IDEAS home Printed from https://ideas.repec.org/p/wiw/wiwrsa/ersa06p262.html
   My bibliography  Save this paper

Analyzing Intra-Distribution Dynamics: A Reappraisal

Author

Listed:
  • Giuseppe Arbia
  • Roberto Basile
  • Gianfranco Piras

Abstract

In this paper we suggest an alternative estimator and an alternative graphical analysis, both developed by Hyndman et al. (1996), to describe the law of motion of cross-sectional distributions of per-capita income and its components in Europe. This estimator has better properties than the kernel density estimator generally used in the literature on intra-distribution dynamics (cf. Quah, 1997). By using the new estimator, we obtain evidence of a very strong persistent behavior of the regions considered in the study, that is poor regions tend to remain poorer and rich regions tend to remain richer. These results are also in line with the most recent literature available on the distribution dynamic approach to regional convergence (Pittau and Zelli, 2006).

Suggested Citation

  • Giuseppe Arbia & Roberto Basile & Gianfranco Piras, 2006. "Analyzing Intra-Distribution Dynamics: A Reappraisal," ERSA conference papers ersa06p262, European Regional Science Association.
    • Handle: RePEc:wiw:wiwrsa:ersa06p262
    as

    Download full text from publisher

    File URL: https://www-sre.wu.ac.at/ersa/ersaconfs/ersa06/papers/262.pdf
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Angelos Liontakis & Dimitris Kremmydas, 2013. "Food Inflation in EU: Distribution Analysis and Spatial Effects," Working Papers 2013-3, Agricultural University of Athens, Department Of Agricultural Economics.
    2. Ronny Correa-Quezada & Lucía Cueva-Rodríguez & José Álvarez-García & María de la Cruz del Río-Rama, 2020. "Application of the Kernel Density Function for the Analysis of Regional Growth and Convergence in the Service Sector through Productivity," Mathematics, MDPI, vol. 8(8), pages 1-20, July.
    3. María Hierro & Adolfo Maza, 2010. "Per capita income convergence and internal migration in Spain: Are foreign‐born migrants playing an important role?," Papers in Regional Science, Wiley Blackwell, vol. 89(1), pages 89-107, March.
    4. Angelos Liontakis & Christos T. Papadas, 2010. "Distribution Dynamics of Food Price Inflation Rates in EU: An Alternative Conditional Density Estimator Approach," Working Papers 2010-6, Agricultural University of Athens, Department Of Agricultural Economics.
    5. Olivier Peron & Serge Rey, 2012. "Trade and convergence of per capita income in the Indian Ocean Zone, 1950–2008," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 49(3), pages 657-683, December.
    6. Maza, Adolfo & Villaverde, José, 2008. "The world per capita electricity consumption distribution: Signs of convergence?," Energy Policy, Elsevier, vol. 36(11), pages 4255-4261, November.
    7. Wei Kang & Sergio J. Rey, 2018. "Conditional and joint tests for spatial effects in discrete Markov chain models of regional income distribution dynamics," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 61(1), pages 73-93, July.
    8. Mendez-Guerra, Carlos, 2019. "Environmental Efficiency and Regional Convergence Clusters in Japan: A Nonparametric Density Approach," MPRA Paper 92245, University Library of Munich, Germany.
    9. Liontakis, Angelos E. & Papadas, Christos T., 2009. "Distribution Dynamics of Food Price Inflation Rates in EU: An Alternative Conditional Density Estimator Approach," 113th Seminar, September 3-6, 2009, Chania, Crete, Greece 58084, European Association of Agricultural Economists.
    10. Adolfo Maza & María Hierro & José Villaverde, 2010. "Measuring intra-distribution income dynamics: an application to the European regions," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 45(2), pages 313-329, October.
    11. Carlos Mendez, 2020. "Regional efficiency convergence and efficiency clusters," Asia-Pacific Journal of Regional Science, Springer, vol. 4(2), pages 391-411, June.
    12. Poletti Laurini, Márcio & Valls Pereira, Pedro L., 2009. "Conditional stochastic kernel estimation by nonparametric methods," Economics Letters, Elsevier, vol. 105(3), pages 234-238, December.
    13. Maza, Adolfo & Hierro, María & Villaverde, José, 2010. "Renewable electricity consumption in the EU-27: Are cross-country differences diminishing?," Renewable Energy, Elsevier, vol. 35(9), pages 2094-2101.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wiw:wiwrsa:ersa06p262. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Gunther Maier (email available below). General contact details of provider: http://www.ersa.org .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.