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Convergence Clubs Beyond GDP: A Non-Parametric Density Approach

Mendez-Guerra, Carlos (2017): Convergence Clubs Beyond GDP: A Non-Parametric Density Approach. Forthcoming in: JASID Conference Proceedings (2017)

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Abstract

In the study of convergence in living standards across countries, per-capita Gross Domestic Product (GDP) has been usually used as a proxy for the measurement of national well-being. However, other important welfare aspects––beyond GDP––should also be considered. This paper revisits the cross-country convergence hypothesis in a context beyond GDP. It focuses on a novel welfare index [Jones and Klenow (2016), American Economic Review, 106(9)] that incorporates measures of consumption, leisure, life expectancy, and inequality. Based on a sample of 128 countries over the 1980-2007 period, the paper first documents the lack of overall sigma and (absolute) beta convergence. Next, through the lens of a stochastic kernel density and a clustering algorithm, it documents the formation of three convergence clubs. Under this classification, the beta convergence coefficient is recovered for each club. However, only the core members of the richest club appear to be reducing their welfare differences in a way that is consistent with the strong notion of sigma convergence. Overall, these results re-emphasize the finding that beta convergence is necessary but not sufficient for sigma convergence––even within convergence clubs and in a context beyond GDP.

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