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Эндогенная Классификация Домохозяйств В Регионах России
[Endogenous household classification: Russian regions]

Author

Listed:
  • Nartikoev, Alan
  • Peresetsky, Anatoly

Abstract

In order to study the structure of society, sociologists usually distinguish several homogeneous social groups, or classes. The most common division consists of three groups: upper, middle and lower classes. Such a partition is traditionally based on a subjective (exogenous) criteria adopted by a particular re-searcher. In this paper, the distribution of households in Russian federal districts is modeled as a mixture of three lognormal distributions. The mixing proportions (probabilities) of the mixture components and the corresponding distribution parameters are modeled as functions of the individual characteristics of households. The result is an endogenous decomposition of household sample into three clusters (lower, middle, upper). This classification allows to analyze the difference between regions and the patterns of intergroup dynamics in the period 2014–2018. The approach used in this work demonstrated great flexi-bility in analyzing the distribution of income, the dynamics of this distribution over time, as well as a migration between relatively homogeneous clusters. The use of mixture density function with endogenously determined probabilities allows for precise detection of the effects of the income heterogeneity determinants within each cluster.

Suggested Citation

  • Nartikoev, Alan & Peresetsky, Anatoly, 2020. "Эндогенная Классификация Домохозяйств В Регионах России [Endogenous household classification: Russian regions]," MPRA Paper 104351, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:104351
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    References listed on IDEAS

    as
    1. Frank A. Cowell & Emmanuel Flachaire, 2014. "Statistical Methods for Distributional Analysis," Working Papers halshs-01115996, HAL.
    2. Flachaire, Emmanuel & Nunez, Olivier, 2007. "Estimation of the income distribution and detection of subpopulations: An explanatory model," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3368-3380, April.
    3. Frank A. Cowell & Philippe Kerm, 2015. "Wealth Inequality: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 29(4), pages 671-710, September.
    4. García-Fernández, Rosa María & Gottlieb, Daniel & Palacios-González, Frederico, 2013. "Polarization, growth and social policy in the case of Israel, 1997-2008," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 7, pages 1-40.
    5. Duangkamon Chotikapanich & William E. Griffiths, 2008. "Estimating Income Distributions Using a Mixture of Gamma Densities," Economic Studies in Inequality, Social Exclusion, and Well-Being, in: Duangkamon Chotikapanich (ed.), Modeling Income Distributions and Lorenz Curves, chapter 16, pages 285-302, Springer.
    6. Diego Winkelried & Javier Torres, 2019. "Economic mobility along the business cycle. The case of Peru," Applied Economics, Taylor & Francis Journals, vol. 51(18), pages 1894-1906, April.
    7. Edwin Fourrier-Nicolaï & Michel Lubrano, 2020. "Bayesian inference for TIP curves: an application to child poverty in Germany," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 18(1), pages 91-111, March.
    8. Pittau, Maria Grazia & Zelli, Roberto & Johnson, Paul, "undated". "Mixture Models and Convergence Clubs," Vassar College Department of Economics Working Paper Series 91, Vassar College Department of Economics.
    9. Alan Nartikoev & Anatoly Peresetsky, 2019. "Modeling the dynamics of income distribution in Russia," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 54, pages 105-125.
    10. Maria Grazia Pittau & Roberto Zelli & Paul A. Johnson, 2010. "Mixture Models, Convergence Clubs, And Polarization," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 56(1), pages 102-122, March.
    11. Lubrano, Michel & Ndoye, Abdoul Aziz Junior, 2016. "Income inequality decomposition using a finite mixture of log-normal distributions: A Bayesian approach," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 830-846.
    12. Maria Grazia Pittau & Roberto Zelli, 2006. "Empirical evidence of income dynamics across EU regions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(5), pages 605-628, July.
    13. Maria Grazia Pittau & Roberto Zelli & Riccardo Massari, 2016. "Evidence of Convergence Clubs Using Mixture Models," Econometric Reviews, Taylor & Francis Journals, vol. 35(7), pages 1317-1342, August.
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    Keywords

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    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions
    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
    • R20 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - General

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