Assessing Regional Wellbeing in Italy: An Application of Malmquist–DEA and Self-organizing Map Neural Clustering

Abstract

Interest in the measurement of wellbeing and quality of life has increased in recent decades and a wide range of statistical and econometric techniques have been used to investigate and measure individual quality of life. Following this line of research, this paper uses data envelopment analysis (DEA) to evaluate the wellbeing performance and ranking of the 20 Italian regions from 2005 to 2011. The analysis is based on 12 indicators which represent some of the different aspects of wellbeing. These include economic conditions, labour market conditions, neighbourhood relationships and the environment. The Malmquist indices obtained from the DEA scores are then used to measure changes in wellbeing over time. The results reveal that northern regions have been performing with more efficiency than southern ones. This paper also uses the self-organizing map technique to cluster regions into homogeneous groups where the within-group-object dissimilarity is minimized and the between-group-object dissimilarity is maximized. The clustering analysis confirms a marked duality in regional wellbeing in Italy.

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Notes

  1. 1.

    As Fig. 1 shows, in this analysis the North consists of 12 regions (Piedmont, Aosta Valley, Lombardy, Liguria, Trentino, Friuli, Veneto, Emilia, Tuscany, Umbria, Marche, Lazio) and the South consists of 8 regions (Abruzzo, Molise, Campania, Apulia, Basilicata, Calabria, Sicily, Sardinia.)

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Acknowledgments

We are indebted to Regione Autonoma della Sardegna (L.R. 7/2007), project: ‘Social capital and regional economic divide’ for financial support. All the usual disclaimers apply.

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Carboni, O.A., Russu, P. Assessing Regional Wellbeing in Italy: An Application of Malmquist–DEA and Self-organizing Map Neural Clustering. Soc Indic Res 122, 677–700 (2015). https://doi.org/10.1007/s11205-014-0722-7

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Keywords

  • Quality of life
  • Data envelopment analysis (DEA)
  • Self-organizing map neural network
  • Panel data