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The Gini Methodology

A Primer on a Statistical Methodology

  • Shlomo Yitzhaki
  • Edna Schechtman

Part of the Springer Series in Statistics book series (SSS, volume 272)

Table of contents

  1. Front Matter
    Pages i-xvi
  2. Theory

    1. Front Matter
      Pages 9-9
    2. Shlomo Yitzhaki, Edna Schechtman
      Pages 1-8
  3. Theory

    1. Front Matter
      Pages 9-9
    2. Shlomo Yitzhaki, Edna Schechtman
      Pages 11-31
    3. Shlomo Yitzhaki, Edna Schechtman
      Pages 51-73
    4. Shlomo Yitzhaki, Edna Schechtman
      Pages 75-98
    5. Shlomo Yitzhaki, Edna Schechtman
      Pages 99-132
    6. Shlomo Yitzhaki, Edna Schechtman
      Pages 133-176
    7. Shlomo Yitzhaki, Edna Schechtman
      Pages 177-195
    8. Shlomo Yitzhaki, Edna Schechtman
      Pages 197-216
    9. Shlomo Yitzhaki, Edna Schechtman
      Pages 217-232
    10. Shlomo Yitzhaki, Edna Schechtman
      Pages 233-245
  4. Applications

    1. Front Matter
      Pages 247-247
    2. Shlomo Yitzhaki, Edna Schechtman
      Pages 249-252
    3. Shlomo Yitzhaki, Edna Schechtman
      Pages 253-273
    4. Shlomo Yitzhaki, Edna Schechtman
      Pages 275-299
    5. Shlomo Yitzhaki, Edna Schechtman
      Pages 343-364
    6. Shlomo Yitzhaki, Edna Schechtman
      Pages 365-385
    7. Shlomo Yitzhaki, Edna Schechtman
      Pages 387-408
    8. Shlomo Yitzhaki, Edna Schechtman
      Pages 409-424
    9. Shlomo Yitzhaki, Edna Schechtman
      Pages 425-451
    10. Shlomo Yitzhaki, Edna Schechtman
      Pages 453-479
    11. Shlomo Yitzhaki, Edna Schechtman
      Pages 481-498
    12. Shlomo Yitzhaki, Edna Schechtman
      Pages 499-513
  5. Back Matter
    Pages 515-548

About this book

Introduction

Gini's mean difference (GMD) was first introduced by Corrado Gini in 1912 as an alternative measure of variability. GMD and the parameters which are derived from it (such as the Gini coefficient or the concentration ratio) have been in use in the area of income distribution for almost a century. In practice, the use of GMD as a measure of variability is justified whenever the investigator is not ready to impose, without questioning, the convenient world of normality. This makes the GMD of critical importance in the complex research of statisticians, economists, econometricians, and policy makers.

This book focuses on imitating analyses that are based on variance by replacing variance with the GMD and its variants. In this way, the text showcases how almost everything that can be done with the variance as a measure of variability, can be replicated by using Gini. Beyond this, there are marked benefits to utilizing Gini as opposed to other methods. One of the advantages of using Gini methodology is that it provides a unified system that enables the user to learn about various aspects of the underlying distribution. It also provides a systematic method and a unified terminology.

Using Gini methodology can reduce the risk of imposing assumptions that are not supported by the data on the model.  With these benefits in mind the text uses the covariance-based approach, though applications to other approaches are mentioned as well.

Keywords

ANOGI ANOVA Economic models GMD Gini coefficient Gini's mean difference Ordinary Least Squares Regression

Authors and affiliations

  • Shlomo Yitzhaki
    • 1
  • Edna Schechtman
    • 2
  1. 1.Central Bureau of StatisticsJerusalemIsrael
  2. 2.Dept. Industrial EngineeringBen-Gurion University of the NegevBeer-ShevaIsrael

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4614-4720-7
  • Copyright Information Springer Science+Business Media New York 2013
  • Publisher Name Springer, New York, NY
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-1-4614-4719-1
  • Online ISBN 978-1-4614-4720-7
  • Series Print ISSN 0172-7397
  • Buy this book on publisher's site