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PublicationWorking paper

Ratio Working Paper No. 316: Mitigating Information Frictions in Trade: Evidence from Export Credit Guarantees

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Abstract

Information frictions make non-simultaneous exchange risky, particularly across borders. Therefore, many countries insure cross-border exchange. We investigate the effects on firm trade, jobs, value added and productivity, using uniquely detailed, comprehensive and longitudinal transaction-level Swedish data on insurance and granular data on exporters and foreign buyers. For identification, we employ matching and differencein- difference and fuzzy regression discontinuity estimators and exploit a quasi-natural experiment. We find strikingly heterogeneous effects across firm size and response variables. The strongest positive effects are for small traders and new users. Overall, the evidence suggests a causal link from export insurance to firm performance.

Lodefalk, M., Tang, A. & Wang, Z. (2018). Mitigating Information Frictions in Trade: Evidence from Export Credit Guarantees. (Ratio Working Paper No. 316)

Details

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

Ratio Working Paper

Related

  • Associate Professor

    Magnus Lodefalk

    magnus.lodefalk@oru.se

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

2026

Published in

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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)

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

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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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