

This paper investigates whether task overlap can equalise the distributional effects of automation for unemployed job seekers displaced from routine jobs. Using a language model, we establish a novel job-to-job task similarity measure. Exploiting the resulting job network to define job markets flexibly, we find that only the most similar jobs affect job finding. Since automation-exposed jobs overlap with other highly exposed jobs, task-based reallocation provides little relief for affected job seekers. We show that this is not true for more recent software exposure, for which task overlap lowers the inequality in job finding. • Similar jobs offer little relief from reduced job finding due to automation exposure. • The job similarity network layout explains low job finding for routine job seekers. • Routine jobs are segregated from less exposed jobs with better prospects. • The impact of task overlap on job finding varies across different technologies.