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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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.)
Aigner, D. J., Lovell, C. A. K., & Schmidt, P. (1977). Formulation and estimation of stochastic frontier production models. Journal of Econometrics, 6, 21–37.
Alesina, A., Di Tella, R., & McCulloch, R. (2001). Inequality and happiness: Are European and American different? NBER Working Paper, No 8198, Cambridge.
Ali, A. I. (1994). Computational aspects of data envelopment analysis. In A. Charnes, W. W. Cooper, A. Y. Lewin, & L. M. Seiford (Eds.), DEA: Theory, methodology and applications (pp. 63–88). Boston: Kluwer Academic Publishers.
Andrews, F. M., & Withey, S. B. (1976). Social indicators of well-being: America’s perception of quality of life. New York: Plenum Press.
Andrienko G., Andrienko, N., Bremm, S., Schreck, T., von Landesberger, T., Bak, P., & Keim, D. (2010). Space-in-time and time-in-space self-organizing maps for exploring spatiotemporal patterns. Eurographics/IEEE-VGTC Symposium on Visualization, 29(3), 913–922.
Anuario Social de España, Fundación La Caixa, Barcelona: various years.
Banker, P. C., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30, 1078–1092.
Bernini, C., Guizzardi, A., & Angelini, G. (2013). DEA-like model and common weights approach for the construction of a subjective community well-being indicator. Social Indicators Research, 114, 405–424.
Bosetti, V., Cassinelli, M., & Lanza, A. (2003). Using data envelopment analysis to evaluate environmentally conscious tourism management. In International conference on tourism and sustainable development, CRENoS Cagliari, Sassari University and World Bank, September 19–20, Chia, Sardinia.
Boyer, R., & Savageu, D. (1981). Places rated almanac: Your guide to finding the best places to live in America. New York: Rand McNally.
Brajša-Žganec, A., Merkaš, M., & Šverko, I. (2011). Quality of life and leisure activities: How do leisure activities contribute to subjective well-being? Social Indicators Research, 102(1), 81–91.
Campbell, A., Converse, P. F., & Rodgers, W. R. (1976). The quality of American life. New York: Sage.
Carboni, O. A., & Russu, P. (2014). Measuring environmental and economic efficiency in Italy: An application of the malmquist-DEA and grey forecasting model. CRENoS, WP 2014_01.
Charnes, A., & Cooper, W. W. (1985). Preface to topics in data envelopment analysis. Annals of Operation Research, 2, 59–94.
Chon, Tae-Soo. (2011). Self-organizing maps applied to ecological sciences. Ecological Informatics, 6, 50–61.
Christakopoulou, S., Dawson, J., & Gari, A. (2001). The community well-being questionnaire: Theoretical context and initial assessment of its reliability and validity. Social Indicators Research, 56(3), 321–345.
Chung, Y. H., Färe, R., & Grosskopf, S. (1997). Productivity and undesirable outputs: A directional distance function approach. Journal of Environmental Management, 51, 229–240.
Cobb, C., Halstead, T., & Rowe, J. (1995). The genuine progress indicator: Summary of data and methodology. San Francisco: Redefining Progress.
Coelli, T. J. (1996). A guide to DEAP version 2.1: A data envelopment analysis (computer) program. University of New England, Department of Econometrics, CEPA Working Paper No. 8/96, Armidale, NSW 2351, Australia. http://www.une.edu.au/econometrics/cepa2.htm#software.
Coelli, T., Rao, D. S. P., O’Donnel, C. J., & Battese, G. E. (2005). An introduction to efficiency and productivity analysis (2nd ed.). Berlin: Springer.
Cook, W. D., & Kress, M. (1990). A data envelopment model for aggregating preference rankings. Management Science, 36(11), 1302–1310.
Cullinane, K., Song, D. W., & Wang, T. F. (2004). An application of DEA windows analysis to container port production efficiency. Review of Network Economy, 32, 184–206.
Dasgupta, P. (2000). Population resources and welfare: An exploration into reproductive and environmental externalities. Working paper.
Diener, E. (1984). Subjective well being. Psychological Bulletin, 95(3), 542–575.
Diener, E., Oishi, S., & Lucas, R. (2003). Personality, culture and subjective well-being: Emotional and cognitive evaluations of life. Annual Review of Psychology, 54, 403–425.
Diener, E., & Suh, E. (1997). Measuring quality of life: Economic, social and subjective indicators. Social Indicators Research, 40, 189–216.
Diener, E., Suh, E. M., Lucas, R. E., & Smith, H. L. (1999). Subjective well-being: Three decades of progress. Psychological Bulletin, 125(2), 276–302.
Dolnicar, S., Yanamandram, V., & Cliff, C. (2011). The contribution of vacations to quality of life. Annals of Tourism Research, 39(1), 59–83.
Erikson, R. (1993). Description of inequality: The Swedish approach to welfare research. In M. Nussbaum & A. Sen (Eds.), The quality of life. Oxford: Clarendon Press.
Erikson, R., Hansen, E. J., Ringen, S., & Uusitalo, H. (1987). The Scandinavian model: Welfare states and welfare research. New York: M.E. Sharpe.
Fare, R., & Grosskopf, S. (2004). Modelling undesirable factors in efficiency evaluation: Comment. European Journal of Operational Research, 157, 242–245.
Färe, R., & Grosskopf, S. (2003). Non-parametric productivity analysis with undesirable outputs: Comment. American Journal of Agriculture Economics, 85, 1070–1074.
Färe, R., & Grosskopf, S. (2009). A comment on weak disposability in nonparametric production analysis. American Journal of Agriculture Economics, 91, 535–538.
Färe, R. S., Grosskopf, S., & Lovell, C. A. K. (1994). Production frontiers. Cambridge: Cambridge University Press.
Färe, R., Grosskopf, S., & Pasurka, C. (1986). Effects on relative efficiency in electric power generation due to environmental controls. Resources and Energy, 8, 167–184.
Färe, R., Grosskopf, S., & Pasurka, C. (1989). The effect of environmental regulations on the efficiency of electric utilities: 1969 versus 1975. Applied Economics, 21, 225–235.
Gillingham, R., & Reece, W. S. (1979). A new approach to quality of life measurement. Urban Studies, 16(3), 329–332.
Gonzalez, E., Carcaba, A., & Ventura, J. (2011). The importance of the geographic level of analysis in the assessment of the quality of life: The case of Spain. Social Indicators Research, 102(2), 209–228.
Hashimoto, A. (1999). Proposing non-uniform evaluation in social systems analysis. Discussion paper series, 827 (Institute of Policy and Planning Science, University of Tsukuba).
Hashimoto, A., & Ishikawa, H. (1993). Using DEA to evaluate the state of society as measured by multiple social indicators. Socio-Economic Planning Sciences, 27, 257–268.
Hashimoto, A., & Kodama, M. (1997). Has liveability of Japan gotten better for 1956–1990? A DEA approach. Social Indicators Research, 40, 359–373.
Hsu, K. C., & Li, S. T. (2010). Clustering spatial–temporal precipitation data using wavelet transform and self-organizing map neural network. Advances in Water Resources, 33, 190–200.
Hua, Z., & Bin, Y. (2007). DEA with undesirable factors. In: Zhu, J., & Cook, W. D. (Eds.), Modeling data irregularities and structural complexities in data envelopment analysis (Chap. 6). Springer Science Series.
Istituto Tagliacarne, various years. http://www.tagliacarne.it/.
Iwasaki, Y. (2007). Leisure and quality of life in an international and multicultural context: What are major pathways linking leisure to quality of life? Social Indicators Research, 82, 233–264.
Jurado, A., & Perez-Mayo, J. (2012). Construction and evolution of a multidimensional well-being index for Spanish Regions. Social Indicators Research, 107(2), 259–279.
Kahneman, D., & Krueger, A. (2006). Developments in the measurement of subjective well being. Journal of Economic Perspective, 20, 3–24.
Kohonen, T. (1982a). Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43, 59–69.
Kohonen, T. (1982b). Analysis of a simple self-organizing process. Biological Cybernetics, 44, 135–140.
Köksal, C. D., & Aksu, A. A. (2007). Efficiency evaluation of a group travel agencies with data envelopment analysis DEA: A case study in the Antalya region, Turkey. Tourism Management, 28, 830–834.
Koua, E. L., & Kraak, M. J. (2008). An integrated exploratory geovisualization environment based on self-organizing map. In Agarwal P., Skupin A., (Eds.), Self-organising maps: Applications in geographic information science (pp. 45–66). New York: Wiley.
Krutilla, K., & Reuveny, R. (2002). The quality of life in the dynamics of economic development. Environment and Development Economics, 7, 23–45.
Kuosmanen, T., & Podinovski, V. (2009). Weak disposability in nonparametric production analysis: Reply to Färe and Grosskopf. American Journal of Agricultural Economics, 91, 539–545.
Lawless, N., & Lucas, R. (2010). Predictors of regional well-being: A county level analysis. Social Indicators Research, 101, 341–357.
Legambiente Ecosistema Urbano. (2012). XIX Rapporto sulla qualità ambientale dei comuni capoluogo di provincia.
Leung, L., & Lee, P. S. N. (2005). Multiple determinants of life quality: The roles of Internet activities, use of new media, social support, and leisure activities. Telematics and Informatics, 22(3), 161–180.
Liu, B. C. (1976). Quality of life indicators in US metropolitan areas: A statistical analysis. New York: Praeger Publishers.
Lloyd, K. M., & Auld, C. J. (2002). The role of leisure in determining quality of life: Issues of content and measurement. Social Indicators Research, 57, 43–71.
Malmquist, S. (1953). Index numbers and indifference surfaces. Trabajos de Estatistica, 4, 209–242.
Meeusen, W., & van den Broek, J. (1977). Efficiency estimation from Cobb–Douglas production functions with composed error. International Economic Review, 18(2), 435–444.
Min, H., Min, H., & Joo, S. J. (2008). A data envelopment analysis-based balanced scorecard for measuring the comparative efficiency of Korean luxury hotels. International Journal of Quality and Reliability Management, 25, 349–365.
Morawetz, D. (1977). Income distribution and self-rated happiness: Some empirical evidence. The Economic Journal, 87, 511–522.
Murias, P., Martínez, F., & Miguel, C. (2006). An economic well-being index for the Spanish provinces: A data envelopment analysis approach. Social Indicators Research, 77(3), 395–417.
Nimrod, G., & Adoni, H. (2006). Leisure-styles and life satisfaction among recent retirees in Israel. Ageing and Society, 26, 607–630.
Nordhaus, W. D. (2002). The health of nations: The contribution of improved health to living standards. In K. M. Murphy & R. H. Topel (Eds.), Exceptional returns. Chicago: University of Chicago Press.
Osberg, L., & Sharpe, A. (1998). An index of economic well-being for Canada. Applied Research Branch, Research Paper R-99-3E, Human Resources Development Canada, Ottawa, Ontario.
Osberg, L., & Sharpe, A. (1999). An index of economic well-being for Canada and the United States. Paper presented at the annual meeting of the American Economic Association, New York, January.
Pena, J. B. (1977). Problemas de la medición del bienestar y conceptos afines. Una aplicación al Caso Español. (I.N.E.: Madrid).
Roback, J. (1982). Wages, rents, and quality of life. Journal of Political Economy, 90, 1257–1278.
Rodrìguez, A., Làtkovà, P., & Sun, Y. Y. (2008). The relationship between leisure and life satisfaction: Application of activity and need theory. Social Indicators Research, 86, 163–175.
Rogerson, R. J. (1999). Quality of life and city competitiveness. Urban Studies, 36(5–6), 969–985.
Rosen, S. (1979). Wage-based indexes of urban quality of life. In P. Mieszkowsi & M. Stratzheim (Eds.), Current issues in urban economics (pp. 74–104). Baltimore: John Hopkins Press.
Scheel, H. (2001). Undesirable outputs in efficiency valuations. European Journal of Operational Research, 132, 400–410.
Seiford, L., & Zhu, J. (2002). Modelling undesirable factors in efficiency evaluation. European Journal of Operational Research, 142, 16–20.
Sen, A. (2000). Desarrollo y libertad. Barcelona: Editorial Planeta.
Sirgy, M. J. (2002). The psychology of quality of life. Dordrecht: Kluwer.
Sirgy, M. J., & Cornwell, T. (2001). Further validation of the Sirgy et al. ’s measure of community quality of life. Social Indicators Research, 56(12), 5–143.
Sirgy, M. J., & Cornwell, T. (2002). How neighborhood features affect quality of life. Social Indicators Research, 59(1), 79–102.
Sirgy, M. J., Gao, T., & Young, R. F. (2008). How residents’ satisfaction with community services influence quality of life (QOL) outcomes? Applied Research in Quality of Life, 3(2), 81–106.
Sirgy, M. J., Rahtz, D., Cicic, M., & Underwood, R. (2000). A method for assessing residents’ satisfaction with community-based services: A quality-of-life perspective. Social Indicators Research, 49, 279–316.
Sirgy, M. J., Widgery, R. N., Lee, D. J., & Yu, G. B. (2010). Developing a measure of community wellbeing based on perceptions of impact in various life domains. Social Indicators Research, 96(2), 295–311.
Slottje, D. (1991). Measuring the quality of life across countries. The Review of Economics and Statistics, 73(4), 684–693.
Sole24ore, various years, Dossier sull’Italia: Qualità di vita. www.ilsole24ore.com.
Spielman, S. E., & Thill, J. C. (2008). Social area analysis, data mining, and GIS. Computers, Environment and Urban Systems, 32(2), 110–122.
Townsend, P. (1979). Poverty on the United Kingdom. London: Penguin.
Tyceta, D. (1996). On the measurement of the environmental performance of firms: A literature review and a productive efficiency perspective. Journal of Environmental Management, 46, 281–308.
Van Praag, B. (2007). Perspectives from the happiness literature and the role of new instruments for policy analysis. CESifo Economic Studies, 53, 42–68.
Van Praag, B. (2011). Well-being inequality and reference groups: An agenda for new research. Journal of Economic Inequality, 9, 111–127.
Van Praag, B. M. S., Frijters, P., & Ferrer-i-Carbonell, A. (2003). The anatomy of subjective well-being. Journal of Economic Behaviour & Organization, 51, 29–49.
Viscovery SOMine lite Version 2.1. (1998). User’s manual Eudaptics software GMBH, Austria.
Yiengprugsawan, Y., Seubsman, S., Khamman, S., Lim, L., & Sleigh, A. (2010). Personal wellbeing index in a national cohort of 87,134 Thai adults. Social Indicators Research, 98, 201–215.
Zaim, O. (2004). Measuring environmental performance of state manufacturing through changes in pollution intensities: A DEA framework. Ecological Economics, 48, 37–47.
Zaim, O., & Taskin, F. (2000). A Kuznets curve in environmental efficiency: An application on OECD countries. Environmental & Resource Economics, 17, 21–36.
Zhu, J. (2001). Multidimensional quality of life measure with an application to fortune’s best cities. Socio-Economic Planning Sciences, 35, 263–284.
Zofio, J. L., & Prieto, A. M. (2001). Environmental efficiency and regulatory standards: The case of CO2 emissions from OECD industries. Resource and Energy Economics, 23, 63–83.
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
- Quality of life
- Data envelopment analysis (DEA)
- Self-organizing map neural network
- Panel data