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Exploring competitiveness and wellbeing in Italy by spatial principal component analysis

  • Carlo Cusatelli
  • Massimiliano Giacalone
  • Eugenia Nissi

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,
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Carlo Cusatelli

University of Bari Aldo Moro, Italy - ORCID: 0000-0003-3770-3060

Massimiliano Giacalone

University of Naples Federico II, Italy - ORCID: 0000-0002-4284-520X

Eugenia Nissi

University of Chieti-Pescara G. D'Annunzio, Italy - ORCID: 0000-0003-3440-601X

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  • Publication Year: 2021
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  • Content License: CC BY 4.0
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Chapter Information

Chapter Title

Exploring competitiveness and wellbeing in Italy by spatial principal component analysis

Authors

Eugenia Nissi, Carlo Cusatelli, Massimiliano Giacalone

DOI

10.36253/978-88-5518-461-8.27

Peer Reviewed

Publication Year

2021

Copyright Information

© 2021 Author(s)

Content License

CC BY 4.0

Metadata License

CC0 1.0

Table of Contents

Book Title

ASA 2021 Statistics and Information Systems for Policy Evaluation

Book Subtitle

BOOK OF SHORT PAPERS of the on-site conference

Editors

Alessandra Petrucci, Bruno Bertaccini, Luigi Fabbris

Peer Reviewed

Publication Year

2021

Copyright Information

© 2021 Author(s)

Content License

CC BY 4.0

Metadata License

CC0 1.0

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 ISSN

2704-601X

Series E-Issn

2704-5846

28

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