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Beyond GDP: Using Equivalent Incomes to Measure Well-Being in Europe

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Abstract

It has become widely accepted that focusing exclusively on income growth may lead to a too narrow-sighted measure of changes in well-being. People care about other dimensions of life, such as their health, employment, social interactions and personal safety. Moreover, an exclusive focus on income growth remains blind to the distribution of income and well-being in the society. We propose therefore a set of five principles for a richer measure of well-being. In particular, we advocate the use of a measure based on “equivalent incomes”, which satisfies these principles. We discuss and illustrate how this equivalent income approach can be implemented in Europe, using the ESS data for 2008 and 2010. We find that introducing inequality aversion and including other dimensions in the analysis leads to a remarkably different perspective on the growth of well-being in Europe.

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Notes

  1. Browsing through the recent issues of this journal, for instance, one can find easily numerous examples of each of these approaches. Diener and Suh (1997) and Bleys (2012) provide surveys and a classification. A schematic overview of the various approaches that extend beyond GDP is provided by Fleurbaey and Blanchet (2013, chapter 1).

  2. More formal and rigorous treatments can be found, inter alia, in Fleurbaey and Maniquet (2011), Schokkaert et al. (2011), Fleurbaey and Blanchet (2013), and Decancq et al. (2015, forthcoming).

  3. Neumayer (1999) makes a similar argument and Fleurbaey and Blanchet (2013, chapter 2) provide an extensive discussion. The conclusion that sustainability should be measured separately can also been found in the Stiglitz–Sen–Fitoussi (2009) report—and is underlying the initiatives at the EU and the OECD level.

  4. Even communitarians will argue that community ties are essential mainly because they define what constitutes an individual. The individual remains the central reference point. A recent challenge to our focus on individual human well-being is perhaps the animal rights movement. This issue is not taken up in this paper.

  5. As documented by a large scale survey organised by the World Bank, even the poorest among the globally poor see poverty and well-being as multidimensional notions (Narayan 2000). A similar conclusion has been reached by a qualitative study carried out by Eurobarometer in Europe (Eurobarometer 2011).

  6. Somarriba and Pena (2009) illustrate the low consistency across three methods to compute the weights of a composite well-being indicator for various European countries. Decancq et al. (2013) provide a comparison of various weighting schemes based on Flemish data. See Decancq and Lugo (2013) for a critical survey of different methods to select a weighting scheme for a composite well-being indicator.

  7. See, for instance, Hausman (2007, p. 50): “A state of affairs in which those who are otherwise worse off are healthier than those who are otherwise more fortunate is more just rather than less just than a state of affairs which is exactly the same except that health is equally distributed”. Or, in another context, Pogge (2002, p. 11): “Consider institutional schemes under which half the population are poor and half have no access to higher education. We may plausibly judge such an order to be more unjust when the two groups coincide than when they are disjoint (so that no one bears both hardships)”. Ferreira and Lugo (2013) discuss the importance of cumulative deprivation for multidimensional poverty measurement. See Decancq (2014) on the impact of neglecting cumulative deprivations when measuring well-being in Russia.

  8. This principle is aligned with Recommendation 8 of the Stiglitz-Sen-Fitoussi report (2009): “Surveys should be designed to assess the links between various quality-of-life domains for each person, and this information should be used when designing policies in various fields”.

  9. When we talk about preferences in this paper, we refer to the individual conception of a good life. This Rawlsian concept includes the values and normative convictions of the individuals and should not be reduced to their mere egoistic self-interest, nor is it necessarily "revealed" in their choice behaviour.

  10. To be precise, researchers still disagree about the relative importance of cognitive and affective components in the question and about the reliability of the questionnaire method (see, for example, Kahneman and Krueger 2006). Various proposals have been made to measure social progress using subjective well-being measures such as life satisfaction and happiness. Recently, the World Happiness Report (Helliwell et al. 2013) provides a comparison of happiness across the world. Veenhoven (1996) proposes to compute a “happy life expectancy” (life expectancy times average happiness on a [0–1] scale).

  11. We face a danger of terminological confusion here. The term “equivalent income” is often used to indicate the “income corrected by using an equivalence scale” (mainly to take account of differences in household composition). Although closely related, this does not perfectly coincide with the interpretation we will give to the concept of equivalent incomes. However, the interpretation given here has a long tradition, and the term is explicitly used at least since the work of King (1983)—see Fleurbaey and Blanchet (2013) for a sketch of the historical background. In the light of that literature it would be equally confusing not to use the term “equivalent income” in this paper. We therefore prefer to stick to it, but warn the reader for the possible confusion.

  12. The second principle is not necessary to support the idea of equivalent income. Equivalent incomes can be calculated for any choice of reference values of the non-income dimensions (see, for example, Decancq et al. forthcoming). However, the choice of the “best” value as reference is an attractive choice. See Fleurbaey and Blanchet (2013) for an extensive discussion.

  13. Interestingly, in this approach of retrieving opinions on the good life from subjective life satisfaction regressions, there is an echo of the conclusion of Diener and Suh (1997 p. 214) in their comparison of economic, social and subjective indicators of quality of life: “[A] complete understanding of objective indicators and how to select them requires that we understand people’s values, and have knowledge about how objective indicators influence people’s experience of well-being”.

  14. A more flexible model which relaxes the assumption \(\tau_{y} = 0\) and allows for a flexible Box-Cox transformation of the income dimension, is feasible but unnecessarily complicates the mathematical expressions for the equivalent income in the following. Moreover, as will become clear below, the simplifying assumption of a logarithmic transformation of income is not rejected by our data.

  15. As in all regression analyses, changing the scale of the x-variables will also change the scale of the estimated coefficients. These should therefore be interpreted cautiously. However, as Eq. (5) shows, it is the product \((\beta + \gamma^{\prime } x_{i} )^{\prime } f(x_{i} )\) that appears in Eq. (5). Therefore, changes in the scaling of the independent variables will not affect the calculated equivalent incomes.

  16. As the survey was organized at the end of the calendar year, it is supposed to describe the situation of the individuals in 2008 and 2010.

  17. Within the European context, alternative data sets to compute equivalent incomes are the Statistics on Income and Living Conditions (SILC), the Survey of Health, Ageing and Retirement in Europe (SHARE), and the European Quality of Life Survey (EQLS) (Eurofound 2012). The available SILC data do not contain a life satisfaction question and do not allow to estimate preferences. The SILC 2013 contains an ad-hoc module on well-being, but at the moment of writing these data were not yet available. The SHARE data only cover the population that is 50 years and older. Interestingly however, the latter data set includes also a set of so-called anchoring vignettes that allow to correct for scale heterogeneity in self-evaluations of life satisfaction (see Angelini et al. 2012).

  18. The country and time-specific cut-offs of these income deciles are taken from an external source. After converting the reported deciles to their corresponding monetary values (by taking the midpoint of each interval), some discrepancies remain between the ranking of the countries according to the average income in the survey and the well-established macro-figures. In addition, corrections for price differences have to be made to allow for comparisons between countries. Therefore we apply an uprating procedure of all incomes such that the country average total household income per capita coincides with the “Real net national income at the price levels and PPPs of 2005” as provided by the OECD on 28/1/2013. “Appendix” provides more details.

  19. “Experience of life” and in particular happiness, could in principle be included in the analysis, as the ESS also includes a happiness question “Taking all things together, how happy would you say you are?”. We have decided not to take this variable as a dimension of life because of its very high correlation with life satisfaction. This common finding may hint at the confusion between the evaluative question on life satisfaction and the affective question on happiness. See also Fleurbaey and Blanchet (2013).

  20. Countries included in the analysis are Belgium (BE), Switzerland (CH), Czech Republic (CZ), Germany (DE), Denmark (DK), Estonia (EE), Spain (ES), Finland (FI), France (FR), Great Britain (GB), Greece (GR), Hungary (HU), Netherlands (NL), Norway (NO), Poland (PL), Russian Federation (RU), Sweden (SE) and Slovenia (SI).

  21. Estimating the same equation with ordinary least squares leads to very similar results.

  22. A more flexible procedure with a Box-Cox transformation for income leads to an estimate of the transformation coefficient τ y  = −0.01. This is indeed very close to 0, supporting the choice of the logarithmic transformation (see also Layard et al. 2008).

  23. We started from the full set of 15 possible interaction effects between the five dimensions and the three considered socio-demographic variables, and then we have dropped consecutively the least significant terms until reaching the presented parsimonious model with all remaining interactions significant at the 10 % level.

  24. The result about safety may seem surprising. One possible explanation is that our “safety”-variable basically measures feelings of unsafety. If these “feelings” are different for males and females (in that females feel more unsafe in similar neighbourhoods) this may have an effect on the estimated coefficient. However, this is not a problem for the calculation of equivalent incomes, since in this calculation the stronger feelings of unsafety among women will be captured by the observed values of \(f(x_{i} )\).

  25. See also the theoretical analysis in Fleurbaey and Blanchet (2013).

  26. The problem does not occur with the other two methods of estimating preferences. Since each of these methods has its own weak and strong points, it would certainly be advisable to compare the results obtained with each of them. At this moment, however, there is no data set that allows us to do so.

  27. As the income data in the European Social Survey are not perfect, the Gini coefficients in Table 9 do not correspond perfectly to the Gini coefficients obtained from macro data (see “Appendix”).

  28. Downloaded on 31 January 2013 from the Eurostat website http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=ilc_di12.

  29. Downloaded on 28 January 2013 from the OECD website http://www.oecd-ilibrary.org/economics/real-net-national-income_2074384x-table16.

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Acknowledgments

We gratefully acknowledge financial support from Belspo for the MEQIN project. We thank an anonymous referee, Romina Boarini, Marleen De Smedt, Marc Fleurbaey, Frank Vandenbroucke and seminar participants in Brussels, Cali, Frankfurt, Ispra, Leuven, London, Paris, Rome, and Stirling for useful comments.

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Authors and Affiliations

  1. Herman Deleeck Centre for Social Policy, University of Antwerp, Antwerp, Belgium

    Koen Decancq

  2. CORE, Université catholique de Louvain, Louvain-La-Neuve, Belgium

    Koen Decancq & Erik Schokkaert

  3. Department of Economics, KU Leuven, Leuven, Belgium

    Koen Decancq & Erik Schokkaert

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  1. Koen Decancq
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  2. Erik Schokkaert
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Correspondence to Koen Decancq.

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An earlier version of the paper, entitled “Beyond GDP: measuring social progress in Europe”, appeared as Euroforum KU Leuven discussion paper (no. 4).

Appendix 1: Uprating Procedure for Total Household Incomes

Appendix 1: Uprating Procedure for Total Household Incomes

The income information in ESS is based on the following question “please tell me which letter describes your household’s total income, after tax and compulsory deductions, from all sources? If you don’t know the exact figure, please give an estimate.” To answer the question, the respondents make use of a country and wave specific showcard that contains 10 decile values estimated on an alternative data source (often SILC or administrative data). We have taken several steps to construct incomes based on these reported letters.

In the first step, every letter is converted to its corresponding monetary value. For the first nine deciles, we selected the midpoint of each decile, assuming an approximately uniform distribution within each decile. Things are more intricate for the top decile, as that is defined as “y or up”, were y denotes the decile value of the 10th decile. We select the monetary value corresponding to the top decile by searching on a fine grid for the monetary value that leads to the equivalized income distribution with the Gini coefficient which is closest to the Gini coefficient of SILC in 2008.Footnote 28 For most countries we could select a monetary value for the top decile such that the Gini corresponds very well to the external source. Yet, for Czech Republic, Greece and Norway in 2008 the income distribution used in this analysis underestimates the inequality, whereas for Slovenia inequality is too high. In 2010, the figures for Denmark, France, the Netherlands and Slovenia are based on underestimations of inequality and those for Norway are too large. Yet, the discrepancies between the Gini coefficient used here and the Gini coefficient from SILC overall remain reasonable.

In the second step, the obtained income distribution is uprated, such that the average corresponds to the “Real net national income at the price levels and PPPs of 2005” as provided by the OECD.Footnote 29 This uprating corrects for missing income components and price differences across the different countries and waves. Note that this uprating leads by construction to a perfect correspondence between the average income and the macro-figures and that it does not affect the (relative) Gini coefficient. Moreover, as the specification of the happiness equation (Expression 4) includes income after a logarithmic transformation in an estimation with time and country dummies, the coefficient of income is not affected by the uprating.

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Decancq, K., Schokkaert, E. Beyond GDP: Using Equivalent Incomes to Measure Well-Being in Europe. Soc Indic Res 126, 21–55 (2016). https://doi.org/10.1007/s11205-015-0885-x

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