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Heavy tails and upper-tail inequality: The case of Russia

Author

Listed:
  • Marat Ibragimov

    (Kazan (Volga region) Federal University)

  • Rustam Ibragimov

    (Imperial College Business School
    Innopolis University)

Abstract

Motivated, in part, by the recent surge of interest in robust inequality measurement, cross-country inequality comparisons, applications of heavy-tailed distributions and the study of global and upper-tail inequality, this paper focuses on robust analysis of heavy-tailedness properties and inequality in the upper tails of income distribution in Russia, as measured, mainly, by its tail indices. The study is based on recently developed approaches to robust inference on the degree of heavy-tailedness and their implications for the analysis of upper-tail inequality discussed in the paper. Among other results, the paper provides robust estimates of heavy-tailedness parameters and tail indices for Russian income distribution and their comparisons with the benchmark values in developed economies reported in the previous literature. The estimates point out to important similarity between heavy-tailedness properties of income distribution and their implications for the analysis of upper-tail income inequality in Russia and those in developed markets.

Suggested Citation

  • Marat Ibragimov & Rustam Ibragimov, 2018. "Heavy tails and upper-tail inequality: The case of Russia," Empirical Economics, Springer, vol. 54(2), pages 823-837, March.
  • Handle: RePEc:spr:empeco:v:54:y:2018:i:2:d:10.1007_s00181-017-1239-0
    DOI: 10.1007/s00181-017-1239-0
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    Cited by:

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    2. Tjeerd de Vries & Alexis Akira Toda, 2022. "Capital and Labor Income Pareto Exponents Across Time and Space," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 68(4), pages 1058-1078, December.
    3. Rustam Ibragimov & Paul Kattuman & Anton Skrobotov, 2021. "Robust Inference on Income Inequality: $t$-Statistic Based Approaches," Papers 2105.05335, arXiv.org, revised Nov 2021.
    4. Ji Hyung Lee & Yuya Sasaki & Alexis Akira Toda & Yulong Wang, 2022. "Capital and Labor Income Pareto Exponents in the United States, 1916-2019," Papers 2206.04257, arXiv.org.
    5. Matteo Barigozzi & Giuseppe Cavaliere & Lorenzo Trapani, 2020. "Determining the rank of cointegration with infinite variance," Discussion Papers 20/01, University of Nottingham, Granger Centre for Time Series Econometrics.
    6. Vladimir Hlasny, 2021. "Parametric representation of the top of income distributions: Options, historical evidence, and model selection," Journal of Economic Surveys, Wiley Blackwell, vol. 35(4), pages 1217-1256, September.
    7. T. M. Maleva & M. A. Kartseva & P. O. Kuznetsova & A. A. Salmina, 2021. "Does the Application of Alternative Methods Change the Pattern of Regional Inequality in Russia?," Regional Research of Russia, Springer, vol. 11(1), pages 18-28, January.
    8. Matteo Barigozzi & Giuseppe Cavaliere & Lorenzo Trapani, 2021. "Inference in heavy-tailed non-stationary multivariate time series," Papers 2107.13894, arXiv.org.

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    More about this item

    Keywords

    Income distribution; Inequality; Heavy-tailedness; Power laws; Pareto distributions; Upper tails; Upper-tail inequality; Global inequality; Russia; Post-Soviet economies; Transition economies; Emerging economies;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • P24 - Political Economy and Comparative Economic Systems - - Socialist and Transition Economies - - - National Income, Product, and Expenditure; Money; Inflation

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