Scandinavian Working Papers in Business Administration
Working Papers,
Örebro University, School of Business
No 2023:13: 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 Monteiro (), Hildegunn Kyvik Nordås (), Giuseppe Pulito (), Sarah Schroeder () and Aili Tang ()
Additional contact information
Erik Engberg: Örebro University School of Business, Postal: Örebro University, School of Business, SE - 701 82 ÖREBRO, Sweden
Holger Görg: University of Kiel, Postal: University of Kiel, Kiellinie 66, 24105 Kiel, GERMANY
Magnus Lodefalk: Örebro University School of Business, Postal: Örebro University, School of Business, SE - 701 82 ÖREBRO, Sweden
Farrukh Javed: Lund University, Postal: Lund University, Box 117, 221 00 Lund, Sweden
Martin Längkvist: Örebro University, Postal: Örebro University, Department of Science and Technology, SE - 701 82 ÖREBRO, Sweden
Natália Monteiro: University of Minho, Postal: School of Economics and Management, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal
Hildegunn Kyvik Nordås: Örebro University School of Business, Postal: Örebro University, School of Business, SE - 701 82 ÖREBRO, Sweden
Giuseppe Pulito: Humboldt University, Postal: Faculty of Economics and Business Administration, Humboldt University, Spandauer Straße 1, room 205, 10178 Berlin, Germany
Sarah Schroeder: Aarhus University, Postal: Aarhus University, Fuglesangs Allé 4, 2632, 119, 8210 Aarhus V, Denmark
Aili Tang: Örebro University School of Business, Postal: Örebro University, School of Business, SE - 701 82 ÖREBRO, Sweden
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 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 highskilled 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
44 pages, December 27, 2023
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