In this paper, we apply the non-parametric method proposed by Quah to examine convergence hypothesis for Italian regions using GDP and total factor productivity measured by the Malmquist index. Using the stochastic kernel approach, this study suggests that the measure of total factor productivity is a crucial precondition for the estimation of a region’s growth. Our results applied to the 20 Italian regions show no convergence for both GDP and TFP variables. For the GDP case, it confirms the Italian divide but for the TFP variable, it reveals the creation of three clubs. However, looking at the long-run density, it reveals that the shape of the ergodic density distribution, for the TFP, is clearly unimodal and it could imply a long-run convergence of regional productivity in Italy.

Stochastic Convergence of Income and Total Factor Productivity: Evidence from the Italian Regions

KOUNETAS, Konstantinos;O. Napolitano;M. Pietroluongo
2018

Abstract

In this paper, we apply the non-parametric method proposed by Quah to examine convergence hypothesis for Italian regions using GDP and total factor productivity measured by the Malmquist index. Using the stochastic kernel approach, this study suggests that the measure of total factor productivity is a crucial precondition for the estimation of a region’s growth. Our results applied to the 20 Italian regions show no convergence for both GDP and TFP variables. For the GDP case, it confirms the Italian divide but for the TFP variable, it reveals the creation of three clubs. However, looking at the long-run density, it reveals that the shape of the ergodic density distribution, for the TFP, is clearly unimodal and it could imply a long-run convergence of regional productivity in Italy.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11367/69447
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