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Analysing Convergence through the Distribution Dynamics Approach: Why and how?

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  • Stefano Magrini

    () (Department of Economics, University Of Venice C� Foscari)

Abstract

The convergence hypothesis has stimulated a heated debate within the growth literature. The present paper compares the two most commonly adopted empirical approaches, the regression approach and the distribution dynamics approach, and argues that the former fails to uncover important features of the dynamics that might characterise the convergence process. Next, it provides an in depth description of the features and underlying assumptions of the distribution dynamics approach as well as a detailed discussion of some important aspects related to the estimate of stochastic kernels via kernel density estimators. Finally, the empirical section allows to emphasises the interpretational advantages stemming from the use of stochastic kernels to capture the evolution of the entire cross-sectional income distribution. Incidentally, through a comparison between the results obtained from alternative sets of Italian regions, it suggest that the use of administrative regions could lead to ambiguous results.

Suggested Citation

  • Stefano Magrini, 2007. "Analysing Convergence through the Distribution Dynamics Approach: Why and how?," Working Papers 2007_13, Department of Economics, University of Venice "Ca' Foscari".
  • Handle: RePEc:ven:wpaper:2007_13
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    References listed on IDEAS

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    Cited by:

    1. Luckstead, Jeff & Devadoss, Stephen, 2014. "Do the world’s largest cities follow Zipf’s and Gibrat’s laws?," Economics Letters, Elsevier, vol. 125(2), pages 182-186.
    2. Vicente Rios Ibañez, 2014. "What drives regional unemployment convergence?," ERSA conference papers ersa14p924, European Regional Science Association.
    3. repec:gam:jsusta:v:10:y:2018:i:2:p:528-:d:132111 is not listed on IDEAS
    4. Silvia Dal Bianco, 2016. "Going clubbing in the eighties: convergence in manufacturing sectors at a glance," Empirical Economics, Springer, vol. 50(2), pages 623-659, March.
    5. Devadoss, Stephen & Luckstead, Jeff, 2015. "Growth process of U.S. small cities," Economics Letters, Elsevier, vol. 135(C), pages 12-14.
    6. David Hincapié Vélez, 2013. "¿Está convergiendo el gasto gubernamental en las Universidades Públicas colombianas?," ENSAYOS DE ECONOMÍA 012250, UNIVERSIDAD NACIONAL DE COLOMBIA SEDE MEDELLIN.
    7. Silvia Dal Bianco, 2009. "A Reassessment of Italian Regional Convergence through a Non-Parametric Approach," Quaderni di Dipartimento 099, University of Pavia, Department of Economics and Quantitative Methods.
    8. Robert Beyer & Michael Stemmer, 2015. "From progress to nightmare - European regional unemployment over time," DNB Working Papers 458, Netherlands Central Bank, Research Department.
    9. Daniele, Vittorio, 2009. "Regional convergence and public spending in Italy. Is there a correlation?," MPRA Paper 14334, University Library of Munich, Germany.
    10. Luckstead, Jeff & Devadoss, Stephen, 2014. "A nonparametric analysis of the growth process of Indian cities," Economics Letters, Elsevier, vol. 124(3), pages 516-519.

    More about this item

    Keywords

    Distribution Dynamics; Stochastic Kernel; Kernel Density Estimation; beta-convergence; Regions;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General
    • O40 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - General
    • O52 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - Europe
    • R10 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - General

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