Social Indicators Research

, Volume 133, Issue 3, pp 985–1010 | Cite as

The Cost of Well-Being

Article
  • 362 Downloads

Abstract

It is by now common knowledge that in switching from GDP to alternative, multidimensional, measures of collective well-being one can provide a better account of a country’s socio-economic conditions. Such a gain, however, comes at the price of losing output-to-input type of link between well-being and the resources necessary to make it available. Since well-being measures are not meant to be only an exercise in documentation, but also to inform policies and priorities, we propose a method to build a measure of well-being in the form of a single index, as for GDP, which takes into account: (1) the social and environmental costs, not considered in the GDP, and (2) the use of conventional resources (capital and labour), not considered in the currently available multidimensional measures of well-being. We use a Data Envelopment Analysis type of model, integrated with Principal Component Analysis, to evaluate OECD countries’ relative efficiency in providing well-being. Our results show that the costs of producing well-being have a large and significant impact on the resulting index of well-being. Therefore, high efficiency in providing well-being and high income cannot be considered a proxy to each other. In addition, it is shown that countries react differently to the different costs of well-being: poor countries are, on average, more efficient in terms of conventional inputs (labour and capital), while rich countries have higher efficiency indices relative to social and environmental costs. The close to zero correlation between GDP and well-being indices for rich countries provides new support to the “Easterlin paradox”.

Keywords

Well-being Data envelopment analysis Better life index GDP Social efficiency 

JEL Classification

C61 E23 I31 

Notes

Acknowledgments

The authors would like to acknowledge comments made by the referees which have produced a much improved paper.

References

  1. Adler, N., & Golany, B. (2001). Evaluation of deregulated airlines network using data envelopment analysis combined with principal component analysis with an application to Western Europe. European Journal of Operational Research, 132, 260–673.CrossRefGoogle Scholar
  2. Adler, N., & Golany, B. (2007). Pca-Dea. In J. Zhu & D. W. Cook (Eds.), Modeling data irregularities and structural complexities in data envelopment analysis. New York: Springer.Google Scholar
  3. Adler, N., & Yazhemsky, E. (2010). Improving discrimination in data envelopment analysis: PCA–DEA or variable reduction. European Journal of Operational Research, 202, 273–284.CrossRefGoogle Scholar
  4. Arrow, K., Dasgupta, P., Goulder, L., Daily, G., Ehrlich, P., Heal, G., et al. (2004). Are we consuming too much? Journal of Economic Perspectives, 183, 147–172.CrossRefGoogle Scholar
  5. Atkinson, A. (2005). Measurement of government output and productivity for the national accounts: Final report. Palgrave Macmillan: HMSO.Google Scholar
  6. Bandura, R. (2005). Measuring country performance and state behavior: A survey of composite indices. Undp/Ods background paper. New York, NY: United Nations Development Program, Office of Development Studies.Google Scholar
  7. Bandura, R. (2008). A survey of composite indices measuring country performance: 2008 update. Working paper, New York, NY: United Nations Development Program, Office of Development Studies.Google Scholar
  8. Bleys, B. (2012). Beyond Gdp: Classifying alternative measures for progress. Social Indicators Research, 109, 355–376.CrossRefGoogle Scholar
  9. Charnes, A., Cooper, W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2, 429–444.CrossRefGoogle Scholar
  10. Cooper, W. W., Seiford, L. M., & Tone, K. (2007). Data envelopment analysis: A comprehensive text with models, applications, references and DEA-solver software. Berlin: Springer.Google Scholar
  11. Costanza, R., Hart, M., Posner, S., & Talberth, J. (2009). Beyond Gdp: The need for new measures of progress, The Pardee Papers no. 4, Boston University.Google Scholar
  12. Costanza, R., Kubiszewski, I., Giovannini, E., Lovins, H., McGlade, J., Pickett, K. E., Ragnarsdóttir, K., Roberts, D., De Vogli, R., & Wilkinson, R. (2014). Development: Time to leave GDP behind. Nature, 505(7483), 283–285.Google Scholar
  13. Coyle, D. (2014). GDP: A brief but affectionate history. Princeton: Princeton University Press.Google Scholar
  14. Dasgupta, P. (2001). Human well-being and the natural environment. Oxford: Oxford University Press.CrossRefGoogle Scholar
  15. De Beukelaer, C. (2014). Gross domestic problem: The politics behind the world’s most powerful number. Journal of Human Development and Capabilities, 15(2-3), 290–291.CrossRefGoogle Scholar
  16. Despotis, D. K. (2005). A reassessment of the human development index via data envelopment analysis. Journal of the Operational Research Society, 56(8), 969–980.CrossRefGoogle Scholar
  17. Dyson, R. G., Allen, R., Camanho, A. S., Podinovski, V. V., Sarrico, C. S., & Shale, E. A. (2001). Pitfalls and protocols in DEA. European Journal of Operational Research, 132(2), 245–259.CrossRefGoogle Scholar
  18. Easterlin, R. A. (1974). Does economic growth improve the human lot? In A. D. Paul & W. R. Melvin (Eds.), Nations and households in economic growth: Essays in honour of Moses Abramovitz. New York: Academic Press Inc.Google Scholar
  19. Färe, R., & Grosskopf, S. (2004). Modeling undesirable factors in efficiency evaluation: Comment. European Journal of Operational Research, 157, 242–245.CrossRefGoogle Scholar
  20. Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society, Series A (General), 120(3), 253–290.CrossRefGoogle Scholar
  21. Fioramonti, L. (2013). Gross domestic problem: The politics behind the world’s most powerful number. Zed Books.Google Scholar
  22. Fleurbaey, M. (2009). Beyond GDP: The quest for a measure of social welfare. Journal of Economic Literature, 47(4), 1029–1075.Google Scholar
  23. Fuà, G. (1993). Crescita economica: Le insidie delle cifre. Bologna: IlMulino.Google Scholar
  24. Golany, B., & Thore, S. (1997). The economic and social performance of nations: Efficiency and returns to scale. Socio-Economic Planning Sciences, 313, 191–204.CrossRefGoogle Scholar
  25. 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
  26. 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
  27. Hervé, M. (2015). RVAideMemoire: Diverse basic statistical and graphical functions, R package version 0.9-52. http://CRAN.R-project.org/package=RVAideMemoire.
  28. INSEE. (2010). Stiglitz report: The French national statistical agenda. Paris: INSEE.Google Scholar
  29. Karabell, Z. (2014). The leading indicators: A short history of the numbers that rule our world. Simon and Schuster.Google Scholar
  30. Kerényi, Á. (2011). The better life index of the organization for economic co-operation and development. Public Finance Quarterly, 56(4), 518–538.Google Scholar
  31. Kutznets, S. (1934). National income 1929–1932. Letter from the Acting Secretary of Commerce transmitting in Response to Senate Resolution, 22, 7.Google Scholar
  32. Lind, N. C. (2014). Better life index. Encyclopedia of quality of life and well-being research. Springer, Netherlands, pp. 381–382.Google Scholar
  33. Lovell, C. A. K., & Pastor, J. T. (1999). Radial DEA models without inputs or without outputs. European Journal of Operational Research, 118, 46–51.CrossRefGoogle Scholar
  34. Lovell, C. A. K., Pastor, J. T., & Turner, J. A. (1995). Measuring macroeconomic performance in the OECD: A comparison of European and non-European countries. European Journal of Operational Research, 873, 507–518.CrossRefGoogle Scholar
  35. Luptacik, M. (2000). Data envelopment analysis as a tool for measurement of eco-efficiency. In Dockner, E. J., Hartl, R. F., Luptacik, M., & Sorger, G. R. Optimization, dynamics, and economic analysis. Physica-Verlag.Google Scholar
  36. Mahlberg, B., & Obersteiner, M. (2001). Remeasuring the HDI by data envelopement analysis. International Institute for Applied Systems Analysis Interim Report, 01–069.Google Scholar
  37. Mizobuchi, H. (2014). Measuring world better life frontier: A composite indicator for OECD better life index. Social Indicators Research, 118(3), 987–1007.CrossRefGoogle Scholar
  38. Murias, P., Martinez, F., & Miguel, C. (2006). An economic wellbeing index for the Spanish provinces: A data envelopment analysis approach. Social Indicators Research, 77(3), 395–417.CrossRefGoogle Scholar
  39. OECD. (2008). Handbook on constructing composite indicators: Methodology and user guide. Paris: OECD Publishing.Google Scholar
  40. OECD. (2009). Measuring Capital: OECD Manual 2009Second edition. OECD Publishing, Paris. doi: 10.1787/9789264068476-en.
  41. OECD. (2011). Compendium of Oecd well-being indicators. Paris: OECD Publishing.Google Scholar
  42. OECD. (2013a). How’s life?. Paris: OECD Publishing.Google Scholar
  43. OECD. (2013b). Net capital stock. In National accounts at a glance 2013, OECD Publishing, Paris. doi: 10.1787/na_glance-2013-25-en.
  44. OECD. (2015a). Labour force statistics. Paris: OECD Publishing.Google Scholar
  45. OECD. (2015b). Productivity archives. Paris: OECD Publishing.Google Scholar
  46. ONS. (2004). Public sector productivity: Health. Uk: Office of National Statistics.Google Scholar
  47. ONS. (2006). Public sector productivity: Health. UK: Office of National Statistics.Google Scholar
  48. ONS. (2007). Productivity handbook. London: Office for National Statistics, Palgrave Macmillan.Google Scholar
  49. ONS. (2008a). Capital inputs in public sector productivity: Methods, issues and data. In UK Centre for the Measurement of Government Activity. Office of National Statistics, UK.Google Scholar
  50. ONS. (2008b). Public sector productivity: Health care. UK: Office of National Statistics.Google Scholar
  51. ONS. (2011). Measuring what matters, National Statistician’s reflections on the national debate on measuring national well-being. Office of National Statistics, UK.Google Scholar
  52. Osberg, L. (1985). The measurement on economic well-being. In D. Laidler (ed.) Approaches to economic well-being, volume 36, MacDonald Commission, University of Toronto Press, Toronto. http://www.csls.ca/iwb/macdonald.pdf.
  53. Osberg, L., & Sharpe, A. (1998). An index of economic well-being for Canada, Research Report, Applied Research Branch, Human Resources Development Canada.Google Scholar
  54. Pastor, J. T. (1996). Translation invariance in data envelopment analysis: A generalization. Annals of Operations Research, 66, 93–102.CrossRefGoogle Scholar
  55. Ramanathan, R. (2006). Evaluating the comparative performance of countries of the Middle East and North Africa: A DEA application. Socio-Economic Planning Sciences, 40(2), 156–167.CrossRefGoogle Scholar
  56. Schreyer, P. (2004). Capital stocks, capital services and multi-factor productivity measures. OECD Economic Studies, 37, 163–184.CrossRefGoogle Scholar
  57. Segre, E., Rondinella, T., & Mascherin, M. (2011). Well-being in Italian Regions. Measures, civil society consultation and evidence. Social Indicators Research, 102(1), 47–69.CrossRefGoogle Scholar
  58. Thompson, R. G., Singleton, F. D., Thrall, R. M., & Smith, B. A. (1986). Comparative site evaluations for locating a high-energy physics lab in Texas. Interfaces, 16, 35–49.CrossRefGoogle Scholar
  59. Tone, K. (2001). A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research, 130, 498–509.CrossRefGoogle Scholar
  60. Tone, K., & Tsutsui, M.(2006). An efficiency measure of goods and bads in DEA and its application to US electric utilities. Presented at Asia Pacific Productivity Conference 2006, Korea.Google Scholar
  61. Ueda, T., & Hoshiai, Y. (1997). Application of principal component analysis for parsimonious summarization of DEA inputs and/or outputs. Journal of the Operations Research Society of Japan, 40(4), 466–478.Google Scholar
  62. Wackernagel, M., & Rees, W. (1998). Our ecological footprint: Reducing human impact on the earth (No. 9). New Society Publishers.Google Scholar
  63. World Bank. (2011). The changing wealth of nations: Measuring sustainable development in the new millennium. Washington, DC: World Bank Publications.Google Scholar
  64. World Bank. (2015). World development indicators. Washington, DC: World Bank Publications.CrossRefGoogle Scholar
  65. World Economic Forum. (2013). The global competitiveness report 2013–2014. Geneva: WEF.Google Scholar
  66. Yap, G. L. C., Ismail, W. R., & Isa, Z. (2013). An alternative approach to reduce dimensionality in data envelopment analysis. Journal of Modern Applied Statistical Methods, 12, 1–17.CrossRefGoogle Scholar
  67. Zaim, O., Fare, R., & Grosskopf, S. (2001). An economic approach to achievement and improvement indexes. Social Indicators Research, 56, 91–118.CrossRefGoogle Scholar
  68. Zhu, J. (2001). Multidimensional quality-of-life measure with an application to Fortune’s best cities. Socio-Economic Planning Sciences, 35, 263–284.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • Vincenzo Patrizii
    • 1
  • Anna Pettini
    • 1
  • Giuliano Resce
    • 2
  1. 1.Department of Economics and ManagementUniversity of FlorenceFlorenceItaly
  2. 2.Department of EconomicsRoma Tre UniversityRomeItaly

Personalised recommendations