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PublikationWorking paper

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

Ladda ner PDF

Sammanfattning

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)

Detaljer

Författare
Lodefalk, M., Tang, A. & Wang, Z.
Publiceringsår
2018
Publicerat i

Ratio Working Paper

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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