Erik Engberg, Holger Görg, Magnus Lodefalk, Farrukh Javed, Martin Längkvist, Natália Monteiro, Hildegunn Kyvik Nordås, Giuseppe Pulito, Sarah Schroeder and Aili Tang
Additional contact information
Erik Engberg: The Ratio Institute, Postal: The Ratio Institute, P.O. Box 5095, SE-102 42 Stockholm, Sweden
Holger Görg: Kiel Institute
Magnus Lodefalk: The Ratio Institute, Postal: The Ratio Institute, P.O. Box 5095, SE-102 42 Stockholm, Sweden
Farrukh Javed: Lund University
Martin Längkvist: Örebro University
Natália Monteiro: The Ratio Institute, Postal: The Ratio Institute, P.O. Box 5095, SE-102 42 Stockholm, Sweden
Giuseppe Pulito: Berlin School of Economics
Sarah Schroeder: Aarhus University, Postal: The Ratio Institute, P.O. Box 5095, SE-102 42 Stockholm, Sweden
Aili Tang: None
Abstract: 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, including language modelling. According to our model, white collar occupations are most exposed to AI, and espe- cially 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 Swe- den, 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: Artificial intelligence; Labour demand; Multi-country firm-level evidence
JEL-codes: E24; J23; J24; N34; O33
Language: English
46 pages, December 27, 2023
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