IDEAS home Printed from https://ideas.repec.org/a/vrs/foeste/v20y2020i1p421-436n25.html
   My bibliography  Save this article

Analysis of Household Income in Poland Based on the Zenga Distribution and Selected Income Inequality Measure

Author

Listed:
  • Trzcińska Kamila

    (University of Lodz, Department of Statistics and Demography, Rewolucji 1905 r. 41, 92-2014Lodz, Poland)

Abstract

Research background: A lot of research has been directed at describing empirical distributions by using a theoretical model. In the literature there are proposals for various types of mathematical functions. In 2010 Zenga proposed a new three-parameter model for economic size distribution which possesses interesting statistical properties which can be used to model income, wealth and financial variables.Purpose: The aim of this paper is to apply the Zenga model to income distributions in Poland by voivodeship.Research methodology: The basis for the calculations presented in the paper has been based on the individual data coming from a random sample obtained within a Household Budget Survey conducted by the central Statistical Office in the year 2014. The parameters estimates of the Zenga distribution were obtained by means of the D’Addario’s invariants methods, mainly with the Pietra index.Results: The results of the conducted approximations, presented in the paper confirmed the good consistency of the Zenga distribution with the empirical income distribution in Poland, both in total and for households.Novelty: The study contributed to the application of a new three-parameter income distribution model to describe income distributions in Poland.

Suggested Citation

  • Trzcińska Kamila, 2020. "Analysis of Household Income in Poland Based on the Zenga Distribution and Selected Income Inequality Measure," Folia Oeconomica Stetinensia, Sciendo, vol. 20(1), pages 421-436, June.
    • Handle: RePEc:vrs:foeste:v:20:y:2020:i:1:p:421-436:n:25
    DOI: 10.2478/foli-2020-0025
    as

    Download full text from publisher

    File URL: https://doi.org/10.2478/foli-2020-0025
    Download Restriction: no
    File URL: https://libkey.io/10.2478/foli-2020-0025?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Łukasiewicz, Piotr & Orłowski, Arkadiusz, 2004. "Probabilistic models of income distributions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 344(1), pages 146-151.
    2. Michał Brzeziński, 2013. "Parametric Modelling of Income Distribution in Central and Eastern Europe," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 5(3), pages 207-230, September.
    3. Francesco Porro, 2015. "Zenga Distribution and Inequality Ordering," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 44(18), pages 3967-3977, September.
    4. Alberto Arcagni & Francesco Porro, 2013. "On the parameters of Zenga distribution," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 22(3), pages 285-303, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Trzcińska Kamila & Zalewska Elżbieta, 2023. "A Comparative Analysis of Household Incomes of People with Different Levels of Education in Poland and the USA," Folia Oeconomica Stetinensia, Sciendo, vol. 23(2), pages 387-401, December.
    2. 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.
    3. Małgorzata Ćwiek & Kamila Trzcińska, 2023. "Assessment of goodness of fit of income distribution in France and Germany based on the Zenga distribution," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(5), pages 4013-4027, October.
    4. Michał Brzeziński, 2013. "Parametric Modelling of Income Distribution in Central and Eastern Europe," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 5(3), pages 207-230, September.
    5. Vladimir Hlasny & Paolo Verme, 2018. "Top Incomes and Inequality Measurement: A Comparative Analysis of Correction Methods Using the EU SILC Data," Econometrics, MDPI, vol. 6(2), pages 1-21, June.
    6. Lucio De Capitani & Federica Nicolussi & Alessandro Zini, 2017. "Trivariate Burr-III copula with applications to income data," METRON, Springer;Sapienza Università di Roma, vol. 75(1), pages 109-124, April.
    7. Greselin, Francesca & Zitikis, Ricardas, 2015. "Measuring economic inequality and risk: a unifying approach based on personal gambles, societal preferences and references," MPRA Paper 65892, University Library of Munich, Germany.
    8. Brzeziński, Michał & Myck, Michal & Najsztub, Mateusz, 2019. "Reevaluating Distributional Consequences of the Transition to Market Economy in Poland: New Results from Combined Household Survey and Tax Return Data," IZA Discussion Papers 12734, Institute of Labor Economics (IZA).
    9. Christian Kleiber, 2008. "A Guide to the Dagum Distributions," Economic Studies in Inequality, Social Exclusion, and Well-Being, in: Duangkamon Chotikapanich (ed.), Modeling Income Distributions and Lorenz Curves, chapter 6, pages 97-117, Springer.
    10. Alina Jȩdrzejczak & Dorota Pekasiewicz & Wojciech Zieliński, 2023. "The shortest confidence interval for the ratio of quantiles of the Dagum distribution," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 21(2), pages 499-509, June.
    11. Francesca Greselin & Ričardas Zitikis, 2018. "From the Classical Gini Index of Income Inequality to a New Zenga-Type Relative Measure of Risk: A Modeller’s Perspective," Econometrics, MDPI, vol. 6(1), pages 1-20, January.

    More about this item

    Keywords

    income distribution; Zenga distribution; the Gini income inequality index; the Zenga income inequality index;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:vrs:foeste:v:20:y:2020:i:1:p:421-436:n:25. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.sciendo.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.