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PublicationArticle (with peer review)

Trade, Migration and Integration – Evidence and Policy Implications

Abstract

This paper takes as its point of departure the unique position recently adopted by Swedish policymakers emphasising migration as a tool to increase trade. We attempt to empirically scrutinise this position. Our results demonstrate that migrants stimulate exports, especially along the extensive product margin of trade and for differentiated products, but have no significant impact on imports. This finding suggests that for small open economies where numerous immigrants are refugees, the strategy of using migration to facilitate trade may only be effective with respect to exports. This paper also contributes to the literature on trade and migration by exploiting data on gender and age, which allow us to draw inferences on the underlying impact channels. We adopt an instrumental variable approach to address the endogeneity issue due to potential reverse causality. The pattern of results is consistent with the hypothesis that migration primarily reduces fixed trade costs resulting from information and trust friction across migrant host and source countries. Importantly, the results imply that policymakers may be able to promote trade by improving immigrants’ labour market integration instead of simply being restricted to promoting more liberal immigration policies, which is generally more controversial.

Hatzigeorgiou, A. & Lodefalk, M. (2015). Trade, Migration and Integration – Evidence and Policy Implications. The World Economy, 38(12), 2013-2048. DOI: 10.1111/twec.12236

Details

Author
Hatzigeorgiou, A. & Lodefalk, M.
Publication year
2015
Published in

The World Economy

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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