Research
Research
Working papers
This paper examines how offshoring affects firms’ organization and skill composition, constructing an instrument to address the endogeneity of the offshoring decision. The findings indicate that offshoring positively affects employment and productivity for Danish firms. Firms adjust their skill composition through two distinct channels on different segments of their task structure: retraining manual workers for less production-specific roles and hiring younger, more educated workers for cognitive tasks. The results suggest that internal labor markets and retraining can facilitate structural transformations, though they may not fully address skill gaps arising from changes in task demand.
This paper provides new evidence on how automation transforms firms’ demand for skills, not by changing the occupational composition, but by reshaping what existing jobs require. Using matched data on firm-level automation investments and detailed job ads from Denmark, we extract multidimensional skill profiles through natural language processing. Guided by a task-based framework, we decompose changes in skill demand into within- and between-occupation components and find that within-occupation adjustments dominate. Automation increases the demand for soft skills in professional and managerial roles and reduces the reliance on routine hard skills in production jobs. Register data confirm parallel shifts in workforce composition: increased experience in high-skill occupations and intensified retraining among lower-skilled workers. Our findings highlight that automation reconfigures work from within, with important implications for training policy and labor market resilience.
IZA DP No. 16717; RFBerlin DP 14/24
We unbox developments in artificial intelligence (AI) to estimate how exposure to these developments affect firm-level labour demand, using detailed register data from Denmark, Portugal and Sweden over two decades. Based on data on AI capabilities and occupational work content, we develop and validate a time-variant measure for occupational exposure to AI across subdomains of AI, such as language modelling. According to the model, white collar occupations are most exposed to AI, and especially white collar work that entails relatively little social interaction. We illustrate its usefulness by applying it to near-universal data on firms and individuals from Sweden, Denmark, and Portugal, and estimating firm labour demand regressions. We find a positive (negative) association between AI exposure and labour demand for high-skilled white (blue) collar work. Overall, there is an up-skilling effect, with the share of white-collar to blue collar workers increasing with AI exposure. Exposure to AI within the subdomains of image and language are positively (negatively) linked to demand for high-skilled white collar (blue collar) work, whereas other AI-areas are heterogeneously linked to groups of workers.
Selected Work in Progress
Who Adopts AI? Evidence from Firms and Workers in Denmark with Hildegunn Kyvik Nordås, Magnus Lodefalk, Mariola Pytlikova, Sarah Schroeder
The Employment Effects of AI Adoption with Mariola Pytlikova, Sarah Schroeder