DETECTING CONVERGENCE CLUBS

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.