Assessing Regional Wellbeing in Italy: An Application of Malmquist-DEA and Self-organizing Map Neural Clustering
Academic Article
Publication Date:
2015
Short description:
Assessing Regional Wellbeing in Italy: An Application of Malmquist-DEA and Self-organizing Map Neural Clustering / Carboni, O.A., Russu, P.. - In: SOCIAL INDICATORS RESEARCH. - ISSN 0303-8300. - 122:3(2015), pp. 677-700. [10.1007/s11205-014-0722-7]
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 (MPI) 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 (SOM) 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.
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 (MPI) 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 (SOM) 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.
Iris type:
1.1 Articolo in rivista
Keywords:
Quality of life; Data envelopment analysis (DEA),; Self-organizing map neural network
List of contributors:
Carboni, Oliviero Antonio; Russu, Paolo
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