AI unboxed and jobs: A novel measure and firm-level evidence from three countries - Erik Engberg; Holger Görg; Magnus Lodefalk; Farrukh Javed; Martin Längkvist; Natália Pimenta Monteiro; Hildegunn Kyvik Nordås; Giuseppe Pulito; Sarah Schroeder; Aili Tang (Rockwool Foundation Berlin (RF Berlin) and Centre for Research & Analysis of Migration (CReAM), 2024)
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.
Keywords: E24; J23; J24; N34; O33; 330; Artificial intelligence; Labour demand; Multi-country firm-level evidence