Exploring competitiveness and wellbeing in Italy by spatial principal component analysis Carlo Cusatelli University of Bari Aldo Moro, Italy Massimiliano Giacalone University of Naples Federico II, Italy Eugenia Nissi University of Chieti-Pescara G. D'Annunzio, Italy This is a section of ASA 2021 Statistics and Information Systems for Policy Evaluation (DOI: 10.36253/978-88-5518-461-8) by Alessandra Petrucci, Bruno Bertaccini, Luigi Fabbris Firenze University Press Firenze 2021 https://doi.org/10.36253/978-88-5518-461-8.27

Available for academic research purposes

Open Access

Copyright Author(s)

Content licence CC BY 4.0

Metadata licence CC0 1.0

This is original content, published for academic research purposes

 Digital edition XML powered by Booksflow

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.

 Well being Spatial Principal Component Analysis (sPCA) Composite Indicators

It is available online at https://doi.org/10.36253/978-88-5518-461-8.27

References Allardt, E. (1981). Experiences from the comparative Scandinavian study, with a bibliography of the project. European Journal of Political Research, 9, pp. 101-111. Andrews, F., Szalai A. (1980). Quality of life: comparative studies, Sage, London, (UK). Bowley, A. (1923). The nature and the purposes of the measurement of social phenomena. P.S. King & Son Ltd., London, (UK). Bureau International du Travail – BIT (1926). Les méthodes d'enquête sur les budgets familiaux, Etudes et Documents, Série N, 9, Genève, (CH). Christian, D.E. (1974). International social indicators: the OECD experience. Social Indicators Research, 1974, 1, pp. 169-186. Engel, E. (1887). La consommation comme mesure du bien-être des individus, des familles, des nations, in Bulletin de l'Institut International de Statistique, Tome II, Héritiers Botta, Roma, (IT). Fourastié, J. (1962). Machinisme et bien-être, niveau de vie et genre de vie en France de 1700 à nos jours. Les éditions de minuit, Paris, (FR). Jombart, T., Devillard, S., Dufour, A. B., Pontier, D. (2008). Revealing cryptic spatial patterns in genetic variability by a new multivariate method. Heredity, 101(1), pp. 92-103. Harris, P., Brunsdon, C., Charlton, M. (2011). Geographically weighted principal components analysis. International Journal of Geographical Information Science, 25(10), pp. 1717-1736. ISTAT (2020), Rapporto BES. Le Play, F. (1855). Les ouvriers européens; études sur les travaux, la vie domestique et la condition morale des populations ouvriéres de l'Europe, précédées d'un exposé de la méthode d'observation, Imprimerie Impériale, Paris, (FR). Mazziotta, M., Pareto, A. (2013). Methods for constructing composite indices: One for all or all for one. Rivista Italiana di Economia Demografia e Statistica, 67(2), pp. 67-80. Moran, P.A.P. (1950), "Notes on Continuous Stochastic Phenomena," Biometrika, 37, pp. 17-33. Tipping, M. E., Bishop, C. M. (1999). Probabilistic principal component analysis. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 61(3), pp. 611-622. Woytinsky, W.S., Woytinsky, E.S. (1953). World population and production. Trends and outlook. The Twentieth Century Fund, New York City, (NY).