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.
Table des matières
Introduction.- The Revived Interest in the Problems of Income and Wealth Distribution.- Re-incorporating Distributional Issues Into the Main Body of Economic Analysis.- Aim and Contents of this Book.- The Parametric Approach to Income and Wealth Distributional Analysis.- The Idea of a Parametric Model for Income and Wealth Distributions.- Brief History of the Models for Studying Income and Wealth Distributions.- The κ-Generalized Distribution.- Underlying Stochastic Process.- Empirical Results and Comparisons to Alternative Income Distributions.- The κ-Generalized Mixture Model for the Size Distribution of Wealth.- Motivation.- Model Specification.- Moments of the κ-Generalized Mixture Model for Net Wealth Distribution.- The Lorenz Curve and the Gini Index of the Net Wealth Distribution Model.- Empirical Results and Comparison of Finite Mixture Models for Net Wealth Distribution.- Four-Parameter Extensions of the κ-Generalized Distribution.- Definitions and Basic Properties.- Population Characteristics.- Empirical Results and Comparisons to Alternative Four-Parameter Statistical Distributions.- Conclusions.- Appendices.- References.- Author Index.- Subject Index.
A propos de l’auteur
Mauro Gallegati is Professor of Economics at the Polytechnic University of Marche (Ancona, Italy). His research includes business fluctuations, nonlinear dynamics, models of financial fragility, and heterogeneous interacting agents. He has published books and many papers in leading journals in the fields of economics, economic history and history of economic analysis, nonlinear mathematics, applied economics, complexity, and econophysics. He has been involved in several international research projects since 2004.
Fabio Clementi, Ph D, is Assistant Professor of Economics at the University of Macerata (Italy). His main research interests focus on the size distribution of income, wealth and firms, the business cycle analysis, and the empirical validation of agent-based economic models with real-world data. He has also been involved in national and international research projects in the field of agent-based computational economics.
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