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, https://www.oru.se/english/employee/erik_engberg
Holger Görg: University of Kiel, Postal: University of Kiel, Kiellinie 66, 24105 Kiel, GERMANY, https://www.ifw-kiel.de/experts/holger-goerg/
Farrukh Javed: Lund University, Postal: Lund University, Box 117, 221 00 Lund, Sweden, https://www.lunduniversity.lu.se/lucat/user/b8c8d0f7315437248f5d744dfc64eb72
Martin Längkvist: Örebro University, Postal: Örebro University, Department of Science and Technology, SE - 701 82 ÖREBRO, Sweden, https://www.oru.se/english/employee/martin_langkvist
Hildegunn Kyvik Nordås: Örebro University School of Business, Postal: Örebro University, School of Business, SE - 701 82 ÖREBRO, Sweden, https://www.oru.se/english/employee/hildegunn_kyvik-nordas
Giuseppe Pulito: Humboldt University, Postal: Faculty of Economics and Business Administration, Humboldt University, Spandauer Straße 1, room 205, 10178 Berlin, Germany, https://www.wiwi.hu-berlin.de/en/Professorships/vwl/microeconomics/people/gpulito
Aili Tang: Örebro University School of Business, Postal: Örebro University, School of Business, SE - 701 82 ÖREBRO, Sweden

No 2023:13, Working Papers from Örebro University, School of Business

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 (search for similar items in EconPapers)
JEL-codes: E24 J23 J24 N34 O33 (search for similar items in EconPapers)
Pages: 44 pages
Date: 2023-12-27
New Economics Papers: this item is included in nep-ain, nep-eur and nep-tid
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Related works:
Working Paper: AI Unboxed and Jobs: A Novel Measure and Firm-Level Evidence from Three Countries (2024)
Working Paper: AI Unboxed and Jobs: A Novel Measure and Firm-Level Evidence from Three Countries (2024)
Working Paper: AI Unboxed and Jobs: A Novel Measure and Firm-Level Evidence from Three Countries (2023)
Working Paper: AI Unboxed and Jobs: A Novel Measure and Firm-Level Evidence from Three Countries (2023)
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