Our systems are now restored following recent technical disruption, and we’re working hard to catch up on publishing. We apologise for the inconvenience caused. Find out more

Recommended product

Popular links

Popular links


The κ-Statistics Approach to Income Distribution Analysis

The κ-Statistics Approach to Income Distribution Analysis

The κ-Statistics Approach to Income Distribution Analysis

Fabio Clementi, University of Macerata, Italy
Mauro Gallegati, Marche Polytechnic University, Italy
Lisa Gianmoena, University of Pisa, Italy
Giorgio Kaniadakis, Politecnico di Torino, Italy
Simone Landini, IRES Piemonte – Socioeconomic Research Institute of Piedmont
May 2025
Not yet published - available from May 2025
Paperback
9781009446358
£17.00
GBP
Paperback

    This Element presents the κ-generalized distribution, a statistical model tailored for the analysis of income distribution. Developed over years of collaborative, multidisciplinary research, it clarifies the statistical properties of the model, assesses its empirical validity and compares its effectiveness with other parametric models. It also presents formulas for calculating inequality indices within the κ-generalized framework, including the widely used Gini coefficient and the relatively lesser-known Zanardi index of Lorenz curve asymmetry. Through empirical illustrations, the Element criticizes the conventional application of the Gini index, pointing out its inadequacy in capturing the full spectrum of inequality characteristics. Instead, it advocates the adoption of the Zanardi index, accentuating its ability to capture the inherent heterogeneity and asymmetry in income distributions.

    Product details

    May 2025
    Paperback
    9781009446358
    75 pages
    229 × 152 mm
    Not yet published - available from May 2025

    Table of Contents

    • 1. Introduction
    • 2. New Insights on the Measurement of Inequality
    • 3. The κ-Generalized Distribution
    • 4. Modeling Income Data Using the κ-Generalized Distribution
    • 5. Conclusion
    • References.
      Authors
    • Fabio Clementi , University of Macerata, Italy
    • Mauro Gallegati , Marche Polytechnic University, Italy
    • Lisa Gianmoena , University of Pisa, Italy
    • Giorgio Kaniadakis , Politecnico di Torino, Italy
    • Simone Landini , IRES Piemonte – Socioeconomic Research Institute of Piedmont