Skip to main content
Log in

Assessing the quality of life in French municipalities: a multidimensional approach

  • S.I. : MCDM 2017
  • Published:
Annals of Operations Research Aims and scope Submit manuscript

Abstract

Quality of life and well-being is a topic of major importance for public policy making and urban planning decisions. However, these concepts entail complex multiple socio-economic aspects, and are ill-defined. Quantitative assessments of quality of life based on composite indicators have become quite popular in academic research and practice. This study presents the application of an approach that combines ideas from multiple criteria decision making and efficiency analysis to the assessment of quality of life at the municipality level. A dataset involving French municipalities in 2012 is used for the analysis based on a rich set of key performance indicators that cover different aspects of quality of life conditions for citizens. The relationship of quality of life performance with the financial strength of the municipalities is also explored.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

Notes

  1. The pessimistic version of model (1) is expressed as \( I_{0*} = \mathop {\hbox{min} }\limits_{{{\mathbf{w}}_{0} \in {\mathcal{W}}}} \frac{{{\mathbf{x}}_{0} {\mathbf{w}}_{0} }}{{\mathop {\hbox{max} }\limits_{i \in A} {\mathbf{x}}_{i} {\mathbf{w}}_{0} }} \). Entani et al. (2002) provided an algorithm to derive the optimal solution explicitly, but this works only when there are no restrictions about the weights (i.e., the weights are simply assumed non-negative).

  2. National Institute for Statistics and Economic Studies (INSEE), Public Equipment database (BPE), Public Finances General Directorate (DGFiP), National Office for Water and Aquatic Environments (ONEMA), Central Directorate of the Judiciary Police (DCPJ) and the National Observatory on Delinquency (ONDRP).

  3. The Kendall’s W coefficient of concordance between the mean scores across the three specifications for the weight restrictions, was 0.986 for municipalities with population at least 500, versus 0.979 for the optimistic results and 0.971 for the pessimistic ones.

  4. The notion of urban unity is based both on building continuity and the number of inhabitants. One calls an urban unit a municipality or a set of municipalities with a continuous building area (no break of more than 200 m between two buildings), which has at least 2000 inhabitants.

References

  • Bigerna, S., & Polinori, P. (2014). Quality of life and efficiency in Italian municipalities. International Advances in Economic Research, 20(3), 347–348.

    Article  Google Scholar 

  • Briec, W. (1999). Hölder distance function and measurement of technical efficiency. Journal of Productivity Analysis, 11(2), 111–131.

    Article  Google Scholar 

  • Burrett, J. (2009). Measuring quality of life in Canadian municipalities. In M. J. Sirgy, R. Phillips, & D. R. Rahtz (Eds.), Community quality-of-life indicators: Best cases III (pp. 155–164). Dordrecht: Springer.

    Chapter  Google Scholar 

  • Cherchye, L., Moesen, W., Rogge, N., & Puyenbroeck, T. Van. (2007). An introduction to ‘Benefit of the Doubt’ composite indicators. Social Indicators Research, 82(1), 111–145.

    Article  Google Scholar 

  • Costa, D. S. J. (2015). Reflective, causal, and composite indicators of quality of life: A conceptual or an empirical distinction? Quality of Life Research, 24(9), 2057–2065.

    Article  Google Scholar 

  • Cummins, R. A., Eckersley, R., Pallant, J., van Vugt, J., & Misajon, R. (2003). Developing a national index of subjective wellbeing: The Australian Unity Wellbeing Index. Social Indicators Research, 64(2), 159–190.

    Article  Google Scholar 

  • de Almeida, P. N., & Dias, L. C. (2012). Value-based DEA models: Application-driven developments. Journal of the Operational Research Society, 63(1), 16–27.

    Article  Google Scholar 

  • Despotis, D. K., & Sotiros, D. (2014). Value-based data envelopment analysis: A piece-wise linear programming approach. International Journal of Multicriteria Decision Making, 4(1), 47–68.

    Article  Google Scholar 

  • Doumpos, M., Zopounidis, C., & Galariotis, E. (2014). Inferring robust decision models in multicriteria classification problems: An experimental analysis. European Journal of Operational Research, 236, 601–611.

    Article  Google Scholar 

  • Doyle, J. R., Green, R. H., & Cook, W. D. (1995). Upper and lower bound evaluation of multiattribute objects: Comparison models using linear programming. Organizational Behavior and Human Decision Processes, 64(3), 261–273.

    Article  Google Scholar 

  • Entani, T., Maeda, Y., & Tanaka, H. (2002). Dual models of interval DEA and its extension to interval data. European Journal of Operational Research, 136(1), 32–45.

    Article  Google Scholar 

  • Fleurbaey, M. (2009). Beyond GDP: The quest for a measure of social welfare. Journal of Economic Literature, 47(4), 1029–1075.

    Article  Google Scholar 

  • Galariotis, E., Guyot, A., Doumpos, M., & Zopounidis, C. (2016). A novel multi-attribute benchmarking approach for assessing the financial performance of local governments: Empirical evidence from France. European Journal of Operational Research, 248(1), 301–317.

    Article  Google Scholar 

  • Gonzalez, E., Carcaba, A., Ventura, J., & Garcia, J. (2011). Measuring quality of life in Spanish municipalities. Local Government Studies, 37(2), 171–197.

    Article  Google Scholar 

  • González, E., Cárcaba, A., & Ventura, J. (2016). Weight constrained DEA measurement of the quality of life in Spanish municipalities in 2011. Social Indicators Research, 136, 1157–1182.

    Article  Google Scholar 

  • Guardiola, J., & Picazo-Tadeo, A. J. (2014). Building weighted-domain composite indices of life satisfaction with data envelopment analysis. Social Indicators Research, 117(1), 257–274.

    Article  Google Scholar 

  • Hagerty, M. R., 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), 1–96.

    Article  Google Scholar 

  • Hashimoto, A., Sugita, T., & Haneda, S. (2009). Evaluating shifts in Japan’s quality-of-life. Socio-Economic Planning Sciences, 43(4), 263–273.

    Article  Google Scholar 

  • Hwang, S.-N., Lee, H.-S., Tang, S.-C., & Hsu, S.-S. (2013). Measuring quality of life using DEA-AR: Focusing on undesirable factors. INFOR: Information Systems and Operational Research, 51(2), 84–91.

    Google Scholar 

  • Kaklauskas, A., Zavadskas, E. K., Radzeviciene, A., Ubarte, I., Podviezko, A., Podvezko, V., et al. (2018). Quality of city life multiple criteria analysis. Cities, 72, 82–93.

    Article  Google Scholar 

  • Keeney, R. L., & Raiffa, H. (1993). Decisions with multiple objectives: Preferences and value trade-offs. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Keeney, R. L., & Sicherman, A. (1976). Assessing and analyzing preferences concerning multiple objectives: An interactive computer program. Behavioral Science, 21(3), 173–182.

    Article  Google Scholar 

  • Martín, J. C., & Mendoza, C. (2013). A DEA approach to measure the quality-of-life in the municipalities of the Canary Islands. Social Indicators Research, 113(1), 335–353.

    Article  Google Scholar 

  • Morais, P., & Camanho, A. S. (2011). Evaluation of performance of European cities with the aim to promote quality of life improvements. Omega, 39(4), 398–409.

    Article  Google Scholar 

  • Pastor, J. T., & Aparicio, J. (2015). Translation invariance in data envelopment analysis. In J. Zhu (Ed.), Data envelopment analysis—A handbook of models and methods (pp. 245–268). New York: Springer.

    Google Scholar 

  • Rogge, N. (2012). Undesirable specialization in the construction of composite policy indicators: The Environmental Performance Index. Ecological Indicators, 23, 143–154.

    Article  Google Scholar 

  • Tavares, G., & Antunes, C. H. (2001). A Tchebycheff DEA model (No. RRR 35-2001). RUTCOR Research Report. New Jersey: Rutgers University.

  • Veenhoven, R. (2007). Subjective measures of well-being. In M. McGillivray (Ed.), Human well-being (pp. 214–239). London: Palgrave Macmillan UK.

    Chapter  Google Scholar 

  • Zhou, P., Ang, B. W., & Poh, K. L. (2007). A mathematical programming approach to constructing composite indicators. Ecological Economics, 62(2), 291–297.

    Article  Google Scholar 

  • 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

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Michalis Doumpos.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Doumpos, M., Guyot, A., Galariotis, E. et al. Assessing the quality of life in French municipalities: a multidimensional approach. Ann Oper Res 293, 789–808 (2020). https://doi.org/10.1007/s10479-018-3068-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-018-3068-8

Keywords

Navigation