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Statistical Methods for Distributional Analysis

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  • Frank A. Cowell

    () (STICERD - LSE - London School of Economics and Political Science)

  • Emmanuel Flachaire

    (GREQAM - Groupement de Recherche en Économie Quantitative d'Aix-Marseille - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique)

Abstract

This Chapter is about the techniques, formal and informal, that are commonly used to give quantitative answers in the field of distributional analysis - covering subjects including inequality, poverty and the modelling of income distributions. It deals with parametric and non-parametric approaches and the way in which imperfections in data may be handled in practice.

Suggested Citation

  • Frank A. Cowell & Emmanuel Flachaire, 2014. "Statistical Methods for Distributional Analysis," Working Papers halshs-01115996, HAL.
    • Handle: RePEc:hal:wpaper:halshs-01115996
    Note: View the original document on HAL open archive server: https://halshs.archives-ouvertes.fr/halshs-01115996
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    Cited by:

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    2. Safari, Muhammad Aslam Mohd & Masseran, Nurulkamal & Ibrahim, Kamarulzaman, 2018. "A robust semi-parametric approach for measuring income inequality in Malaysia," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 1-13.
    3. Vladimir Hlasny, 2020. "Parametric Representation of the Top of Income Distributions: Options, Historical Evidence and Model Selection," Working Papers 547, ECINEQ, Society for the Study of Economic Inequality.
    4. Winter, Simon & Schlesewsky, Lisa, 2019. "The German feed-in tariff revisited - an empirical investigation on its distributional effects," Energy Policy, Elsevier, vol. 132(C), pages 344-356.
    5. Engelhardt, Carina & Wagener, Andreas, 2017. "The income distribution of voters: a case study from Germany," Hannover Economic Papers (HEP) dp-586, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    6. Oancea, Bogdan & Andrei, Tudorel & Pirjol, Dan, 2017. "Income inequality in Romania: The exponential-Pareto distribution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 486-498.

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    More about this item

    Keywords

    inequality measure; welfare indices; hypothesis testing; influence function; bootstrap; confidence intervals; poverty measure; dominance criteria; non-parametric methods; goodness of fit; parametric modelling;
    All these keywords.

    JEL classification:

    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement
    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General

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