Automation and the Changing Composition of Skill Demand

This paper provides new evidence on how automation reshapes firms' demand for skills, not only by changing the occupational composition, but also by reshaping what existing jobs require. Using matched data on firm-level automation investments and detailed job vacancy postings from Denmark, we extract multidimensional skill profiles through natural language processing and decompose changes in skill demand into within- and between-occupation components. Within-occupation adjustment is a quantitatively important margin, accounting for 14-39% of total skill demand change depending on skill type and occupational group. Drawing on a task-based framework that links automation to shifts in multiple skill types within occupations, we estimate the causal effect of automation using a staggered difference-in-differences design. The effects are heterogeneous across the occupational hierarchy: among managers and professionals, automation increases the demand for soft skills, shifting the within-occupation skill mix toward interpersonal and cognitive competencies; among production workers, adjustment operates primarily through reduced hiring rather than changes in skill requirements, while retraining intensity rises by 5 percentage points. Our findings highlight that automation operates through multiple adjustment margins, with implications for training policy and labour market resilience.