Ebook: The Gini Methodology: A Primer on a Statistical Methodology

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.

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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.

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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.

Content:
Front Matter....Pages i-xvi
Front Matter....Pages 9-9
Introduction....Pages 1-8
Front Matter....Pages 9-9
More Than a Dozen Alternative Ways of Spelling Gini....Pages 11-31
The Gini Equivalents of the Covariance, the Correlation, and the Regression Coefficient....Pages 33-49
Decompositions of the GMD....Pages 51-73
The Lorenz Curve and the Concentration Curve....Pages 75-98
The Extended Gini Family of Measures....Pages 99-132
Gini Simple Regressions....Pages 133-176
Multiple Regressions....Pages 177-195
Inference on Gini-Based Parameters: Estimation....Pages 197-216
Inference on Gini-Based Parameters: Testing....Pages 217-232
Inference on Lorenz and on Concentration Curves....Pages 233-245
Front Matter....Pages 247-247
Introduction to Applications....Pages 249-252
Social Welfare, Relative Deprivation, and the Gini Coefficient....Pages 253-273
Policy Analysis....Pages 275-299
Policy Analysis Using the Decomposition of the Gini by Non-marginal Analysis....Pages 301-341
Incorporating Poverty in Policy Analysis: The Marginal Analysis Case....Pages 343-364
Introduction to Applications of the GMD and the Lorenz Curve in Finance....Pages 365-385
The Mean-Gini Portfolio and the Pricing of Capital Assets....Pages 387-408
Applications of Gini Methodology in Regression Analysis....Pages 409-424
Gini’s Multiple Regressions: Two Approaches and Their Interaction....Pages 425-451
Front Matter....Pages 247-247
Mixed OLS, Gini, and Extended Gini Regressions....Pages 453-479
An Application in Statistics: ANOGI....Pages 481-498
Suggestions for Further Research....Pages 499-513
Back Matter....Pages 515-548

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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.

Content:
Front Matter....Pages i-xvi
Front Matter....Pages 9-9
Introduction....Pages 1-8
Front Matter....Pages 9-9
More Than a Dozen Alternative Ways of Spelling Gini....Pages 11-31
The Gini Equivalents of the Covariance, the Correlation, and the Regression Coefficient....Pages 33-49
Decompositions of the GMD....Pages 51-73
The Lorenz Curve and the Concentration Curve....Pages 75-98
The Extended Gini Family of Measures....Pages 99-132
Gini Simple Regressions....Pages 133-176
Multiple Regressions....Pages 177-195
Inference on Gini-Based Parameters: Estimation....Pages 197-216
Inference on Gini-Based Parameters: Testing....Pages 217-232
Inference on Lorenz and on Concentration Curves....Pages 233-245
Front Matter....Pages 247-247
Introduction to Applications....Pages 249-252
Social Welfare, Relative Deprivation, and the Gini Coefficient....Pages 253-273
Policy Analysis....Pages 275-299
Policy Analysis Using the Decomposition of the Gini by Non-marginal Analysis....Pages 301-341
Incorporating Poverty in Policy Analysis: The Marginal Analysis Case....Pages 343-364
Introduction to Applications of the GMD and the Lorenz Curve in Finance....Pages 365-385
The Mean-Gini Portfolio and the Pricing of Capital Assets....Pages 387-408
Applications of Gini Methodology in Regression Analysis....Pages 409-424
Gini’s Multiple Regressions: Two Approaches and Their Interaction....Pages 425-451
Front Matter....Pages 247-247
Mixed OLS, Gini, and Extended Gini Regressions....Pages 453-479
An Application in Statistics: ANOGI....Pages 481-498
Suggestions for Further Research....Pages 499-513
Back Matter....Pages 515-548
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