Scandinavian Working Papers in Business Administration

Working Papers,
Örebro University, School of Business

No 2024:10: Artificial Intelligence, Hiring and Employment: Job Postings Evidence from Sweden

Erik Engberg (), Mark Hellsten (), Farrukh Javed (), Magnus Lodefalk (), Radka Sabolová (), 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
Mark Hellsten: Aarhus University, Postal: Aarhus University, Department of Economics, Fuglesangs Alle 4, 8210 Aarhus, Denmark
Farrukh Javed: Lund University, Postal: Lund Universtity, Box 117, SE - 21 00 Lund, Sweden
Magnus Lodefalk: Örebro University School of Business, Postal: Örebro University, School of Business, SE - 701 82 ÖREBRO, Sweden
Radka Sabolová: Örebro University School of Business, Postal: Örebro University, School of Business, SE - 701 82 ÖREBRO, Sweden
Sarah Schroeder: Aarhus University, Postal: Aarhus University, Department of Economics, Fuglesangs Alle 4, 8210 Aarhus, Denmark
Aili Tang: Örebro University School of Business, Postal: Örebro University, School of Business, SE - 701 82 ÖREBRO, Sweden

Abstract: This paper investigates the impact of artificial intelligence (AI) on hiring and employment, using the universe of job postings published by the Swedish Public Employment Service from 2014-2022 and universal register data for Sweden. We construct a detailed measure of AI exposure according to occupational content and find that establishments exposed to AI are more likely to hire AI workers. Survey data further indicate that AI exposure aligns with greater use of AI services. Importantly, rather than displacing non-AI workers, AI exposure is positively associated with increased hiring for both AI and non-AI roles. In the absence of substantial productivity gains that might account for this increase, we interpret the positive link between AI exposure and non-AI hiring as evidence that establishments are using AI to augment existing roles and expand task capabilities, rather than to replace non-AI workers.

Keywords: Artificial Intelligence; Technological Change; Automation; Labour Demand

JEL-codes: D22; J23; J24; O33

Language: English

17 pages, November 7, 2024

Full text files 

wp-10-2024.pdf PDF-file Full text

Download statistics

Questions (including download problems) about the papers in this series should be directed to ()
Report other problems with accessing this service to Sune Karlsson ().

RePEc:hhs:oruesi:2024_010This page generated on 2024-11-09 04:36:10.