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Cited by 260

The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.

Estimates of Time to Economic Convergence: An Analysis of Regions of the European Union

Fingleton, Bernard·1999·International Regional Science Review

Abstract

According to the simplest neoclassical theory, closer integration will ultimately eliminate the current differences in per capita incomes between European Union (EU) regions. Models based on the Keynesian tradition and endogenous growth theory suggest different outcomes. This article looks at the evidence for convergence along neoclassical lines and also from a point of view that accepts the critique of neoclassical theory. Estimates are made of the time it will take for two different forms of equilibrium to be realized. From a neoclassical perspective, there is only weak evidence that EU regions are converging in terms of their output per capita, requiring more than two centuries for approximate convergence to be achieved. From a non-neoclassical perspective, stochastic equilibrium will require more than three centuries, although the hypothesis that the regions have already achieved stochastic equilibrium cannot be dismissed.

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Metadata

Authors
Fingleton, B.
Venue
International Regional Science Review
Published
1999-04
Volume
22
Issue
1
Pages
5-34
ISSN
  • 0160-0176 (ISSN-L)
  • 1552-6925
OpenAlex
W2036180133
Cited by
260
References
45
FWCI
16.56
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Estimates of Time to Economic Convergence: An Analysis of Regions of the European Union. https://app.cochat.ai/papers/10.1177/016001769902200102
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Fingleton, B. (1999). Estimates of Time to Economic Convergence: An Analysis of Regions of the European Union. International Regional Science Review. https://doi.org/10.1177/016001769902200102

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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.