The Distribution of Income and Wealth: Parametric Modeling with the κ-Generalized Family

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This book presents a systematic overview of cutting-edge research in the field of parametric modeling of personal income and wealth distribution, which allows one to represent how income/wealth is distributed within a given population. The estimated parameters may be used to gain insights into the causes of the evolution of income/wealth distribution over time, or to interpret the differences between distributions across countries. Moreover, once a given parametric model has been fitted to a data set, one can straightforwardly compute inequality and poverty measures. Finally, estimated parameters may be used in empirical modeling of the impact of macroeconomic conditions on the evolution of personal income/wealth distribution. In reviewing the state of the art in the field, the authors provide a thorough discussion of parametric models belonging to the “κ-generalized” family, a new and fruitful set of statistical models for the size distribution of income and wealth that they have developed over several years of collaborative and multidisciplinary research. This book will be of interest to all who share the belief that problems of income and wealth distribution merit detailed conceptual and methodological attention.

Author(s): Fabio Clementi, Mauro Gallegati (auth.)
Series: New Economic Windows
Edition: 1
Publisher: Springer International Publishing
Year: 2016

Language: English
Pages: XVI, 177
Tags: Econometrics; Socio- and Econophysics, Population and Evolutionary Models; Organizational Studies, Economic Sociology; Statistics for Business/Economics/Mathematical Finance/Insurance; Game Theory, Economics, Social and Behav. Sciences

Front Matter....Pages i-xvi
Introduction....Pages 1-9
The Parametric Approach to Income and Wealth Distributional Analysis....Pages 11-15
The \(\kappa \) -Generalized Distribution....Pages 17-52
The \(\kappa \) -Generalized Mixture Model for the Size Distribution of Wealth....Pages 53-74
Four-Parameter Extensions of the \(\kappa \) -Generalized Distribution....Pages 75-84
Conclusions....Pages 85-92
Back Matter....Pages 93-177