Binary segmentation methods based on Gini index: a new approach to the multidimensional analysis of income inequalities

Name: Binary segmentation methods based on Gini index: a new approach to the multidimensional analysis of income inequalities
Brand: Vita e Pensiero
Availability: OnlineOnly

digital

Article

Magazine

STATISTICA & APPLICAZIONI

Section

Open Access

Title

Binary segmentation methods based on Gini index: a new approach to the multidimensional analysis of income inequalities

Authors

Michele Costa, Giuliano Galimberti, Angela Montanari

Publisher

Vita e Pensiero

Format

Article |

Pdf

Pages

Language

English

Online from

06-2016

Issn

1824-6672 (print) | 2283-6659 (digital)

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Description Readers' comments

The role of individual covariates in explaining income differences and poverty structure has been deeply analyzed in the literature.

In this paper we propose to study the effect of socio-demographic and geographical characteristics on subgroup differences by developing a non parametric regression model for income inequalities, based on recursive partitioning methods. Within the philosophy of Classification and Regression Trees we suggest to replace the usually employed splitting criterion, based on the well known decomposition into between and within group deviance components, with a new criterion based on Gini index, which minimizes inequality within subgroups. This solution allows to better detect the covariates which mainly influence income inequality by taking into account all the income distributional aspects and points out specific income profiles.

Keywords: regression trees, Gini inequality index, Gini index decomposition.

Authors biography

Michele Costa, Dipartimento di Scienze Statistiche – Università di Bologna – via Belle Arti, 41, 40126 BOLOGNA (e-mail: michele.costa@unibo.it)
Giuliano Galimberti, Dipartimento di Scienze Statistiche – Università di Bologna – via Belle Arti, 41, 40126 BOLOGNA (e-mail: giuliano.galimberti@unibo.it).
Angela Montanari, Dipartimento di Scienze Statistiche – Università di Bologna – via Belle Arti, 41, 40126 BOLOGNA (e-mail: angela.montanari@unibo.it).

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