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PublicationArticle (with peer review)

Immigrant employment and the contract enforcement costs of offshoring

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Abstract

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.

Details

Author
Hatzigeorgiou, A., Karpaty, P., Kneller, R., & Lodefalk, M.
Publication year
2024
Published in

Review of World Economics, 1-29.

Related

  • Associate Professor

    Magnus Lodefalk

    magnus.lodefalk@oru.se

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Article (with peer review)

Artificial Intelligence for Public Use

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

2026

Published in

Digital Society

Abstract

The public sector faces rising expectations, demographic change and persistent efficiency challenges. This article examines both the potential and limitations of AI in public services. Using an interdisciplinary conceptual framework, it analyses organisational, regulatory, technical and ethical barriers. It also simulates AI’s possible long-term effects on labour demand and productivity in Sweden using administrative data and occupational AI-exposure measures. The scenarios suggest that AI could help ease labour shortages and raise productivity, although effects vary substantially across subsectors and types of organisation. The results are scenarios, not forecasts.

Article (without 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

Publication year

2026

Published in

Zenodo

Abstract

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.

Reports

Who is afraid of AI? Who should be?

Engberg, E., Görg, H., Hellsten, M., Javed, F., Lodefalk, M., Längkvist, M., & ..

Publication year

2026

Published in

Kiel Policy Brief, 2026.

Abstract

  • Occupations that are highly cognitive, non-physical, and low in social interaction — typically higher-skill white-collar roles such as data analysts, software developers, and translators — turn out to be highly AI-exposed
  • Occupations requiring manual dexterity or intensive interpersonal contact — such as construction labourers or nursing aides — remain among the least exposed to current AI technologies
  • Aggregate occupational exposure to AI has risen markedly since 2010, with especially rapid gains in the late 2010s and early 2020s
  • Our baseline estimates show no detectable effect of AI exposure on total firm employment, while it is associated with clear skill upgrading
    1. Engberg, E., Görg, H., Hellsten, M., Javed, F., Lodefalk, M., Längkvist, M., & .. (2026). Who is afraid of AI? Who should be?. Kiel Policy Brief, 2026.
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