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Published in: Journal of Happiness Studies 4/2019

17-04-2018 | Research Paper

Distributional Dynamics of Life Satisfaction in Europe

Authors: Vanesa Jorda, Borja López-Noval, José María Sarabia

Published in: Journal of Happiness Studies | Issue 4/2019

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Abstract

The promotion of subjective well-being is becoming a central goal of social and public policy. In this regard, it is sometimes argued that subjective well-being inequality is an informative indicator of social tensions. In this paper, we investigate the evolution of the life satisfaction distribution in Europe since 1973 using data from the Eurobarometer surveys. In order to respect the ordinal nature of subjective well-being and to avoid the need to impose an arbitrary scale, we use the Abul Naga and Yalcin index. We demonstrate that this index can be characterised as a measure of both inequality and polarisation, depending on the value of the parameters. We find that, at the European level, life satisfaction inequality was significantly higher in 2014 than in 1995. This result is mainly explained by the increase in inequality in the Mediterranean countries and Ireland in recent years, but especially since the Great Recession. Although polarisation and inequality present a similar trend at the European level, some differing patterns are observed for particular countries, thus suggesting that these two phenomena are not only conceptually different, but also complementary in the analysis of the distribution of subjective well-being.

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Appendix
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Footnotes
1
Within this class, we find the interquartile range, which is scale dependent, but since it is based on the ranking of individuals, it is order preserving. The main problem of this measure is that it ignores a large amount of information contained between the two quartiles.
 
2
See Cowell and Flachaire (2012) and Silber and Yalonetzky (2011) for alternative inequality measures for ordinal variables.
 
3
The concept of bipolarisation is associated with the concentration of the population in two poles, whereas polarisation considers a clustering of the population around an arbitrary number of poles. Socio-economic polarisation (Zhang and Kanbur 2001) evaluates the differences between socially predefined groups by ethnicity, religion, etc., and the within-country variation of these groups. A decrease of the distance between the different subgroups would reduce socio-economic polarisation because it narrows the gap between the different poles. On the contrary, as the within-group variation declines, the groups become more homogeneous and, hence, socio-economic polarisation increases. Although these concepts are closely related, the underlying phenomena that they measure may present contrasting evolutions (see Duclos and Taptué 2015).
 
4
The absolute index would be the only case that holds the property of symmetry, i.e. the same importance is given to the distribution above and below the median. However, as this index is insensitive to IB movements, hence it might not be used to evaluate either inequality or polarisation.
 
5
Actually, since 2012 the survey distinguishes among individuals who do not answer the question and those that answer “I do not know”, but given that only very few people choose this response category it does not seem a problem to simply omit them.
 
6
With the exception of Luxembourg that usually collects data of 300 individuals.
 
7
The values of the α parameter have been chosen to cover the whole range of this parameter (0, 1). The β parameter has been set to infinity, to present the evolution of the limiting measure; to 4 following Abul Naga and Yalcin (2008); and to 1.5 to analyse the evolution of inequality when more weight is given to disparities above the median. The results for the limiting case when α tends to 0 and β tends to infinity have not been presented because it would correspond to a measure which neglects both inequality above and below the median.
 
8
The median category in Europe is the third one during the period 1995–2014. The life satisfaction variable used in this study involves four categories, hence the distribution below the median includes the first two, not at all satisfied and not very satisfied, and above the median it also comprises two categories, fairly satisfied and very satisfied.
 
9
In Greece, the modal category changes from “fairly satisfied” to “not very satisfied” from 2011 onwards. Therefore, we only present estimates on inequality and polarisation until 2011 because the ANY measure is not comparable across distributions with different modal categories.
 
10
For the sake of clarity we only present few combinations of the parameter values. In “Appendix 2”, we present the evolution of inequality and polarisation for other parameter values, which reveal parallel patterns to those presented in this section.
 
11
Using the Cantril scale is the simplest, although widely used, approach to transform ordinal variables into cardinal ones. See Van Doorslaer and Jones (2003) for a review on the different techniques adopted in the literature to define a scale for categorical variables.
 
12
The most equal distribution has all the probability mass in one category: \(\widehat{F} = [0, 0, \ldots , 1, \ldots ,1],\)\(\widehat{f} = [0, 0, \ldots ,1, 0, \ldots ,0]\). Note that \(\widehat{F}\) is not unique. The probability mass can cluster around any category, so there are J different most equal distributions. The most unequal distribution concentrates the probability mass around the two ends: \(\widetilde{F} = [0.5, 0.5, \ldots ,1], \widehat{f} = [0.5, 0, \ldots ,0.5]\).
 
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Metadata
Title
Distributional Dynamics of Life Satisfaction in Europe
Authors
Vanesa Jorda
Borja López-Noval
José María Sarabia
Publication date
17-04-2018
Publisher
Springer Netherlands
Published in
Journal of Happiness Studies / Issue 4/2019
Print ISSN: 1389-4978
Electronic ISSN: 1573-7780
DOI
https://doi.org/10.1007/s10902-018-9985-z

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