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PublicationArticle (with peer review)

Migrants’ Influence on Firm-level Exports

Abstract

We examine the role of migrants in trade using a firm-level approach. We exploit a new employer–employee panel for Sweden, which encompasses close to 600,000 full-time employees, approximately 12,000 firms and data for 176 countries for the period 1998–2007. The resulting analysis provides novel firm-level evidence on the trade-migration relationship. Foreign-born workers have a positive association with firm exports. However, immigrants do not have an unconditional positive impact on firm trade. Mainly small firms gain from hiring foreign-born workers, and migrants need to be skilled and recently arrived to have a clear positive impact on firm export performance.

Hatzigeorgiou, A., & Lodefalk, M. (2016). Migrants’ Influence on Firm-level Exports. Journal of Industry, Competition and Trade, 16(4), 477–497. DOI: 10.1007/s10842-015-0215-7

Details

Author
Hatzigeorgiou, A., & Lodefalk, M.
Publication year
2016
Published in

Journal of Industry Competition and Trade

Related

  • Associate Professor

    Magnus Lodefalk

    magnus.lodefalk@oru.se

Similar content

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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