- ASA 2021 Statistics and Information Systems for Policy Evaluation
- Edited by Bruno Bertaccini, Luigi Fabbris, Alessandra Petrucci
Exploring competitiveness and wellbeing in Italy by spatial principal component analysis
- Carlo Cusatelli
- Massimiliano Giacalone
- Eugenia Nissi
- © 2021 Author(s) |
- CC BY 4.0
- DOI: 10.36253/978-88-5518-461-8.27
Well being is a multidimensional phenomenon, that cannot be measured by a single descriptive indicator and that, it should be represented by multiple dimensions. It requires, to be measured by combination of different dimensions that can be considered together as components of the phenomenon. This combination can be obtained by applying methodologies knows as Composite Indicators (CIs). CIs are largely used to have a comprehensive view on a phenomenon that cannot be captured by a single indicator. Principal Component Analysis (PCA) is one of the most popular multivariate statistical technique used for reducing data with many dimension, and often well being indicators are obtained using PCA. PCA is implicitly based on a reflective measurement model that it non suitable for all types of indicators. Mazziotta and Pareto (2013) in their paper discuss the use and misuse of PCA for measuring well-being. The classical PCA is not suitable for data collected on the territory because it does not take into account the spatial autocorrelation present in the data. The aim of this paper is to propose the use of Spatial Principal Component Analysis for measuring well being in the Italian Provinces.
- Keywords:
- Well being,
- Spatial Principal Component Analysis (sPCA),
- Composite Indicators,
University of Bari Aldo Moro, Italy - ORCID: 0000-0003-3770-3060
University of Naples Federico II, Italy - ORCID: 0000-0002-4284-520X
University of Chieti-Pescara G. D'Annunzio, Italy - ORCID: 0000-0003-3440-601X
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Chapter Information
Chapter Title
Exploring competitiveness and wellbeing in Italy by spatial principal component analysis
Authors
Carlo Cusatelli, Massimiliano Giacalone, Eugenia Nissi
Language
English
DOI
10.36253/978-88-5518-461-8.27
Peer Reviewed
Publication Year
2021
Copyright Information
© 2021 Author(s)
Content License
Metadata License
Bibliographic Information
Book Title
ASA 2021 Statistics and Information Systems for Policy Evaluation
Book Subtitle
BOOK OF SHORT PAPERS of the on-site conference
Editors
Bruno Bertaccini, Luigi Fabbris, Alessandra Petrucci
Peer Reviewed
Publication Year
2021
Copyright Information
© 2021 Author(s)
Content License
Metadata License
Publisher Name
Firenze University Press
DOI
10.36253/978-88-5518-461-8
eISBN (pdf)
978-88-5518-461-8
eISBN (xml)
978-88-5518-462-5
Series Title
Proceedings e report
Series ISSN
2704-601X
Series E-ISSN
2704-5846