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The Cost of Well-Being

Author

Listed:
  • Vincenzo Patrizii

    (University of Florence)

  • Anna Pettini

    (University of Florence)

  • Giuliano Resce

    (Roma Tre University)

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”.

Suggested Citation

  • Vincenzo Patrizii & Anna Pettini & Giuliano Resce, 2017. "The Cost of Well-Being," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 133(3), pages 985-1010, September.
  • Handle: RePEc:spr:soinre:v:133:y:2017:i:3:d:10.1007_s11205-016-1394-2
    DOI: 10.1007/s11205-016-1394-2
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    2. Greco, Salvatore & Ishizaka, Alessio & Resce, Giuliano & Torrisi, Gianpiero, 2020. "Measuring well-being by a multidimensional spatial model in OECD Better Life Index framework," Socio-Economic Planning Sciences, Elsevier, vol. 70(C).
    3. Fusco, Elisa, 2023. "Potential improvements approach in composite indicators construction: The Multi-directional Benefit of the Doubt model," Socio-Economic Planning Sciences, Elsevier, vol. 85(C).
    4. Lance Newey & Rui Torres Oliveira, 2019. "Wellbeing as Emergent from the Leveraging of Polarities: Harnessing Component Interdependencies," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 144(2), pages 575-600, July.
    5. Salvatore Greco & Alessio Ishizaka & Menelaos Tasiou & Gianpiero Torrisi, 2019. "On the Methodological Framework of Composite Indices: A Review of the Issues of Weighting, Aggregation, and Robustness," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 141(1), pages 61-94, January.
    6. Raffaele Lagravinese & Paolo Liberati & Giuliano Resce, 2017. "Exploring health outcomes by stochastic multi-objective acceptability analysis: an application to Italian regions," Working Papers. Collection B: Regional and sectoral economics 1703, Universidade de Vigo, GEN - Governance and Economics research Network.
    7. Shine George & P. P. Anil Kumar, 2022. "Indicator-based assessment of capacity development for disaster preparedness in the Indian context," Environment Systems and Decisions, Springer, vol. 42(3), pages 417-435, September.
    8. Xie, Chengyuan & Jin, Xiaotong, 2023. "The role of digitalization, sustainable environment, natural resources and political globalization towards economic well-being in China, Japan and South Korea," Resources Policy, Elsevier, vol. 83(C).
    9. Resce, Giuliano & Maynard, Diana, 2018. "What matters most to people around the world? Retrieving Better Life Index priorities on Twitter," Technological Forecasting and Social Change, Elsevier, vol. 137(C), pages 61-75.
    10. Paolo Liberati & Giuliano Resce, 2022. "Regional Well-Being and its Inequality in the OECD Member Countries," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 20(3), pages 671-700, September.
    11. Lagravinese, Raffaele & Liberati, Paolo & Resce, Giuliano, 2019. "Exploring health outcomes by stochastic multicriteria acceptability analysis: An application to Italian regions," European Journal of Operational Research, Elsevier, vol. 274(3), pages 1168-1179.

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    More about this item

    Keywords

    Well-being; Data envelopment analysis; Better life index; GDP; Social efficiency;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • E23 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Production
    • I31 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - General Welfare, Well-Being

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