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Estimation of income distribution and detection of subpopulations: an explanatory model

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  • Flachaire, Emmanuel
  • Núñez, Olivier

Abstract

Inequality and polarization analyses are complementary but conceptually different. They are usually implemented independently in practic e, with different a priori assumptions and different tools. In this paper, we develop a unique method to study simultaneously these different and complementary concerns. Based on mixture models, the method we develop includes at the same time : an estimation of income distribution with no a priori assumptions - a decomposition in several homogeneous subpopulations - an explanatory model to study the structure of the income distribution. Length: 20 pages

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  • Flachaire, Emmanuel & Núñez, Olivier, 2003. "Estimation of income distribution and detection of subpopulations: an explanatory model," DES - Working Papers. Statistics and Econometrics. WS ws030201, Universidad Carlos III de Madrid. Departamento de Estadística.
    • Handle: RePEc:cte:wsrepe:ws030201
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    Cited by:

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    2. Dorothée Boccanfuso & Bernard Decaluwé & Luc Savard, 2008. "Poverty, income distribution and CGE micro-simulation modeling: Does the functional form of distribution matter?," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 6(2), pages 149-184, June.
    3. Ivana Malá, 2015. "Vícerozměrný pravděpodobnostní model rozdělení příjmů českých domácností [Multivariate Probability Model For Incomes of the Czech Households]," Politická ekonomie, Prague University of Economics and Business, vol. 2015(7), pages 895-908.
    4. Stéphane Guerrier & Samuel Orso & Maria-Pia Victoria-Feser, 2018. "Parametric Inference for Index Functionals," Econometrics, MDPI, vol. 6(2), pages 1-11, April.
    5. Ivana Malá, 2012. "The Use of Finite Mixtures of Lognormal Distribution for the Modelling of Income Distributions [Použití konečných směsí pro modelování příjmových rozdělení]," Acta Oeconomica Pragensia, Prague University of Economics and Business, vol. 2012(4), pages 26-39.
    6. William E. Griffiths and Gholamreza Hajargasht, 2012. "GMM Estimation of Mixtures from Grouped Data:," Department of Economics - Working Papers Series 1148, The University of Melbourne.
    7. Nartikoev, Alan & Peresetsky, Anatoly, 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.
    8. Longford, N.T. & Pittau, M.G., 2006. "Stability of household income in European countries in the 1990s," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 1364-1383, November.
    9. Nartikoev, Alan & Peresetsky, Anatoly, 2020. "Эндогенная Классификация Домохозяйств В Регионах России [Endogenous household classification: Russian regions]," MPRA Paper 104351, University Library of Munich, Germany.
    10. Kazuhiko Kakamu, 2022. "Bayesian analysis of mixtures of lognormal distribution with an unknown number of components from grouped data," Papers 2210.05115, arXiv.org, revised Sep 2023.
    11. Michele Bavaro & Federico Tullio, 2023. "Intergenerational mobility measurement with latent transition matrices," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 21(1), pages 25-45, March.
    12. Ivana Malá, 2013. "Použití konečných směsí logaritmicko-normálních rozdělení pro modelování příjmů českých domácností [The Use of Finite Mixtures of Lognormal Distribution for the Modelling of Household Income Distri," Politická ekonomie, Prague University of Economics and Business, vol. 2013(3), pages 356-372.

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