0 Datasets
0 Files
Get instant academic access to this publication’s datasets.
Yes. After verification, you can browse and download datasets at no cost. Some premium assets may require author approval.
Files are stored on encrypted storage. Access is restricted to verified users and all downloads are logged.
Yes, message the author after sign-up to request supplementary files or replication code.
Join 50,000+ researchers worldwide. Get instant access to peer-reviewed datasets, advanced analytics, and global collaboration tools.
✓ Immediate verification • ✓ Free institutional access • ✓ Global collaborationJoin our academic network to download verified datasets and collaborate with researchers worldwide.
Get Free AccessAbstract Cultural trust biases (i.e., stereotypes) play an important role in shaping multinational banks’ cross-border exposures. Exploiting a unique identification strategy and combining European regulatory data on banks’ sovereign debt portfolios with existing and new surveys across 30 European countries, we show that multinational banks are more likely to lend to the government of a country when the residents of the countries where they operate exhibit more trust in the residents of that country. This result is robust to saturating our models with time-varying fixed effects at bank and country-pair levels, controlling for financial, informational, political and cultural linkages, and instrumenting trust via genetic and somatic similarities. Bank-level trust similarly drives corporate lending across borders and tilts banks’ sovereign portfolios toward long-term maturities. Its role is amplified when governments are hit by salience shocks such as Eurozone crises and the Brexit referendum. As potential transmission channels of stereotypes from foreign bank branches to headquarters, we provide evidence consistent with culturally biased communication and internal transfers of human capital.
Barry Eichengreen, Orkun Saka (2025). Cultural Stereotypes of Multinational Banks. , DOI: https://doi.org/10.1093/jeea/jvaf032.
Datasets shared by verified academics with rich metadata and previews.
Authors choose access levels; downloads are logged for transparency.
Students and faculty get instant access after verification.
Type
Article
Year
2025
Authors
2
Datasets
0
Total Files
0
Language
en
DOI
https://doi.org/10.1093/jeea/jvaf032
Access datasets from 50,000+ researchers worldwide with institutional verification.
Get Free Access