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PublicationArticle (with peer review)

The impact of hiring top workers on productivity: what is the role of absorptive capacity?

Abstract

We examine the heterogeneous productivity impacts of hiring top workers on small and medium-sized enterprises, exploiting matched employer–employee panel data and employing within-firm as well as matching and difference-in-difference estimators. The results provide robust evidence that the productivity impact is stronger for firms with higher absorptive capacity. Technological laggards within an industry benefit more strongly from hiring top workers if their workforce is more well-educated.

Lodefalk, M., & Tang, A. (2018). The impact of hiring top workers on productivity: what is the role of absorptive capacity?Applied Economics Letters, 25(20), 1402-1406. DOI: 10.1080/13504851.2017.1422594

Details

Author
Lodefalk, M., & Tang, A.
Publication year
2018
Published in

Applied Economics Letters

Related

  • Associate Professor

    Magnus Lodefalk

    magnus.lodefalk@oru.se

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Working paper

Artificial Intelligence for Public Use

Lodefalk, M.; Engberg, E.; Lidskog, R.; Tang, A.
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Publication year

2025

Published in

Örebro University School of Business Working Paper

Abstract

This paper investigates the economic and societal impacts of Artificial Intelligence (AI) in the public sector, focusing on its potential to enhance productivity and mitigate labour shortages. Employing detailed administrative data and novel occupational exposure measures, we simulate future scenarios over a 20-year horizon, using Sweden as an illustrative case. Our findings indicate that advances in AI development and uptake could significantly alleviate projected labour shortages and enhance productivity. However, outcomes vary substantially across sectors and organisational types, driven by differing workforce compositions. Complementing the economic analysis, we identify key challenges that hinder AI’s effective deployment, including technical limitations, organisational barriers, regulatory ambiguity, and ethical risks such as algorithmic bias and lack of transparency. Drawing from an interdisciplinary conceptual framework, we argue that AI’s integration in the public sector must address these socio-technical and institutional factors comprehensively. To unlock AI’s full potential, substantial investments in technological infrastructure, human capital development, regulatory clarity, and robust governance mechanisms are essential. Our study thus contributes both novel economic evidence and an integrated societal perspective, informing strategies for sustainable and equitable public-sector digitalisation.

Article (with peer review)

Artificial Intelligence and Worker Stress Evidence from Germany

Koch, M., & Lodefalk, M.
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Publication year

2025

Published in

Digital Society, 4(1), 5

Abstract

We use individual survey data providing detailed information on stress, technology adoption, and work, worker, and employer characteristics, in combination with recent measures of AI and robot exposure, to investigate how new technologies affect worker stress. We find a persistent negative relationship, suggesting that AI and robots could reduce the stress level of workers in Germany. We furthermore provide evidence on potential mechanisms to explain our findings. Overall, the paper contributes to the economic literature by providing suggestive evidence of modern technologies changing the way we perform our work in a way that reduces stress and work pressure.

Article (with peer review)

Artificial intelligence, hiring and employment: job postings evidence from Sweden

Engberg, E., Hellsten, M., Javed, F., Lodefalk, M., Sabolová, R., Schroeder, S., & Tang, A

Publication year

2025

Published in

 Applied Economics Letters

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 to 2022 and full-population administrative data for Sweden. We exploit 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.

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