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Well-Being in the Italian Regions Over Time

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Abstract

In this paper, we define clusters of homogeneous Italian regions with respect to the evolution over time of well-being. In particular we identify three partitions of the Italian regions based on the yearly time series of the economic, social and environmental dimensions of the BES (equitable and sustainable well-being), in the period 2010–2016. The partitions are obtained using a Dynamic Time Warping-based Fuzzy C-Medoids clustering model for multivariate time series. The results show a territorial gap between northern and southern regions with respect to the economic, social and environmental dimensions. The central regions have a diversified behavior with respect to the different dimensions.

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Fig. 1

Source Our elaboration based on (Gibson et al. 2005, 58)

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Notes

  1. “The valuable capacity of the human mind to simplify a complex situation in a compact characterization becomes dangerous when not controlled in terms of definitely stated criteria. With quantitative measurements especially, the definiteness of the result suggests, often misleadingly, a precision and simplicity in the outlines of the object measured. Measurements of national income are subject to this type of illusion and resulting abuse, especially since they deal with matters that are the center of conflict of opposing social groups where the effectiveness of an argument is often contingent upon oversimplification” (Kuznets 1934, 5:6).

  2. For a review of different conceptualizations of well-being, see: Gasper (2007).

  3. The Commission on the Measurement of Economic Performance and Social Progress was set up by French President Nicolas Sarkozy in January 2008. It produced a final report in September 2009 calling for a shift of emphasis from measuring economic production to measuring people’s well-being.

  4. HDI is based on Sen’s capabilities-functionings theory (Sen 1999.) Elaborated by the United Nations Development Programme (UNDP), it take into account three dimensions (considered the basic capabilities central to human development): a long and healthy life, knowledge and a decent standard of living. The HDI was first calculated in 1990 and the HDI rankings of most of the UN countries are published in the Human Development Report.

  5. The aim of GPI is to redefine progress developing an economic indicator that attempts to get much closer to the economic reality that people experience. GPI includes more that twenty positive and negative aspects of our economic lives (Cobb et al. 1995). It uses the same personal consumption data as the GDP but takes into account a number of other factors, such as income distribution or the value of volunteer and housework. The result is a substantively different picture than that presented by the GDP.

  6. HPI, developed by the http://www.neweconomics.org/gen/New Economics Foundation, combines environmental impact with well-being to measure the environmental efficiency with which people live long and happy lives (Marks et al. 2006).

  7. For a detailed summary of the path of the BES and its methodological developments, see Italian National Institute of Statistics (2015).

  8. In the BES project there were two different committees: a Steering Committee, made up of Istat, CNEL experts and stakeholders, which identified the 12 domains; a Scientific Committee, made up of Istat researchers and academic experts, which identified the indicators to measure well-being in each dimension.

  9. As highlighted by Alaimo and Maggino (2020), the method used can have an impact on the results obtained; in particular, the weighting and the aggregation are critically important steps.

  10. This normalisation procedure allows assessing absolute changes over time. Using AMPI, we compute the score of each unit independently of the others, in contrast to the MPI where the mean and standard deviation of the individual indicators are required (Mazziotta and Pareto 2017, 179).

  11. Given a three-way data time array \(\mathbf {X}\equiv \{x_{ijt}:i=1,\ldots ,N;\;j=1,\ldots ,M;\;t=1,\ldots ,T\}\), the normalization of basic indicators is computed as follows:

    $$\begin{aligned} r_{ijt} = \frac{(x_{ijt} - MIN_{x_{j}})}{(MAX_{x_{j}} - MIN_{x_{j}})} *60 +70 \end{aligned}$$
    (1)

    where \(x_{ijt}\) is the value of the indicator \(j^{th}\) in the unit \(i^{th}\) at the time \(t^{th}\); \(MIN_{x_{j}}\) and \(MAX_{x_{j}}\) are the two goalposts of the indicator j and \(r_{ijt}\) is the normalized value. If the indicator j has negative polarity, we compute the complement of formula 1 with respect to 200 (Mazziotta and Pareto 2017, 990). The two goalposts are defined as follows:

    $$\begin{aligned} Ref_{x_{j}} \pm \Delta \quad \text {with} \quad \Delta = \frac{(\underset{it}{Max_{x_{ijt}}} - \underset{it}{Min_{x_{ijt}}})}{2} \end{aligned}$$
    (2)

    where \(Ref_{x_{j}}\) is the reference value, i.e. the value of the indicator \(j^{th}\) in a specific unit \(i^{th}\) at a specific time \(t^{th}\) and \(\underset{it}{Max_{x_{ijt}}}\) and \(\underset{it}{Min_{x_{ijt}}}\) are, respectively, the maximum and the minimum value of the indicator \(j^{th}\) in all units and all time periods. Thus, each indicator assume the value 100 for the reference unit considered in the time occasion considered in all basic indicators; all the other values of each unit for all the time occasions will be expressed in reference to this value, allowing a comparison in time and space. Using this normalization, the range of the normalized values is (70, 130).

  12. Istat calculates the composite indicators used in this work, but for some years now it has not included them in the Report. However, composites are downloadable from the “data” section of each BES Report at the following address: https://www.istat.it/en/well-being-and-sustainability/the-measurement-of-well-being/indicators.

  13. Data for the domains Quality of services and Income and Inequality are available until 2016.

  14. According to the dominant position in literature, sustainable development can be considered as a three-way holistic framework, involving three complex systems, economic, social and environmental, interacting with one another. Some authors have criticized this position, estimating that the pillars that underpin SD are not necessarily three. For a review of the main positions, see: Alaimo and Maggino (2020). Despite the criticism, the three-way model, as elaborated in Agenda 21, remains dominant and hegemonic in literature and it is the basis of the indicator system proposed by the United Nations.

  15. Notice that for analyzing the results we adopt the three groups of Italian regions according to the NUTS (Nomenclature of Territorial Units for Statistics) codes of Italy; i.e.:

    • Northern regions: Piedmont, Aosta Valley, Liguria, Lombardy, autonomous province of Trento, autonomous province of Bolzano, Veneto, Friuli Venezia Giulia and Emilia Romagna;

    • Central Regions: Tuscany, Umbria, Marche and Lazio;

    • Southern regions: Abruzzo, Basilicata, Campania, Puglia, Molise, Calabria, Sardinia and Sicily.

    .

  16. We realize all figures included in this paper, using R and STATA statistical software. In particular, we use the R packages ggplot2 (Wickham 2016) for the representation of time series and corrplot (Wei and Simko 2013) for the membership degrees matrices; the STATA module SPMAP (Pisati 2018) for the cartograms.

  17. It should be remembered that the values of the composites are within a range (70,130). The value only shows the distance between the two time series.

  18. For a detailed description of the indicators, see: https://www.istat.it/en/well-being-and-sustainability/the-measurement-of-well-being/indicators.

  19. In particular, we can observe this situation in the domains: Education and training, Predatory crime, Subjective well-being and Homicides.

  20. In Figs. 12 and 13, we have renamed this domain, using “Innovation”, for reasons of space.

  21. Lazio has the highest values (on average, 18.6%, while national value is 15%) in this indicator for the entire period considered.

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Authors and Affiliations

  1. Department of Social Sciences and Economics, Sapienza University of Rome, Rome, Italy

    Pierpaolo D’Urso, Leonardo Salvatore Alaimo & Riccardo Massari

  2. Italian National Institute of Statistics - Istat, Rome, Italy

    Leonardo Salvatore Alaimo

  3. Department of Political Sciences, LUISS Guido Carli, Rome, Italy

    Livia De Giovanni

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  1. Pierpaolo D’Urso
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  2. Leonardo Salvatore Alaimo
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  3. Livia De Giovanni
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  4. Riccardo Massari
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Correspondence to Pierpaolo D’Urso.

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Appendix

Appendix

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Table 2 Membership degrees: economic, social and environmental dimensions
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D’Urso, P., Alaimo, L.S., De Giovanni, L. et al. Well-Being in the Italian Regions Over Time. Soc Indic Res 161, 599–627 (2022). https://doi.org/10.1007/s11205-020-02384-x

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