Well-being analysis of Italian provinces with spatial principal components

Massimiliano Giacalone, Raffaele Mattera and Eugenia Nissi

Socio-Economic Planning Sciences, 2022, vol. 84, issue C

Abstract: Well-being is a multidimensional concept that cannot be described using a single indicator. By the synthesis of different dimensions it is possible to obtain composite indicators (CIs). Principal Components Analysis (PCA) is one of the most popular multivariate statistical techniques used building CIs. However, the fact that PCA does not take into account the spatial dimension of the phenomenon makes it unsuitable for studying the well-being of urban areas. In this paper, we propose to use Spatial Principal Component Analysis (SPCA) for measuring well-being. The SPCA technique provides principal component scores that summarize both the spatial variability and the spatial autocorrelation structure among the statistical units. In this paper, the SPCA is used to construct well-being CIs for the Italian provinces.

Keywords: Composite indicators; Spatial principal component analysis; BES; Urban areas; Spatial analysis; Territorial disparities (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0038012122001720
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:soceps:v:84:y:2022:i:c:s0038012122001720

DOI: 10.1016/j.seps.2022.101377

Access Statistics for this article

Socio-Economic Planning Sciences is currently edited by Barnett R. Parker

More articles in Socio-Economic Planning Sciences from Elsevier
Bibliographic data for series maintained by Catherine Liu ().