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Bayesian inference for TIP curves: an application to child poverty in Germany

Author

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  • Edwin Fourrier-Nicolai

    (AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique)

  • Michel Lubrano

    (AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique, School of Economics, Jiangxi University of Finance and Economics)

Abstract
TIP curves are cumulative poverty gap curves used for representing the three different aspects of poverty: incidence, intensity and inequality. The paper provides Bayesian inference for TIP curves, linking their expression to a parametric representation of the income distribution using a mixture of log-normal densities. We treat specifically the question of zero-inflated income data and survey weights, which are two important issues in survey analysis. The advantage of the Bayesian approach is that it takes into account all the information contained in the sample and that it provides small sample credible intervals and tests for TIP dominance. We apply our methodology to evaluate the evolution of child poverty in Germany after 2002, providing thus an update the portrait of child poverty in Germany given in Corak et al. (Rev. Income Wealth 54(4), 547–571, 2008).

Suggested Citation

  • Edwin Fourrier-Nicolai & Michel Lubrano, 2020. "Bayesian inference for TIP curves: an application to child poverty in Germany," Post-Print hal-02477216, HAL.
    • Handle: RePEc:hal:journl:hal-02477216
    DOI: 10.1007/s10888-019-09426-6
    Note: View the original document on HAL open archive server: https://amu.hal.science/hal-02477216
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Edwin Fourrier-Nicolai & Michel Lubrano, 2019. "The Effect of Aspirations on Inequality: Evidence from the German Reunification using Bayesian Growth Incidence Curves," AMSE Working Papers 1914, Aix-Marseille School of Economics, France.
    2. Edwin Fourrier-Nicolaï & Michel Lubrano, 2021. "Bayesian Inference for Parametric Growth Incidence Curves," Research on Economic Inequality, in: Research on Economic Inequality: Poverty, Inequality and Shocks, volume 29, pages 31-55, Emerald Group Publishing Limited.
    3. Michel Lubrano & Zhou Xun, 2023. "The Bayesian approach to poverty measurement," Post-Print halshs-04135764, HAL.
    4. Michel Lubrano & Zhou Xun, 2021. "The Bayesian approach to poverty measurement," AMSE Working Papers 2133, Aix-Marseille School of Economics, France.
    5. Michel Lubrano & Zhou Xun, 2023. "The Bayesian approach to poverty measurement," Post-Print hal-04347292, HAL.
    6. Nartikoev, Alan & Peresetsky, Anatoly, 2020. "Эндогенная Классификация Домохозяйств В Регионах России [Endogenous household classification: Russian regions]," MPRA Paper 104351, University Library of Munich, Germany.

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    More about this item

    Keywords

    poverty; inequality; zero-inflatedmodel; mixture model; survey weights; Bayesian inference;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
    • I38 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Government Programs; Provision and Effects of Welfare Programs

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