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The Endogenity of the Optimum Currency Area Criteria
Abstract
A country' suitability for entry into a currency union depends on a number of economic conditions. These include, inter alia, the intensity of trade with other potential members of the currency union, and the extent to which domestic business cycles are correlated with those of the other countries. But international trade patterns and international business cycle correlations are endogenous. This paper develops and investigates the relationship between the two phenomena. Using thirty years of data for twenty industrialised countries, we uncover a strong and striking empirical finding: countries with closer trade links tend to have more tightly correlated business cycles.
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Cited by
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Boehm, C. E., Flaaen, A., & Pandalai-Nayar, N. (2019) · The Review of Economics and Statistics · doi:10.1162/rest_a_00750 · Cited by 616
Melitz, J. (2008) · European Economic Review · doi:10.1016/j.euroecorev.2007.05.002 · Cited by 474
Trade networks and colonial trade spillovers
Berthou, A., & Ehrhart, H. (2017) · Review of International Economics · doi:10.1111/roie.12288 · Cited by 27
Metadata
- Authors
- Frankel, J. A., & Rose, A. K.
- Venue
- The Economic Journal
- Publisher
- Oxford University Press (OUP)
- Published
- 1998-07-01
- Volume
- 108
- Issue
- 449
- Pages
- 1009-1025
- ISSN
- 0013-0133 (ISSN-L)
- 1468-0297
- OpenAlex
- W3125146190
- Cited by
- 2,540
- References
- 0
- FWCI
- 84.25
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: The Endogenity of the Optimum Currency Area Criteria. https://app.cochat.ai/papers/10.1111/1468-0297.00327
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- Frankel, J. A., & Rose, A. K. (1998). The Endogenity of the Optimum Currency Area Criteria. The Economic Journal. https://doi.org/10.1111/1468-0297.00327
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
