Abstract
In the analysis of the difference in the distribution and profiles of the equitable and sustainable well-being, the territorial dimension is a fundamental reading-key for local policies since it allows the areas of advantage or relative deprivation to emerge more accurately. Specifically, in Italy the provincial level coincides with the administrative area of metropolitan cities, which are the subject of growing attention from European and national policies. The BES 2018 report by Italian National Institute of Statistics (ISTAT) has confirmed that from 2015 an improvement in many areas of well-being has been marked, even if territorial differences remain stable both in levels and dynamics. These differences appear in some cases as real structural differences between the North and South of Italy. Then, the measures of equitable and sustainable well-being in the territories allow, in various degrees, to deepen and specify this situation employing synthetic measures of well-being. In this work, we propose a statistical methodology focused on the simultaneous partial least squares structural equation modeling and simultaneous K-means clustering to obtain a composite indicator of Italian well-being and at the same time a classification of Italian territorial micro-areas by means of the just updated provincial data about BES 2018. In this way, the territorial differences of well-being can be more reliably and more exactly defined on the basis of the relationships among all elementary indicators and domains proposed in the analysis of well-being by ISTAT.
Highlights
The territorial dimension is a very important key for local policies in the analysis of the distribution and profiles of the equitable and sustainable well-being, since it allows areas of advantage or relative deprivation to emerge more accurately
Much of the existing literature on well-being indicators lacks the general consensus on what well-being means and how to measure it, many statistical approaches have been adopted to build composite measures as a composite indicator or a composite index through conceptual and mathematical combinations of different elementary indicators based on theoretical frameworks (Salzman 2003; Maggino 2017) and taking into account the availability of data over time and in territorial units (Mazziotta and Pareto 2013)
We employ the methodology focused on the simultaneous Partial Least Squares Structural Equation Modeling (PLS-SEM) and K-Means clustering to obtain a composite indicator of Italian well-being and, simultaneously, a classification of the Italian provinces based on BES 2018 available provincial data
Summary
The territorial dimension is a very important key for local policies in the analysis of the distribution and profiles of the equitable and sustainable well-being, since it allows areas of advantage or relative deprivation to emerge more accurately. The BES 2018 report by Italian National Institute of Statistics (ISTAT) has confirmed that from 2015 an improvement in many areas of well-being has been observed, even if territorial differences remain stable both in the levels and dynamics. The Partial Least Squares Structural Equations Models and, simultaneously, K-means clustering method (PLS-SEM-KM) proposed by Fordellone and Vichi (2020), are employed in order both to build a well-being composite indicator and simultaneously cluster the territorial micro-areas on the basis of different levels of the built composite indicator. NM-PLS is based on the concept of optimal scaling (OS) This methodology is useful for composite indicator building when observed variables are qualitative and quantitative. The paper is structured as follows: in Sect. 1, the well-being concept is defined; in Sect. 2, the measurement of well-being is discussed; in Sect. 3 the PLS-SEM-KM approach is presented; in Sects. 4 and 5 the application on BES data of the PLS-SEM-KM simultaneous approach and the results obtained by the composite indicator construction are shown, respectively; in Sect. 6 some concluding remarks on the proposed methodology and suggestions for future research are given
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