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Italian economic dualism and convergence clubs at regional level

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Abstract
This paper compares the long run prediction of convergence clubs introduced by Quah (1996 and 1997) with the actual observed dynamics of the Italian regions during the period 1970-2004. Economic dynamics is described by the evolution per capita GDP and different notions of distance are introduced to compare the trajectories of the regions. In addition, by means of hierarchical clustering methodologies the set of economies are segmented. By using the average distance, the study identifies two main performance clubs resembling the long run prediction of two converge clubs. On the other hand, the distance correlation shows different co-movements between members of the same cluster, indicating a variety of responses to external shocks. In particular the average distance identifies a clear division between a high performance club consisting of regions from the Center North, and a low performance club composed by regions from the South and islands. The presence of a cluster composed by center north regions is substantially confirmed by the distance correlation analysis, suggesting an homogeneous response to external shock. By contrast Southern regions display the same dynamical evolution but difference in co-movements. Our analysis provides hints about the fundamentals that link the regions in their process of divergence. In fact the performance clubs pattern we discovered reflects the distribution of economic activities as well as the structural attributes of the regional economies.

Suggested Citation

  • N. Garrido & F. Mureddu, 2011. "Italian economic dualism and convergence clubs at regional level," Working Paper CRENoS 201116, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    • Handle: RePEc:cns:cnscwp:201116
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    2. Weili Zhang & Jibin Fu & Quan Ju, 2020. "A study on the model of economic growth convergence in developing regions: an empirical analysis from Henan Province, China," Empirical Economics, Springer, vol. 59(2), pages 547-567, August.
    3. Weili Zhang & Wei Xu & Xiaoye Wang, 2019. "Regional convergence clubs in China: identification and conditioning factors," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 62(2), pages 327-350, April.
    4. Aiello, Francesco & Castiglione, Concetta, 2014. "Being efficient to stay strong in a weak economy. The case of calabrian manufacturing firms," MPRA Paper 54366, University Library of Munich, Germany.
    5. Piras, Romano, 2022. "Structural change, growth, and convergence in Italy: 1951–1970," Structural Change and Economic Dynamics, Elsevier, vol. 61(C), pages 362-379.
    6. Iacopo Odoardi & Donatella Furia & Piera Cascioli, 2021. "Can social support compensate for missing family support? An examination of dropout rates in Italy," Regional Science Policy & Practice, Wiley Blackwell, vol. 13(1), pages 121-139, February.
    7. Sławomir Pastuszka & Jurand Skrzypek, 2017. "Konwergencja czy dywergencja regionów włoskich?," Gospodarka Narodowa. The Polish Journal of Economics, Warsaw School of Economics, issue 2, pages 101-130.
    8. Alessandro Crociata & Iacopo Odoardi & Massimiliano Agovino & Pier Luigi Sacco, 2020. "A missing link? Cultural capital as a source of human capital: evidence from Italian regional data," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 64(1), pages 79-109, February.

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    More about this item

    Keywords

    economic convergence; economic dualism; hierarchical clustering;
    All these keywords.

    JEL classification:

    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism
    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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