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OECD: One or Many? Ranking Countries with a Composite Well-Being Indicator

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Abstract

This paper provides a composite indicator of well-being for the 35 OECD countries, as well as South Africa, Russia and Brazil for the period 2013–2016, considering data on 10 different well-being domains from the OECD Better Life Index. In a first stage, countries are ranked according to their well-being indicator, constructed combining Data Envelopment Analysis with the Benefit-of-the-Doubt principle and Multi-Criteria-Decision-Making techniques. In a second stage, well-being clubs are identified using hierarchical cluster analysis, revealing that well-being is highly polarised. Moreover, as well-being affects people, population size is accounted for in the cluster analysis, showing that for the largest proportion of people in our sample, well-being is remarkably low.

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Notes

  1. Available at www.oecdbetterlifeindex.org.

  2. The debates on whether or not to weight, and how to weight are not free from controversy. However, we agree with Hsieh (2004) that, despite its shortcomings, weighting is a useful practice that merits consideration in empirical analyses of well-being.

  3. In this respect, the BLI webpage allows users to weight the different domains in order to create their own overall well-being indicator according to their individual preferences. While this is an interesting option for users, we do not believe it is particularly useful for scientific analyses. Furthermore, users’ weights might differ from those of the people interviewed in the BLI dataset.

  4. In addition to all OECD countries, we decided to also include Brazil, Russia and South Africa in our analysis because they account for an important share of the total world population, particularly the first two. Furthermore, it is well known that the discriminating capacity of the DEA-BoD techniques employed in our empirical strategy increases with the number of observations. Accordingly, including the three above-mentioned countries improves the suitability of this technique for the purpose of our research.

  5. Furthermore, Mizobuchi (2017b) also employed the BLI dataset to decompose cross-country differences in subjective well-being as the result of both socio-economic factors and sensitivity of happiness. However, we do not believe that paper represents a precedent for our research since its main purpose is not to compute a well-being composite indicator.

  6. Existing literature in the field of well-being assessment at the macro level, e.g., using data on countries or other geographical areas, has tended to disregard the idea that well-being actually affects people’s daily life. However, in our view, it is important to take into account the number of people that experience low/high well-being, since it can yield a more realistic picture of how life is in the world than mere indicators can. For example, well-being in highly developed economies such as Switzerland or the European Nordic countries is probably high, but in terms of relative population they represent only a small fraction; on the contrary, developing countries such as Brazil and Russia are highly populated, but we might expect them to have relatively low levels of well-being.

  7. These data are publicly available at www.oecdbetterlifeindex.org. At the time of writing this paper, the last update was made in June 2016.

  8. This database is similar in nature to the BLI, although the information provided differs in terms of indicators and time coverage.

  9. For the homicide rate indicator, the 10th and 90th percentiles are considered to capture its variability.

  10. It is worth pointing out that Latvia joined the OECD in 2016, the year in which the non-OECD country South Africa was first included in the BLI dataset. Accordingly, data for these two countries refer to 2016.

  11. In recent decades, DEA has been used in hundreds of empirical papers to assess different facets of performance (see Cook and Seiford 2009; Sueyoshi et al. 2017 for reviews), or even to build composite indicators. The flexibility of this technique in assessing performance in different frameworks constitutes one of its main advantages. However, as with other techniques in the field of social sciences, DEA also has its limitations. In addition to those specifically mentioned later in this paper regarding the construction of composite indicators to establish rankings, general limitations of DEA include its deterministic nature and the sensitivity of the results to the presence of outliers and/or influential observations in the sample. The latter drawback requires careful treatment of the dataset before computing the performance indicators; e.g., identifying and deleting outliers. Further details on DEA are provided in Cooper et al. (2007).

  12. In more technical terms, in order to translate the conventional input/output DEA formulation into an appropriate model for computing a composite well-being indicator, it must be assumed that each country has what Lovell et al. (1995: 509), following Koopmans (1951), called a helmsman. According to the former authors, ‘… this assumption implies that one input, the helmsman, provides varying amounts of several services [well-being domains in our case study], and that every country has exactly one helmsman. Thus the input vector collapses to a scalar, and the value of this scalar is unity for every country’.

  13. This approach thus avoids the use of exogenous weighting schemes based on expert opinions or other ad hoc criteria; e.g. the Analytic Hierarchy Process (AHP).

  14. In practice, this can be done by computing a series of composite well-being indicators with enough values for the parameter t (always ranging from zero to one), e.g., at intervals of 0.001, as we do in this paper, and then calculating the average as the sole well-being indicator.

  15. In contrast, non-hierarchical approaches are less popular for implementation reasons. They require the establishment of a central point or seed, from which distance to the observations is measured. In most cases, however, the identification of this central point is complicated.

  16. Other alternatives have also been used in the empirical literature to build the DEA-BoD composite indicator, such as the Slack-based measure (SBM) introduced by Tone (2001), e.g., Reig-Martínez et al. (2011), Reig-Martínez (2013), Guardiola and Picazo-Tadeo (2014).

  17. As shown by the Spearman’s rank correlations displayed in Table 3, and as already commented on, the well-being scores obtained in the collective optimum and the most penalised country optimum scenarios lead to very similar rankings of countries as with the integer solution. Results at the country level from the collective optimum and the most penalised country optimum scenarios are available upon request.

  18. In Mizobuchi (2014), the number of countries with well-being scores of one ranges between 6 and 18 (out of 34), depending on the model considered, pointing to a worrying lack of discriminating capacity of the DEA-BoD models employed. Additionally, countries such as Turkey and Brazil are awarded well-being scores equal to one in most of the scenarios considered, which seems quite implausible.

  19. These two additional correlations reach values of 0.460 and 0.581, respectively; moreover, both are also statistically significant at the 1% confidence level.

  20. Kernel distributions and clustering analyses have also been carried out using the DEA-BoD-MCDM scores obtained in the collective optimum and most penalised country optimum scenarios, yielding no significant differences with respect to those presented in the DEA-BoD-MCDM-integer scenario. The results for these alternative analyses are available upon request.

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Acknowledgements

We acknowledge financial support from the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund (ECO2014-55221-P and ECO2016-75237-R), as well as from the Valencian government (PROMETEOII/2014/053). Moreover, we are thankful for useful comments and suggestions received from the referees.

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Authors and Affiliations

  1. Department of Economics and INTECO Joint Research Unit, University Jaume I, Castellón de la Plana, Spain

    Jesús Peiró-Palomino

  2. Department of Applied Economics II and INTECO Joint Research Unit, University of Valencia, Valencia, Spain

    Andrés J. Picazo-Tadeo

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  1. Jesús Peiró-Palomino
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Correspondence to Andrés J. Picazo-Tadeo.

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Appendix

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Table 7 Description of the well-being dimensions and their indicators
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Peiró-Palomino, J., Picazo-Tadeo, A.J. OECD: One or Many? Ranking Countries with a Composite Well-Being Indicator. Soc Indic Res 139, 847–869 (2018). https://doi.org/10.1007/s11205-017-1747-5

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