Abstract
This paper aims at establishing a clear link between different types of inequality and life satisfaction in Europe. We analyse the relationship between life satisfaction and both income inequality and inequality of opportunity using seven waves of the European Social Survey. The results show that in Europe both income inequality and inequality of opportunity reduce people’s life satisfaction. Our main results suggest that all socio-economic groups are dissatisfied with income inequality, whereas primarily low socio-economic individuals worry about inequality of opportunity. We find that expected mobility is very important in explaining the link between inequality and life satisfaction for all socio-economic groups in Europe. We advance the hypothesis that life satisfaction is conditioned by a mix of normative arguments against inequality and by the fear/possibility to lose/gain a good social position. This result complements findings on the mediating role of social mobility in the relationship with subjective well-being.
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Notes
SWB is a generic term used in the introduction to indicate indistinctly life satisfaction, happiness and quality of life. Our empirical analysis focuses on life satisfaction.
The literature on income inequality proposes both simpler and more complex indices than the Gini (Salverda et al 2009). If the research interest is more on the extremes of the distribution than on the centre, then percentile ratios (e.g. the Palma ratio) can be quite useful and simple to compute. Given that in many European countries income inequality has mainly increased because of polarisation at the extremes of the income distribution (Atkinson and Piketty 2007), an income quintile share ratio (the p80/p20), has recently become more frequently proposed as the official measure of inequality both by the OECD and by Eurostat.
The probability to move up or down in the social ladder is always present in our societies even if countries might be at a different level.
For a comprehensive philosophical discussion about this distinction, see Roemer (1998).
Other studies prefer to see inequality of opportunity as a broader concept that link income inequality with intergenerational mobility (Corak 2013). What we are interested in this study is rather intragenerational mobility and the probability to improve social status within the course of one generation.
Perceptions of inequality and of social mobility might be different than observed measures. Research on the topic showed that perceptions of inequality are often not so detached from the actual level of inequality (Oishi et al 2011; Schneider 2012). This is often due to perceived unfairness and lack of trust. The effects change according to the population group. Perceptions of social mobility are usually quite detached from reality (Kraus and Tan 2015; Bjørnskov et al 2013). In this paper, we analyse only the effect of objective measures of inequality on SWB. We do so because we want to establish a causal link between inequality and SWB. Further research on how actual inequality is reflected into perceptions of inequality and on how social mobility is perceived by different social groups in Europe would be extremely beneficial for this study.
For simplicity we refer to poor and rich individuals even if to be more precise we should call them respectively individuals with low socio-economic status and individuals with high socio-economic status.
Another possible question we could have used is about happiness (Taking all things together, how happy would you say you are?). This variable measures emotional reactions associated with SWB. We tested the effects of inequality on a composite index of the two components, life satisfaction and happiness, and we found that the effects were an average of the two components. We have therefore decided to show only the results on life satisfaction because this component is linked with a more cognitive judgement of the situation.
Kolenikov and Angeles (2009) compare different estimators and find that the tetrachoric version might provide somewhat better results when estimating socio-economic status.
During the elaboration of this study, we ran a large series of different factor analyses by changing both the indicator variables and the estimators. The study results are very robust to these changes, suggesting that the exact way of estimating the Socio-economic status variable is of less importance. We also analysed the number of factors to be retained for each country and year combination and we always found a single factor that had a substantially higher eigenvalue (above 1) than the others (below 1).
Ferreira and Gignoux (2011) discuss some properties of the different inequality measures and finally use the mean logarithmic deviation applied on a measure of income and in Ferreira and Gignoux (2014) the same authors propose to use the variance for variables without inherent scale. This latter approach is the one we also employ in this study.
We use the Breusch-Pagan test to test for the presence of country-level effects and find in all models not accounting for country-specific effects would yields biased results. We therefore always estimate fixed-effects models.
We use the Nadaraya-Watson estimator, which computes the kernel weighted local average.
Note that while both indicators are defined on a support of zero to one, the numerical values are not directly comparable.
As a robustness check on the inequality measure, we performed also a model with the interquintile ratio p80/p20 instead of the Gini. The results displayed in the online appendix are very similar to those obtained using the Gini index.
Note that the sample size differs from the income inequality regressions to the inequality of opportunity regressions. We choose not to harmonise the data as we would lose another ten to twenty observations and we consider this loss to be more problematic than the slightly reduced comparability across regressions due to different sample sizes.
When excluding the country- and year fixed effects, the coefficient is significant (see Table 9 in the online appendix)
We also tried to perform this analysis for different regions in Europe (e.g. Eastern Europe, Northern Europe, Southern Europe and Continental Europe). However, the resulting sample sizes were too small and as a consequence the estimates became extremely unstable to small changes in the sample definitions. We therefore limit our analysis in this article to the full sample of countries.
Running Model (1) with the same subset of countries as in Model (3) gives exactly the same coefficient for the Gini index found in Model (1). This means that the significance level of the Gini index is not affected by the collinearity with the IOP measure, but by the number of countries.
The number of countries in this regression is 124 instead of 122 because we can include those countries with only one observation, namely Luxembourg and Romania.
The same results were produced using a multi-level instead of our fixed effect model. Here we weight each country to resemble the macro regressions presented before.
These results hold true if we take income quartiles instead of quartiles of socio-economic status. The problems of income quartile are, however, that the variable is not continuous, but categorical, and that its classes changed after 2006 declining from 12 to 10. Starting from 2008 it is therefore impossible to talk about proper income quartiles, as it is impossible to talk about proper income deciles in the previous years.
We also performed a test including a direct question about normative arguments against inequality. The results do not change with the inclusion of this additional variable. More details can be found in the online appendix.
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Ravazzini, L., Chávez-Juárez, F. Which Inequality Makes People Dissatisfied with Their Lives? Evidence of the Link Between Life Satisfaction and Inequalities. Soc Indic Res 137, 1119–1143 (2018). https://doi.org/10.1007/s11205-017-1623-3
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DOI: https://doi.org/10.1007/s11205-017-1623-3