Equalising the effects of automation? The role of task overlap for job finding
利用语言模型构建任务相似度指标，发现自动化暴露岗位的任务重叠度高，导致被替代的求职者难以通过任务转移找到新工作，但软件暴露的影响则不同。
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.