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Power Law Scaling in the World Income Distribution

Author

Listed:
  • Corrado Di Guilmi

    () (Department of Economics - Università Politecnica delle Marche)

  • Mauro Gallegati

    () (Department of Economics - Università Politecnica delle Marche)

  • Edoardo Gaffeo

    () (Department of Economics - University of Udine)

Abstract

We show that over the period 1960-1997, the range comprised between the 30th and the 85th percentiles of the world income distribution expressed in terms of GDP per capita invariably scales down as a Pareto distribution. Furthermore, the time path of the power law exponent displays a negatively sloped trend. Our findings suggest that the cross-country average growth process appears to be scale invariant but for countries in the tails of the world income distribution, and that the relative volatility of smaller countries' growth processes have increased over time.

Suggested Citation

  • Corrado Di Guilmi & Mauro Gallegati & Edoardo Gaffeo, 2003. "Power Law Scaling in the World Income Distribution," Economics Bulletin, AccessEcon, vol. 15(6), pages 1-7.
  • Handle: RePEc:ebl:ecbull:eb-03o40003
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Roki Iwahashi & Tomohiro Machikita, 2004. "A new empirical regularity in world income distribution dynamics, 1960-2001," Economics Bulletin, AccessEcon, vol. 6(19), pages 1-15.
    2. Pasquale Cirillo & Mauro Gallegati, 2012. "The Empirical Validation of an Agent-based Model," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 38(4), pages 525-547.
    3. Rafael González-Val & Marcos Sanso-Navarro, 2010. "Gibrat’s law for countries," Journal of Population Economics, Springer;European Society for Population Economics, vol. 23(4), pages 1371-1389, September.
    4. Alexander M. Petersen & Boris Podobnik & Davor Horvatic & H. Eugene Stanley, 2010. "Scale invariant properties of public debt growth," Papers 1002.2491, arXiv.org.
    5. G. Fagiolo & M. Napoletano & A. Roventini, 2007. "How do output growth-rate distributions look like? Some cross-country, time-series evidence," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 57(2), pages 205-211, May.
    6. Carlo Bianchi & Pasquale Cirillo & Mauro Gallegati & Pietro Vagliasindi, 2007. "Validating and Calibrating Agent-Based Models: A Case Study," Computational Economics, Springer;Society for Computational Economics, vol. 30(3), pages 245-264, October.
    7. Shana M. Sundstrom & David G. Angeler & Ahjond S. Garmestani & Jorge H. García & Craig R. Allen, 2014. "Transdisciplinary Application of Cross-Scale Resilience," Sustainability, MDPI, Open Access Journal, vol. 6(10), pages 1-24, October.
    8. D. Garlaschelli & T. Di Matteo & T. Aste & G. Caldarelli & M. I. Loffredo, 2007. "Interplay between topology and dynamics in the World Trade Web," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 57(2), pages 159-164, May.
    9. Jia Shao & Plamen Ch. Ivanov & Boris Podobnik & H. Eugene Stanley, 2007. "Quantitative relations between corruption and economic factors," Papers 0705.0161, arXiv.org.
    10. repec:bla:revinw:v:63:y:2017:i:1:p:189-196 is not listed on IDEAS
    11. Diego Garlaschelli & Maria I. Loffredo, 2007. "Effects of network topology on wealth distributions," Papers 0711.4710, arXiv.org, revised Jan 2008.
    12. Tomson Ogwang, 2011. "Power laws in top wealth distributions: evidence from Canada," Empirical Economics, Springer, vol. 41(2), pages 473-486, October.

    More about this item

    Keywords

    Growth;

    JEL classification:

    • O4 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity
    • E1 - Macroeconomics and Monetary Economics - - General Aggregative Models

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