A DEA Approach to Measure the Quality-of-Life in the Municipalities of the Canary Islands

Abstract

The notion of the quality of life has always intrigued economists, sociologists and other researchers in the area of social science. Since the genesis of the definition of Gross Domestic Product (GDP) as a truthful measure of well-being and economic development, other sophisticated methodologies have been proposed in the literature to measure the quality-of-life (QOL) that extend in a multidimensional way this complex concept. Measuring QOL in municipalities consists in finding a set of comparable attributes that can be weighted by some metric in order to construct a synthetic index. Thus, the narrow vision obtained by a single measure as the GDP, in which differences in the QOL cannot be fully analyzed, is overcome. Based upon a refinement of data envelopment analysis (DEA)—the cross-efficiency method, the current paper develops a synthetic QOL index that is based in 19 partial indicators which present the tradeoffs of different dimension for the 87 municipalities of the Canary Islands in Spain. Marginal rates of substitution are calculated to evaluate the tradeoffs on QOL dimensions. A method is also proposed to determine the scores chart of each municipality which can be used as a tool to policy makers in order to establish a program of improving the ranking position of the municipality identifying the critical QOL factors.

This is a preview of subscription content, access via your institution.

Fig. 1

Notes

  1. 1.

    NUTS represents the initials for Nomenclature of Territorial Units for Statistics used by the European Union for statistical purposes. It is classified according to the number of population in the regions of each European country. The European division model establishes five levels, of which NUTS I represent the highest one.

  2. 2.

    The index is calculated as the ratio of the sum of the class marks of educational level and the total population. The class marks range from 0 (illiterate) to 4.5 (Ph.D.).

  3. 3.

    DEA can be applied to scenarios where the data cannot be strictly interpreted as inputs or outputs or there is no direct functional relationship between the variables. In such situations, a general guideline to the classification of the variables is that variables for which lower levels are better are considered inputs, while outputs are those variables for which higher amounts are better.

  4. 4.

    CCR and BCC acronyms are sometimes used in reference to CRS and VRS models. The acronyms come from the initial of the authors of the papers that employed these two different envelopment surfaces (Charnes et al. 1978 and Banker et al. 1984).

  5. 5.

    The different assumptions about the scalar produce distinct envelopment surfaces: VRS, CRS or extensions of these basic models.

  6. 6.

    Marks are obtained for each of the factors as follows: A, B, C and D are given if the value belongs to the first, second, third and fourth quartile, respectively.

References

  1. Adler, N., Friedman, L., & Sinuany-Stern, Z. (2002). Review of ranking methods in the data envelopment analysis context. European Journal of Operational Research, 140, 249–265.

    Article  Google Scholar 

  2. Ali, A., & Seiford, L. M. (1993). The mathematical programming approach to efficiency analysis. In H. O. Fried, C. A. K. Lovell, & S. S. Schmidt (Eds.), The measurement of productive efficiency: Techniques and applications. New York: Oxford University Press.

    Google Scholar 

  3. Bandura, R. (2008). A survey of composite indices measuring country performance: 2008 update. Working paper. United Nations Development Programme (UNDP). Resource document http://web.undp.org/developmentstudies/docs/indices_2008_bandura.pdf. Accessed September, 2011.

  4. Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30, 1078–1092.

    Article  Google Scholar 

  5. Bérenger, V., & Verdier-Chouchane, A. (2007). Multidimensional measures of well-being: standard of living and quality of life across countries. World Development, 35(7), 1259–1276.

    Article  Google Scholar 

  6. Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444.

    Article  Google Scholar 

  7. Charnes, A., Cooper, W. W., Huang, Z. M., & Wei, Q. L. (1989a). Cone ratio data envelopment analysis and multi-objective programming. International Journal of Systems Science, 20, 1099–1118.

    Article  Google Scholar 

  8. Charnes, A., Cooper, W. W., & Li, S. (1989b). Using DEA to evaluate relative efficiencies in the economic performance of Chinese cities. Socio-Economic Planning Sciences, 23, 325–344.

    Article  Google Scholar 

  9. Charnes, A., Cooper, W., Lewin, A. Y., & Seiford, L. M. (1994). Data envelopment analysis. Theory, methodology and applications. Boston: Kluwer Academic.

    Book  Google Scholar 

  10. Coelli, T. (1996). A guide to DEAP version 2.1: A data envelopment analysis (computer) program. CEPA working paper 96/08. Centre for efficiency and productivity analysis. University of New England: Armidale.

  11. Coelli, T., Rao, D. S. P., & Battese, G. E. (1998). An introduction to efficiency and productivity analysis. Boston: Kluwer Academic.

    Book  Google Scholar 

  12. Cooper, W., Sieford, L., & Tone, K. (2000). Data envelopment analysis. A comprehensive text with models, applications, reference and DEA–Solver software. Norwell: Kluwer Academic Publishers.

    Google Scholar 

  13. Doyle, J. R., & Green, R. (1994). Efficiency and cross-efficiency in data envelopment analysis: Derivatives, meanings and uses. Journal of the Operational Research Society, 45(5), 567–578.

    Google Scholar 

  14. Easterlin, R. A. (1974). Does economic growth improve the human lot? In P. A. David & M. W. Reder (Eds.), Nations and households in economic growth: Essays in honour of Moses Abramovitz. New York: Academic Press, Inc.

    Google Scholar 

  15. Golany, B., & Thore, S. (1997). The economic and social performance of nations: efficiency and returns to scale. Socio- Economic Planning Sciences, 31, 191–204.

    Article  Google Scholar 

  16. González, E., Cárcaba, A., & Ventura, J. (2011). Quality of life ranking of Spanish municipalities. Revista de Economía Aplicada, 19, 123–148.

    Google Scholar 

  17. Hagerty, M. R., Cummnis, R. A., Ferriss, A. L., Land, K., Michalos, A. C., & Peterson, M. (2001). Quality of life indexes for national policy: Review and agenda for research. Social Indicators Research, 55(1), 1–96.

    Article  Google Scholar 

  18. 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.

    Article  Google Scholar 

  19. Kaufmann D., Kraay A., & Zoido-Lobatón P. (1999). Aggregating governance indicators. Policy research working papers. World Bank Institute. Resource document http://info.worldbank.org/governance/wgi/pdf/govind.pdf. Accessed November, 2011.

  20. OECD. (2008). Handbook on constructing composite indicators: Methodology and user guide. Paris: OECD Publishing.

    Google Scholar 

  21. Puolamaa, M., Kaplas, M., & Reinikainen, T. (1996). Index of economic friendliness. A methodological study. Helsinki: Eurostat.

    Google Scholar 

  22. Rahman, T., Mittelhammer, R., & Wandschneider, P. (2005). Measuring the quality of life across countries. Research paper no. 2005/06. World Institute for Development Economics Research.

  23. Royuela, V., Suriñach, J., & Reyes, M. (2003). Measuring quality of life in small areas over different periods of time: Analysis of the province of Barcelona. Social Indicators Research, 64(1), 51–74.

    Article  Google Scholar 

  24. Saltelli, A. (2007). Composite indicators between analysis and advocacy. Social Indicators Research, 81, 65–77.

    Article  Google Scholar 

  25. Seiford, L. M., & Thrall, R. M. (1990). Recent developments in data envelopment analysis: The mathematical programming approach to frontier analysis. Journal of Econometrics, 46, 7–38.

    Article  Google Scholar 

  26. Sexton, T. R., Silkman, R. H., & Hogan, A. J. (1986). Data envelopment analysis: Critique and extensions. In R. H. Silkman (Ed.), Measuring efficiency: An assessment of data envelopment analysis (pp. 73–105). San Francisco: Jossey-Bass.

    Google Scholar 

  27. Somarriba, N. (2008). Aproximación a la calidad de vida social e individual en la Europa comunitaria. Doctoral thesis, Universidad de Valladolid. Resource document http://www.eumed.net/tesis/2010/mnsa/index.htm. Accessed December, 2011.

  28. Stiglitz, J. E., Sen, A., & Fitoussi J. P. (2009). Report by the commission on the measurement of economic performance and social progress. Resource document http://www.stiglitz-sen-fitoussi.fr/documents/rapport_anglais.pdf. Accessed September, 2011.

  29. Sueyoshi, T. C. (1992). Measuring the industrial performance of Chinese cities by data envelopment analysis. Socio- Economic Planning Sciences, 26, 75–88.

    Article  Google Scholar 

  30. Zhu, J. (2001). Multidimensional quality-of-life measure with an application to Fortune’s best cities. Socio-Economic Planning Sciences, 35(4), 263–284.

    Article  Google Scholar 

Download references

Acknowledgments

The authors thank Eduardo Gonzalez, professor of the Department of Business Administration at the University of Oviedo for providing most of the data used in this research. Additional gratitude extends to Professor Alex Michalos and two anonymous referees for their valuable comments. The usual disclaimer applies.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Juan Carlos Martín.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Martín, J.C., Mendoza, C. A DEA Approach to Measure the Quality-of-Life in the Municipalities of the Canary Islands. Soc Indic Res 113, 335–353 (2013). https://doi.org/10.1007/s11205-012-0096-7

Download citation

Keywords

  • Quality-of-life
  • Well-being
  • DEA
  • Cross-efficiency
  • Synthetic index