Building Weighted-Domain Composite Indices of Life Satisfaction with Data Envelopment Analysis
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The specialised literature has frequently addressed the relationship between life domains and people’s satisfaction with life. Some researchers have posed questions regarding the importance of domains, therefore interpreting them as weightings and creating domain satisfaction indices. This paper illustrates how Data Envelopment Analysis (DEA) and Multi-Criteria-Decision-Making (MCDM) techniques can be employed to compute domain-based composite indices of life satisfaction and weightings for life domains. Furthermore, an empirical application is performed on a sample of 178 people living in a rural community in Yucatan (Mexico). One of the main features of the aforementioned techniques is that weightings might differ from one individual to another. Accordingly, several weighting schemes are used to compute different life satisfaction indices, in addition to a constant equally-weighted index. Based on the goodness-of-fit criteria commonly used in this literature, our main result is that DEA-MCDM indicators of life satisfaction do not improve the relationship with self-reported life satisfaction in comparison to the equally-weighted index.
KeywordsData Envelopment Analysis Domains of life Life satisfaction indices Multi-Criteria-Decision-Making Weightings
The authors gratefully acknowledge the comments and suggestions made by an anonymous referee and also the financial support from the Spanish Ministry of Economy and Competitiveness (projects ECO2011-30260-C03-01 and ECO2012-32189) and the Generalitat Valenciana (program PROMETEO 2009/098).
- Bernini, C., Guizzardi, A., & Angelini, G. (2012). DEA-like model and common weights approach for the construction of a subjective community well-being indicator. Social Indicators Research (in press). doi: 10.1007/s11205-012-0152-3.
- Campbell, A., Converse, P. E., & Rogers, W. L. (1976). The quality of American life: Perceptions, evaluations, and satisfactions. New York: Russel Sage.Google Scholar
- Cooper, W. W., Seiford, L. M., & Tone, K. (2007). Data Envelopment Analysis. A comprehensive text with models, applications, references and DEA-Solver software. New York: Springer-Business Media-LCC.Google Scholar
- Guardiola, J., García-Rubio, M. A., & Guidi-Gutiérrez, E. (2013b). Water access and subjective well-being: The case of Sucre, Bolivia. Applied Research in Quality of Life (in press). doi: 10.1007/s11482-013-9218.
- Gujarati, D. (1995). Basic econometrics. New York: McGraw-Hill.Google Scholar
- Hsieh, C. M. (2012). Importance is not unimportant: The role of importance weighting in QOL Measures. Social Indicators Research, 109, 267–278.Google Scholar
- Hsieh, C. M. (2013). Issues in evaluating importance weighting in quality of life measures. Social Indicators Research, 110, 681–693.Google Scholar
- Jurado, A., & Perez-Mayo, J. (2012). Construction and evolution of a multidimensional well-being index for the Spanish regions. Social Indicators Research, 107, 259–279.Google Scholar
- Reig-Martínez, E. (2013). Social and economic wellbeing in Europe and the Mediterranean Basin: Building an enlarged Human Development Indicator. Social Indicators Research, 111, 527–547.Google Scholar
- Rojas, M. (2007). The complexity of well-being: A life-satisfaction conception and a domains-of-life approach. In I. Gough & A. McGregor (Eds.), Researching well-being in developing countries. Cambridge: Cambridge University Press.Google Scholar
- Sexton, T. R. (1986). The methodology of data envelopment analysis. In R. H. Silkman (Ed.), Measuring efficiency: An assessment of Data Envelopment Analysis, new directions for program evaluation. San Francisco, CA: Jossey Bass.Google Scholar
- Shephard, R. W. (1970). Theory of cost and production functions. Princeton: Princeton University Press.Google Scholar
- Wu, C. H. (2009). Enhancing quality of life by shifting importance perception among life domains. Journal of Happiness Studies, 10, 37–47.Google Scholar