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Regional well-being indicators and dispersion from a multidimensional perspective: evidence from Italy

Abstract

Interest in measuring well-being, as opposed to the more traditional economic indicators of growth, has increased significantly over recent years. This paper aims to contribute to the empirical literature on well-being indicators and dispersion across regions in terms of both quality of life and economic progress. Italian regions are used as case studies and 10 different multidimensional determinants of well-being are considered: culture and free time; education; employment; environment; availability of essential public services; health; material living conditions; personal security; research and innovation; and the strength of social relations. We calculated, by applying principal component analysis, synthetic indicators for each well-being determinant and for each region so as to generate—again, by means of the same methodology—an index of overall well-being. The study was conducted for every year over the period 2004–2010. Results clearly show that differences in well-being between regions are not necessarily in line with those based on per capita GDP, suggesting a need to give more attention to quality-of-life features of economic progress in public policy goals and design. Furthermore, the paper looked at dispersion across regions and regional rank mobility over the same period. Italian regions have tended to become more similar in terms of well-being over time, but no evidence of significant intra-distributional mobility emerges.

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Notes

  1. 1.

    The best-known alternative is the Human Development Index (HDI), which combines GDP per capita, literacy and average life expectancy into a single index.

  2. 2.

    For a survey on the latter approach, see Bandura (2008), Stiglitz et al. (2009), Annoni and Weziak-Bialowolska (2012), Costanza et al. (2009). Bleys (2012) proposes a scheme for classifying 23 of the indicators available in the literature.

  3. 3.

    The BES database is available at http://www.istat.it.

  4. 4.

    These dimensions are: “subjective well-being”; “politics and institutions”; and “landscape and cultural heritage”.

  5. 5.

    Before assuming the replicability of the current study and keeping only the first component, the cross-checking with the Kaiser criterion and the Scree test is always required.

  6. 6.

    We find similar results by considering the minimum and maximum values of the principal component(s) extracted for each year.

  7. 7.

    The literature refers to the long-run trend of the coefficient of variation as \(\sigma \)-convergence (Friedman 1992; Sala-I-Martin 1994): by adapting the Sala-I-Martin (1996) approach on GDP convergence across countries, regions are converging in the sense of \(\sigma \) if the dispersion of their well-being decreases over time. However, some authors assess convergence by referring to the mobility of units (countries or regions, for instance) over time within the given distribution of the relevant variable, known as \(\beta \)-convergence: if the relevant variable in regions which initially have a less advantageous position exhibits faster growth than the relevant variable in those regions that initially show higher values, there is absolute \(\beta \)-convergence. Although the concepts of \(\sigma \) and \(\beta \)-convergence are related, they do not always show up together: as a matter of fact, the existence of \(\beta \)-convergence is a necessary, but not sufficient, condition for the existence of \(\sigma \)-convergence. Mobility within the distribution (\(\beta \)-convergence) does not ensure that dispersion diminishes over time (\(\sigma \)-convergence), while on the other hand, \(\sigma \)-convergence implies (is sufficient for) \(\beta \)-convergence, but is not a necessary condition (Sala-I-Martin 1996).

  8. 8.

    Some authors use Kendall’s index of rank concordance as a nonparametric approach for assessing \(\beta \)-convergence, known as \(\gamma \)-convergence (Boyle and McCarthy 1997; Marchante et al. 2006).

  9. 9.

    If more than one principal component is extracted for a dimension, we have more than one synthetic indicator for that ambit; for the RWBI, we have a single indicator.

  10. 10.

    Alternatively, on the left side of the equation, one may see the predicted values of the RWBI. However, this does not make any difference in our analysis.

  11. 11.

    The main disadvantage of this approach is that the effect on the dependent variable is related to the composite variables \(\theta _{d}\) instead of \(X_{d}\).

  12. 12.

    The validation of the analysis is further assessed by verifying that, for each dimension, a) the structure of correlations meets the necessary threshold, with results from Kaiser’s measure of sampling adequacy falling into the acceptable range (above 0.50) for each year, both for the overall set of variables and individual variables, and b) the Bartlett test shows that nonzero correlations exist (see Hair et al. 2014, 103). For the sake of brevity, we neither comment nor show the results of these tests, but they are available from the authors on request.

  13. 13.

    The correlation matrix for each well-being dimension and detailed information on the results of the principal component analysis are available from the authors on request.

  14. 14.

    Italy is often looked at in terms of its sub-national areas. These are constituted by eight regions (Valle d’Aosta, Piemonte, Lombardia, Trentino-Alto Adige, Friuli-Venezia Giulia, Liguria, Emilia Romagna and Veneto) for the North; four regions for the Centre (Toscana, Marche, Umbria and Lazio) and eight regions for the South, or Mezzogiorno (Abruzzo, Molise, Campania, Puglia, Basilicata, Calabria, Sicilia and Sardegna).

  15. 15.

    The coefficient of variation is also used in literature as a measure of inequality (Jordà and Sarabia 2014; Cowell and Fiorio 2011). We refer to both terms as synonyms.

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Author information

Correspondence to Rosanna Nisticò.

Additional information

We would like to dedicate this article to the memory of Giovanni Anania.

We wish to thank Giovanni Anania, Michele Capriati, Sierdjan Koster, Philip McCann and Viktor Venhorst for their helpful comments on an earlier draft of the paper.

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Appendix

Appendix

See Table  8.

Table 8 Well-being dimensions: indicators, definitions and sources (database subsections in parenthesis)

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Ferrara, A.R., Nisticò, R. Regional well-being indicators and dispersion from a multidimensional perspective: evidence from Italy. Ann Reg Sci 55, 373–420 (2015). https://doi.org/10.1007/s00168-015-0704-y

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