The Gini Methodology

Beschreibung

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.

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.

Autor*in

Shlomo Yitzhaki

Themen in »The Gini Methodology«

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

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“This book represents a useful primer on the Gini methodology area. It is recommended to all readers who are interested in learning more about econometrics and welfare economics, particularly Gini regressions, Gini-based parameters, inference purposes with many empirical applications.” (Ndéné Ka and Stéphane Mussard, The Journal of Economic Inequality, Vol. 13, 2015)

“The book The Gini methodology by S. Yitzhaki and E. Schechtman is based on Gini’s Mean Deviation (GMD) and the methodology based on it. … To a student acquainted with the usual methods of statistics, the book provides an interesting alternative technique which makes for very good reading. Even at an advanced level, it provides a refreshing perspective through which the traditional methods can examined.” (Sugata Sen Roy, Mathematical Reviews, February, 2014)

"In recent decades we have seen a growing interest of scholars of different cultural background on the results and problems related to Corrado Gini’s scientific production and in particular on the mean difference (GMD) and his famous “concentration ratio” also known as Gini inequality index or Gini coefficient (GI). Now, these scholars have a fresh and clear source from which to draw information and ideas to satiate their thirst for knowledge by reading the very interesting and stimulating book “The G

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