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“Beyond GDP” Effects on National Subjective Well-Being of OECD Countries

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Abstract

Some authors claim that maximizing subjective well-being is a more meaningful social objective than maximizing GDP and that other factors beyond income play a major role in defining well-being. In this work, we study two issues connected with this claim, looking at the context of OECD member countries. We look at the crowded category of proposed, “beyond GDP” policy-controlled factors, searching for evidence that some might be major determinants of national average subjective well-being. We also seek to compare any such effect with that of GDP, in order to evaluate if these factors have a better chance of leading to a maximization of well-being than GDP itself. In our analyses, we make use of partial order methods that have been rarely applied to this field of study. They seem particularly appropriate to the case, as well-being and its components are generally theorized as strongly multidimensional while standard modeling strategies require a great deal of compromise when working with many potential regressors and non-trivial levels of multicollinearity.

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Notes

  1. See Stevenson and Wolfers (2008), the subsequent comments by Becker et al. (2008) and, with different arguments, Oswald (2008).

  2. “Unlike economic indicators, which locate a person’s well-being primarily in the material realm of marketplace production and consumption, well- being indicators assess the full range of inputs to the quality of life, from social relationships to spirituality and meaning, from material consumption to feelings of relaxation and security” (Diener and Tov 2012, p. 3).

  3. Attempts of putting subjective well-being at the center of policy decision making have well documented foundations in Bentham’s quantitative hedonism (Kahneman and Riis 2005). For a distinction between hedonic and eudaimonic research on well-being see, for instance, Deci and Ryan (2008).

  4. See for example Beckerman (1994) and Daly (1995) on manmade and natural capital.

  5. For the source of data and the metric of SWB used here, see Sect. 2.1.

  6. In other words, it has already been used as a major component of the “quantitative evidence on the social situation” (OECD 2014, p. 78) used by very relevant institutions.

  7. http://www.oecd.org/social/statistics.htm. See the publication for definitions and methodology.

  8. http://www.oecd-ilibrary.org/environment/data/oecd-environment-statistics_env-data-en.

  9. http://www.oecd.org/gender/data/.

  10. See Edwards et al. (2000) for a definition of formative and generative models in the development of constructs (indicators).

  11. A detailed formalization of the mathematical background and the algorithm of POSAC can be found in Shye and Amar (1985) and Shye (1985) and the algorithm it is currently supported in the statistical package Systat.

  12. At this stage, indicators can be ranked so that the direction of their relation with the underlying construct is uniform.

  13. Clearly, by determining the coordinates (x, y) of each profile, POSAC attributes that profile a score on the J axis and a score on the L axis.

  14. In a Hasse diagram, the four incomparable profiles can be put side to side in any configuration and the method itself does not provide the information required to distinguish between the incomparability of \(a^{\left( k \right)}\) and \(a^{\left( r \right)}\) and that of \(a^{\left( k \right)}\) and \(a^{\left( l \right)}\).

  15. For an extensive list with detailed comparison see Brüggemann and Patil (2011) and Brüggemann et al. (2005).

  16. See Figure 1 of supplementary material.

  17. See Figure 3 of supplementary material.

  18. See Figure 4 of supplementary material.

  19. See Figure 2 of supplementary material.

  20. The reader should keep in mind that the social cohesion indicators provided by OECD originate from surveys and report a subjective feeling of the respondent, so it could be appropriate to define this as “perception of social cohesion”.

  21. See Figure 6 of supplementary material.

  22. See Figure 5 of supplementary material.

  23. See Figure 9 of supplementary material.

  24. See Figure 7 of supplementary material.

  25. See Table 1 of supplementary material.

  26. See Figure 10 of supplementary material.

  27. See Figure 8 of supplementary material.

  28. See Figures 11 and 13 of supplementary material.

  29. See Figure 14 of supplementary material.

  30. See Figure 12 of supplementary material.

  31. See Greene (2011, p. 160); BIC is a criterion for model selection that balances the likelihood of the model with the number of parameters in it. A model with a lower BIC should be preferred over a similar model with a higher BIC as the introduction of additional parameters in the second model did not provide sufficient improvements in likelihood.

  32. As defined in the data section of this work.

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  1. Department of Economics and Business Studies, University of Genoa, Genoa, Italy

    Barbara Cavalletti & Matteo Corsi

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  1. Barbara Cavalletti
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  2. Matteo Corsi
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Correspondence to Matteo Corsi.

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Cavalletti, B., Corsi, M. “Beyond GDP” Effects on National Subjective Well-Being of OECD Countries. Soc Indic Res 136, 931–966 (2018). https://doi.org/10.1007/s11205-016-1477-0

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