Abstract
In this article, we analyse the relation between different economic inequality indicators and social cohesion. Previous research usually narrows down economic inequality to income inequality, or distinguishes several types of economic inequality. Little attention has until now been given to how different aspects of economic inequality might be related to each other and can have an effect on social cohesion. This article analyses several indicators of economic inequality and makes a distinction between indicators measuring income inequality, poverty, economic strain and unequal distributions of wealth. Arguing that these indicators represent different aspects of inequality, we hypothesise that they cannot be reduced to one latent concept of inequality and have specific relations with social cohesion. In order to test this hypothesis, we conducted an exploratory factor analysis. This resulted in two different factors: one associated with economic hardship, and one associated with imbalances in market outcomes. This would imply that inequality indicators can be classified into two underlying concepts. Secondly, we related the factor scores of the two latent concepts to the social cohesion indicators via regression analyses. This paper focuses on European countries and uses pooled data from the European Social Survey (period 2006–2012), in combination with macro-level data drawn from the OECD, Eurostat and the World Bank. The results demonstrate that the strength of the link between inequality and citizens’ attitudes depends on the type of inequality indicator we analyse: only the factor economic deprivation can be significantly linked to social cohesion.
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Notes
In this period, four ESS rounds were organised: in 2006, 2008, 2010 and 2012.
Most of the studies discussed in this section use the Gini coefficient of disposable income to measure income inequality.
In contrast to the Gini of disposable income (which is the spread of income after taxes and transfers).
Via the Gini coefficient.
Next to the S80/S20 ration, the S90/S10 ratio is also prevalent in income inequality research (the S90/S10 calculates the ratio of income held by the richest 10% to the income held by the poorest 10%). The S90/S10 focuses more on the extremes of the income distribution, the S80/S20 ratio compromises a broader view of income imbalances and was therefore preferred by the authors. Other ways of measuring within-country income inequality are, amongst others, measuring the absolute range of income, assessing the relative mean deviation, the variance of income etc. (Cowell 2011).
These indicators indeed correlate with each other and their composite ratio (Pearson’s correlation coefficients range from 0.143 to 0.728), but the correlation only becomes problematic in the case of the correlation between material deprivation and the at-risk-of-poverty and social exclusion ratio (ρ 0.951).
There is a debate on whether unemployment rates are a causal factor of economic inequality or an inherent part of it (Atkinson 2015; Stiglitz 2012). Conceptualising it as a measurement of economic strain and an unequal distribution of employment opportunities (and hence, the economic resources that emanate from it), we would like to test whether it is indeed part of (one of the) dimensions of economic inequality.
Currently several scientific initiatives are working on providing more specific information on the concentration of wealth, such as the World Wealth and Income Database (Alvaredo et al. 2016). Piketty (2015) does provide information on some European countries (such as France, Sweden and the United Kingdom), yet this dataset provides too limited information on the different European Social Survey Participants to add this as a variable in the subsequent analyses.
The KMO test value was 0.722.
A similar approach was taken with regards to the Gini coefficient of market income. Replacing the Gini index of Eurostat with the coefficient of the Standardized World Income Inequality Database did not change the factor structure.
We excluded the indicator of unemployment in the calculation of countrywide factor scores of economic inequality. We speculate that the low factor loading points at the fact that unemployment cannot be seen as a measurement of economic inequality an sich, but rather as a cause or consequence of economic inequality, which is in line with previous research.
Age of Democracy is measured via dummy coding. The reference category (0) implies that the country did not have a communist regime in its past.
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Goubin, S. Is Inequality a Latent Construct? An Assessment of Economic Inequality Indicators and Their Relation with Social Cohesion in Europe. Soc Indic Res 136, 21–40 (2018). https://doi.org/10.1007/s11205-016-1522-z
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DOI: https://doi.org/10.1007/s11205-016-1522-z