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PublikationArtikel (med peer review)

Immigrant employment and the contract enforcement costs of offshoring

Sammanfattning

Offshoring continues to be an important dimension of firms’ internationalization choices. However, offshoring also increases contract enforcement costs by inhibiting the coordination and monitoring of performance. Immigrant employees may reduce such costs through their specific knowledge of the employer, their country of birth and access to foreign networks. In this paper, we investigate the role of immigrant employees within firms on firm offshoring, employing rich administrative Swedish microlevel data that include specific information about the characteristics of employees, manufacturing firms and their bilateral offshoring. Our results support the hypothesis that immigrant employees increase offshoring by lowering contract enforcement costs. Hiring one additional immigrant employee is linked to a relatively larger increase in offshoring at the intensive than the extensive margin, on average. The association to offshoring is considerably stronger for skilled immigrant employees and for contract and R&D intensive offshoring. Instrumental variable estimations demonstrate qualitatively similar results, while a placebo test with randomized immigrant employment does not generate any link between immigrants and offshoring.

Hatzigeorgiou, A., Karpaty, P., Kneller, R., & Lodefalk, M. (2024). Immigrant employment and the contract enforcement costs of offshoring. Review of World Economics, 1-29.

Detaljer

Författare
Hatzigeorgiou, A., Karpaty, P., Kneller, R., & Lodefalk, M.
Publiceringsår
2024
Publicerat i

Review of World Economics, 1-29.

Relaterat

  • Docent

    Magnus Lodefalk

    magnus.lodefalk@oru.se

Liknande innehåll

Artikel (med peer review)

Artificial Intelligence for Public Use

Lodefalk, M.; Engberg, E.; Lidskog, R.; Skargren, F.; Tang, A.
Ladda ner

Publiceringsår

2026

Publicerat i

Digital Society

Sammanfattning

Offentlig sektor står inför ökande förväntningar, demografiska förändringar och återkommande effektivitetsproblem. Artikeln undersöker både möjligheter och begränsningar med AI i offentlig verksamhet. Med ett tvärvetenskapligt ramverk analyseras organisatoriska, rättsliga, tekniska och etiska hinder. Författarna simulerar också AI:s möjliga långsiktiga effekter på arbetskraftsbehov och produktivitet i Sverige med hjälp av administrativa data och mått på yrkens AI-exponering. Scenarierna tyder på att AI kan bidra till att minska arbetskraftsbrist och öka produktiviteten, men effekterna varierar mellan verksamhetsområden och organisationstyper. Resultaten är scenarier, inte prognoser.

Artikel (utan peer review)

DAIOE: Dynamic AI Occupational Exposure (dataset and construction pipeline)

E. Engberg, H. Görg, M. Hellsten, F. Javed, M. Lodefalk, M. Längkvist, N. Monteiro, H. Kyvik Nordås, G. Pulito, S. Schroeder & A. Tang

Publiceringsår

2026

Publicerat i

Zenodo

Sammanfattning

DAIOE is a dataset of occupation-year measures of exposure to artificial intelligence, accompanied by its construction pipeline and documentation. The index combines progress across public AI benchmarks with occupational ability profiles to estimate how exposure changes over time. The dataset covers multiple occupational classifications, including Swedish SSYK. It is a research data resource, not a peer-reviewed article.

Working paper

Who Adopts AI? Evidence on Firms, Technologies and Workers

Pulito, G., Pytlikova, M., Schroeder, S. & Lodefalk, M.

Publiceringsår

2026

Publicerat i

GLO Discussion Paper

Sammanfattning

Using two waves of nationally representative Danish firm surveys linked to employer–employee administrative registers, we study how adoption varies across artificial intelligence (AI) and related advanced technologies. We show that AI adoption is highly technology-specific. While firm size and digital infrastructure predict adoption broadly, workforce composition operates through distinct channels: STEM-educated workforces predict core AI adoption, whereas non-STEM university-educated workforces are associated with generative AI adoption, indicating different human capital complementarities. The factors associated with adoption differ from those predicting deployment breadth: firm size and digital maturity matter for both, whereas workforce composition primarily predicts adoption alone. Machine learning and natural language processing are deployed across multiple business functions, whereas other advanced technologies remain concentrated in specific operational domains. Individual-level evidence provides a foundation for these patterns, with awareness of workplace AI usage concentrated among managers and high-skilled workers. Self-reported AI knowledge is higher among younger and more educated individuals. Finally, commonly used occupational AI exposure measures vary substantially in their ability to predict observed adoption, with benchmark-based measures outperforming patent-based and large-language-model-focused alternatives. These findings show that treating AI as a monolithic category obscures economically meaningful variation in who adopts, what they deploy, and how well existing measures capture it.

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