Social Indicators Research

, Volume 102, Issue 2, pp 209–228 | Cite as

The Importance of the Geographic Level of Analysis in the Assessment of the Quality of Life: The Case of Spain

  • Eduardo GonzálezEmail author
  • Ana Cárcaba
  • Juan Ventura


There is a growing literature on the assessment of quality of life conditions in geographically and/or politically divided regions. Sometimes these territories are countries within a specified supranational structure, such as the European Union, for instance, and sometimes they are regions within countries. There is also some research that focuses on the municipal level of analysis, measuring the quality of life in cities. In the end what the researcher obtains is, at best, an average of the living conditions in the specified territory. However, if results are intended to have policy implications, attention should be paid to the variance in living conditions within regions. In this paper we attempt to quantify the relative importance of three different geographic levels of analysis in assessing the quality of life of the Spanish population. The geo-political division in Spain consists firstly of regions called Comunidades Autónomas, which are then divided into provinces which in turn are divided into municipalities. We are interested in evaluating the extent to which the quality of life conditions of an average person living in a given municipality are explained by the province and region in which the municipality is located. To do so, we first construct a composite indicator of quality of life (QoL) for the 643 largest municipalities of Spain using 19 variables which are weighted using Value Efficiency Analysis (VEA). VEA is a refinement of Data Envelopment Analysis (DEA) that imposes some consistency on the weights of the indicators used to construct the aggregate index. The indicators cover aspects related to consumption, social services, housing, transport, environment, labour market, health, culture and leisure, education and security. We then make a variance decomposition of the VEA scores to assess the importance of the three levels of geo-political administration. The results show that the municipal level is the most important of these, accounting for 52% of the variance in QoL. Regions explain 38% while provinces only account for a moderate 10%. Therefore, political action at the regional and municipal level would seem to have a larger impact on QoL indicators.


Quality of life Municipalities Regions DEA VEA Variance components analysis 



we gratefully acknowledge the helpful comments received from an anonymous referee. Financial support for this research was provided by the Spanish Ministerio de Educación y Ciencia (Plan Nacional de I+D+I: SEJ2007-67001/ECON) with FEDER funding.


  1. 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.Google Scholar
  2. Allen, R., Athanassopoulos, R., Dyson, G., & Thanassoulis, E. (1997). Weights restrictions and value judgements in Data Envelopment Analysis: Evolution, development and future directions. Annals of Operations Research, 73, 13–34.CrossRefGoogle Scholar
  3. Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies. Management Science, 39, 1261–1264.Google Scholar
  4. La Caixa. (2001). Anuario Económico de España.Google Scholar
  5. Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2, 429–444.CrossRefGoogle Scholar
  6. Despotis, D. K. (2005a). A reassessment of the human development index via Data Envelopment Analysis. The Journal of the Operational Research Society, 56, 969–980.CrossRefGoogle Scholar
  7. Despotis, D. K. (2005b). Measuring human development via Data Envelopment Analysis: The case of Asia and the Pacific. OMEGA, 33(5), 385–390.CrossRefGoogle Scholar
  8. Dyson, R. G., & Thanassoulis, E. (1988). Reducing weight flexibility in Data Envelopment Analysis. Journal of Operational Research Society, 6, 563–576.Google Scholar
  9. Hagerty, M., Cummins, R. A., Ferriss, A. L., Land, K., Michalos, A. C., Peterson, M., et al. (2001). Quality of life indexes for national policy: Review and agenda for research. Social Indicators Research, 55, 1–96.CrossRefGoogle Scholar
  10. Hagerty, M., & Land, K. (2007). Constructing summary indices of quality of life. A model for the effect of heterogeneous importance weights. Sociological Methods and Research, 35(4), 455–496.CrossRefGoogle Scholar
  11. Halme, M., Joro, T., Korhonen, P., Salo, S., & Wallenius, J. (1999). A value efficiency approach to incorporating preference information in Data Envelopment Analysis. Management Science, 45(1), 103–115.CrossRefGoogle Scholar
  12. 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.CrossRefGoogle Scholar
  13. Hashimoto, A., & Kodama, M. (1997). Has livability of Japan gotten better for 1956–1990? A DEA approach. Social Indicators Research, 40, 359–373.CrossRefGoogle Scholar
  14. Hollingsworth, B., & Smith, P. (2003). Use of ratios in Data Envelopment Analysis. Applied Economics Letters, 10, 733–735.CrossRefGoogle Scholar
  15. Korhonen, P., Siljamäki, A., & Soismaa, M. (1998). Practical aspects of value efficiency analysis. Interim Report IR-98-42. International Institute for Applied Systems Analysis.Google Scholar
  16. Marshall, E., & Shortle, J. (2005). Using DEA and VEA to evaluate quality of life in the Mid-Atlantic States. Agricultural and Resource Economics Review, 34(2), 185–203.Google Scholar
  17. MERCO-Mercociudad. (2008). Monitor Empresarial de Reputación Corporativa. Retrieved February 18, 2009, from http://www.mercoinfo/ver/mercociudad/ranking-sectorial.
  18. 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.CrossRefGoogle Scholar
  19. OCU-Organización de Consumidores y Usuarios. (2007). Encuesta sobre calidad de vida en las ciudades. Compra Maestra, 317, 28–33.Google Scholar
  20. OECD. (2008). Handbook on constructing composite indicators. Paris: OECD Publishing.Google Scholar
  21. Pedraja, F., Salinas, J., & Smith, P. (1997). On the role of weight restrictions in Data Envelopment Analysis. Journal of Productivity Analysis, 8, 215–230.CrossRefGoogle Scholar
  22. Pena, J. B. (1977). Problemas de la medición del bienestar y conceptos afines (Una aplicación al caso español). Madrid: Instituto Nacional de Estadística (INE).Google Scholar
  23. Roback, J. (1982). Wages, rents, and the quality of life. Journal of Political Economy, 90(6), 1257–1278.CrossRefGoogle Scholar
  24. Roll, Y., Cook, W. D., & Golany, B. (1991). Controlling factor weights in Data Envelopment Analysis. IIE Transactions, 23, 2–9.CrossRefGoogle Scholar
  25. Rosen, S. (1979). Wage-based indexes of urban quality of life. In P. Mieszkowski & M. Straszheim (Eds.), Current issues in urban economics. Baltimore: Johns Hopkins University Press.Google Scholar
  26. Sánchez, M. A., & Rodríguez, N. (2003). El bienestar social en los municipios andaluces en 1999. Revista Asturiana de Economía, 27, 99–119.Google Scholar
  27. Sarrico, C. S., & Dyson, R. G. (2004). Restricting virtual weights in Data Envelopment Analysis. European Journal of Operational Research, 159, 17–34.CrossRefGoogle Scholar
  28. Searle, S. R. (1971). Linear models. New York: Wiley.Google Scholar
  29. Searle, S. R., Casella, G., & McCulloch, C. E. (1992). Variance components. New York: Wiley.CrossRefGoogle Scholar
  30. Somarriba, N., & Pena, B. (2009). Synthetic indicators of quality of life in Europe. Social Indicators Research, 94(1), 115–133.CrossRefGoogle Scholar
  31. Stiglitz, J., Sen, A., & Fitoussi, J. P. (2009). Report of the commission on the measurement of economic performance and social progress (CMEPSP). Accessed at on September, 14, 2009.
  32. Thompson, R. G., Singleton, F., Thrall, R., & Smith, B. (1986). Comparative site evaluations for locating a high energy physics lab in Texas. Interfaces, 16, 35–49.CrossRefGoogle Scholar
  33. Wong, Y.-H. B., & Beasley, J. E. (1990). Restricting weight flexibility in Data Envelopment Analysis. Journal of Operational Research Society, 41, 829–835.Google Scholar
  34. Zarzosa, P. (2005). La calidad de vida en los municipios de Valladolid. Valladolid: Diputación Provincial de Valladolid.Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.Business DepartmentUniversity of OviedoOviedoSpain
  2. 2.Accounting DepartmentUniversity of OviedoOviedoSpain

Personalised recommendations