IDEAS home Printed from https://ideas.repec.org/a/bpj/ecqcon/v29y2014i2p119-128n4.html
   My bibliography  Save this article

How We Can Evaluate the Inequality in Flint

Author

Listed:
  • Porro Francesco

    (Department of Statistics and Quantitative Methods, Università degli Studi di Milano-Bicocca, Via Bicocca degli Arcimboldi 8, 20126 Milano, Italy)

Abstract

The inequality analysis plays an important role since the beginning of the last century, in the economic, social and political debate. From the first pioneering paper of Gini, this subject has become more and more fascinating. The several tools proposed in the literature for evaluating the inequality belong basically to two families: on the one hand there are inequality curves which represent (also graphically) the local pattern of inequality in all segments of the considered population; on the other hand, inequality indexes (that often can be derived from a particular inequality curve) which summarize its measure in one number. Different indexes are needed to reveal different viewpoints toward inequality. In this paper, the features of the relatively new inequality I(p) curve are described. Beyond many theoretical results, also an empirical analysis based on real income data of Flint is performed.

Suggested Citation

  • Porro Francesco, 2014. "How We Can Evaluate the Inequality in Flint," Stochastics and Quality Control, De Gruyter, vol. 29(2), pages 119-128, December.
  • Handle: RePEc:bpj:ecqcon:v:29:y:2014:i:2:p:119-128:n:4
    DOI: 10.1515/eqc-2014-0012
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/eqc-2014-0012
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.

    File URL: https://libkey.io/10.1515/eqc-2014-0012?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Stephen P. Jenkins & Richard V. Burkhauser & Shuaizhang Feng & Jeff Larrimore, 2011. "Measuring inequality using censored data: a multiple‐imputation approach to estimation and inference," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 174(1), pages 63-81, January.
    2. Philip Armour & Richard V. Burkhauser & Jeff Larrimore, 2016. "Using The Pareto Distribution To Improve Estimates Of Topcoded Earnings," Economic Inquiry, Western Economic Association International, vol. 54(2), pages 1263-1273, April.
    3. Singh, S K & Maddala, G S, 1976. "A Function for Size Distribution of Incomes," Econometrica, Econometric Society, vol. 44(5), pages 963-970, September.
    4. Pundir, Sudesh & Arora, Sangeeta & Jain, Kanchan, 2005. "Bonferroni Curve and the related statistical inference," Statistics & Probability Letters, Elsevier, vol. 75(2), pages 140-150, November.
    5. Muliere, Pietro & Scarsini, Marco, 1989. "A note on stochastic dominance and inequality measures," Journal of Economic Theory, Elsevier, vol. 49(2), pages 314-323, December.
    6. Atkinson, Anthony B., 1970. "On the measurement of inequality," Journal of Economic Theory, Elsevier, vol. 2(3), pages 244-263, September.
    7. Giovanni Maria Giorgi & Michele Crescenzi, 2005. "A look at the Bonferroni inequality measure in a reliability framework," Econometrics 0507004, University Library of Munich, Germany.
    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. Vladimir Hlasny & Paolo Verme, 2022. "The Impact of Top Incomes Biases on the Measurement of Inequality in the United States," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 84(4), pages 749-788, August.
    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. 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.
    4. Kleiber, Christian, 2005. "The Lorenz curve in economics and econometrics," Technical Reports 2005,30, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    5. 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.
    6. Francesco Andreoli & Eugenio Peluso, 2016. "So close yet so unequal: Reconsidering spatial inequality in U.S. cities," Working Papers 21/2016, University of Verona, Department of Economics.
    7. Fabio Clementi & Mauro Gallegati & Giorgio Kaniadakis, 2010. "A model of personal income distribution with application to Italian data," Empirical Economics, Springer, vol. 39(2), pages 559-591, October.
    8. Schluter, Christian & van Garderen, Kees Jan, 2009. "Edgeworth expansions and normalizing transforms for inequality measures," Journal of Econometrics, Elsevier, vol. 150(1), pages 16-29, May.
    9. Rolf Aaberge & Magne Mogstad, 2011. "Robust inequality comparisons," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 9(3), pages 353-371, September.
    10. Jose Maria Sarabia & Francisco Azpitarte, 2012. "On the relationship between objective and subjective inequality indices and the natural rate of subjective inequality," Working Papers 248, ECINEQ, Society for the Study of Economic Inequality.
    11. Fontanari Andrea & Cirillo Pasquale & Oosterlee Cornelis W., 2020. "Lorenz-generated bivariate Archimedean copulas," Dependence Modeling, De Gruyter, vol. 8(1), pages 186-209, January.
    12. Markus P. A. Schneider, 2013. "Race & Gender Differences in the Experience of Earnings Inequality in the US from 1995 to 2010," Working Papers 1303, New School for Social Research, Department of Economics.
    13. Ekaterina Selezneva & Philippe Van Kerm, 2016. "A distribution-sensitive examination of the gender wage gap in Germany," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 14(1), pages 21-40, March.
    14. Rolf Aaberge, 2009. "Ranking intersecting Lorenz curves," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 33(2), pages 235-259, August.
    15. 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.
    16. Kleiber, Christian, 1997. "The existence of population inequality measures," Economics Letters, Elsevier, vol. 57(1), pages 39-44, November.
    17. W. Henry Chiu, 2021. "Intersecting Lorenz curves and aversion to inverse downside inequality," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 56(3), pages 487-508, April.
    18. Francesco Andreoli & Claudio Zoli, 2020. "From unidimensional to multidimensional inequality: a review," METRON, Springer;Sapienza Università di Roma, vol. 78(1), pages 5-42, April.
    19. Fabio Clementi & Mauro Gallegati & Giorgio Kaniadakis, 2012. "A new model of income distribution: the κ-generalized distribution," Journal of Economics, Springer, vol. 105(1), pages 63-91, January.
    20. Claudio Zoli, 2002. "Inverse stochastic dominance, inequality measurement and Gini indices," Journal of Economics, Springer, vol. 77(1), pages 119-161, December.

    More about this item

    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:bpj:ecqcon:v:29:y:2014:i:2:p:119-128:n:4. 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.degruyter.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.