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DETECTING CONVERGENCE CLUBS

Published online by Cambridge University Press:  10 July 2018

Fuat C. Beylunioğlu
Affiliation:
Istanbul Bilgi University
M. Ege Yazgan
Affiliation:
Istanbul Bilgi University
Thanasis Stengos*
Affiliation:
University of Guelph
*
Address correspondence to: Thanasis Stengos, Department of Economics and Finance, University of Guelph, Guelph, Ontario, N1G 2W1, Canada; e-mail: tstengos@uoguelph.ca

Abstract

The convergence hypothesis, which is developed in the context of growth economics, asserts that the income differences across countries are transitory, and developing countries will eventually attain the level of income of developed ones. On the other hand, convergence clubs hypothesis claim that the convergence can only be realized across groups of countries that share some common characteristics. In this study, we propose a new method to find convergence clubs that combines a pairwise method of testing convergence with maximum clique and maximal clique algorithms. Unlike many of those already developed in the literature, this new method aims to find convergence clubs endogenously without depending on a-priori classifications. In a Monte Carlo simulation study, the success of the method in finding convergence clubs is compared with a similar algorithm. Simulation results indicated that the proposed method perform better than the compared algorithm in most cases. In addition to the Monte Carlo, a new empirical evidence on the existence of convergence clubs is presented in the context of real data applications.

Type
Articles
Copyright
© Cambridge University Press 2018

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Footnotes

This work has been produced as a part of the research project (project no: 113K757) supported by The Scientific and Technological Research Council of Turkey (TUBİTAK). Ege Yazgan and Thanasis Stengos would like to acknowledge financial support from TUBİTAK, while Stengos would also like to thank SSHRC of Canada.

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