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Cited by 438

The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.

Spatial Empirics for Economic Growth and Convergence

Open access
Rey, Sergio J.·2001·Geographical Analysis
DOI 10.1111/j.1538-4632.2001.tb00444.x
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Abstract

This paper suggests some new empirical strategies for analyzing the evolution of regional income distributions over time and space. These approaches are based on extensions to the classical Markov transition matrices that allow for a more comprehensive analysis of the geographical dimensions of the transitional dynamics. This is achieved by integrating some recently developed local spatial statistics within a Markov framework. Insights to not only the frequency with which one economy may transition across different classes in the income distribution, but also how those transitions may or may not be spatially dependent are provided by these new measures. A number of indices are suggested as ways to characterize the space‐time dynamics and are illustrated in a case study of U. S. regional income dynamics over the 1929–1994 period.

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Crossref · 10.1111/j.1538-4632.2001.tb00444.x

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Metadata

Authors
Rey, S. J.
Venue
Geographical Analysis
Publisher
Wiley
Published
2001-07
Volume
33
Issue
3
Pages
195-214
ISSN
  • 0016-7363 (ISSN-L)
  • 1538-4632
DOI
10.1111/j.1538-4632.2001.tb00444.x
OpenAlex
W2084583092
Cited by
438
References
28
FWCI
41.16
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Spatial Empirics for Economic Growth and Convergence. https://app.cochat.ai/papers/10.1111/j.1538-4632.2001.tb00444.x
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Rey, S. J. (2001). Spatial Empirics for Economic Growth and Convergence. Geographical Analysis. https://doi.org/10.1111/j.1538-4632.2001.tb00444.x

Data sources

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.