Abstract
The aftermath of the Global Financial and Economic Crisis of 2007/2008 turned out as a veritable “stress test” for European welfare states. Aiming to stabilize citizens’ living conditions and mitigate socio-economic hardship, European governments have engaged in active crisis management. Yet, the protective capacities of European welfare states vary, as does individuals’ exposure to crisis-induced social risks. Hence, the crisis has impacted countries and the members of different social classes unequally. Against this backdrop, this paper asks how Europeans’ perceptions of the personal impact of the crisis are associated with their welfare attitudes, focusing on variations between social classes and across nations. Using cross-sectional Eurobarometer survey data from 2010 for 27 European countries, I find that perceived crisis impact is associated with greater support for welfare state responsibility and redistribution. However, this association is not homogeneous but moderated by an individual’s class position as well as national economic conditions and social spending levels. More specifically, on the individual level, perceived crisis impact is associated with more favourable welfare attitudes not only among its traditional supporters—such as the working class or the unemployed—but also among its traditional opponents, notably the self-employed. Furthermore, where social spending is higher, welfare state support is less strongly related to perceived crisis impact, suggesting that more encompassing welfare states mitigate the subjective impacts of the crisis. However, redistribution appears to be slightly more contested between those affected and those not affected by the crisis under better economic conditions. Given the challenges facing welfare states across Europe, it remains to be seen how stable a basis of popular support can be formed on these grounds.
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Source: Own depiction, solid arrows denote hypothesized relationships; dashed arrows denote relations on which no specific hypothesis are formulated
Source: Eurobarometer 2010 (74.1), r = 0.63, p < 0.001, N = 27
Source: Eurobarometer 2010 (74.1), r = 0.40, p < 0.05, N = 27
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Notes
Other factors, such as insecurity about one’s own standard-of-living or empathy with crisis victims, have been recently suggested as mechanisms behind welfare state support in hard times (Blekesaune 2013; Owens and Pedulla 2014). So far, however, few studies have been able to empirically separate the influence of these factors.
Along these lines, European countries’ labour market and social policy responses to the crisis have varied not only according to the severity of the downturn, but partly also, as some have argued, in accordance with their overarching approaches to social protection and welfare regimes (Chung and Thewissen 2011; Starke et al. 2013; Van Hooren et al. 2014). While such claims are not uncontested (Hörisch and Weishaupt 2012), short-time working schemes have been most notably devised in the coordinated market economies of Scandinavia and Continental Europe, but they have also been introduced in some Central and Eastern European countries (Leschke and Jepsen 2012: 296).
A slightly different version of the item on the personal impact of the crisis was fielded in an earlier Eurobarometer survey in 2009 (EB 71.1), but again, no items on the welfare state were included at that time.
Alternatively, I also recalculated the multivariate analyses with the “it depends” category coded as missing. While the results did not differ substantially from the analyses presented in the empirical section, such a coding would result in a loss of over 2500 cases. Thus, I decided to keep to the coding as described above. Furthermore, additional analyses using the original categorical coding with multinomial logit models (with country-level fixed effects) also provided similar results.
Again, I conducted a robustness check using the original coding in an ordered logistic multilevel model, which produced essentially similar findings.
Instead of averaging over the years 2007–2009, I alternatively calculated changes in the macro-level measures between 2007 and 2009 (as well as between 2008 and 2010) and included these changes in the multivariate models together with the levels in 2007 (or 2008, respectively) as base value. However, the only statistically significant effect in this alternative operationalization could be found for the level of unemployment in 2007. Therefore, I decided to use averages over the years 2007–2009 as macro-variables (alternative analyses are available upon request).
All analyses are conducted with Stata 12 by using the xtmelogit command to estimate the multilevel logistic regressions based on maximum likelihood estimation (for details see Rabe-Hesketh and Skrondal 2008: 248, 258–261).
Formally, the intraclass correlation \(\rho_{I}\) is defined as the proportion of group-level variance compared with the total variance: \(\rho_{I} = \frac{{\tau^{2} }}{{\tau^{2 } + \sigma^{2} }}\) whereby τ 2 refers to the population variance between groups (in this case, countries) while σ2 refers to the population variance within groups (in this case, individuals), which is fixed to σ2 = π2/3 (= 3.29) in the case of random intercept models (Guo and Zhao 2000: 454). Between- and within-group variance together constitute the total variance (Snijders and Bosker 1999: 17). Hox (2010: 244) suggests that in general, intraclass correlations of 0.05, 0.10, and 0.15 can be considered as small, medium, and large, respectively.
Including the individual and contextual variables reduces the unexplained macro-level variance by 42% in Model M2a and by 46% in Model M3a. For redistributive preferences, including the individual and contextual variables reduces the unexplained macro-level variance by 30% in all models (M1b, M2b, and M3b).
Examining the extent of self-employment across countries by using the Eurobarometer data, comparatively high shares of self-employed respondents can be found in some Southern European countries (Greece, Italy, Portugal, and Cyprus), while low shares of self-employed respondents exist in the Baltic states (Estonia, Latvia, Lithuania) and the Scandinavian countries (Sweden, Denmark, Finland), with the other nations lying in between. When examining the educational background of the self-employed, the highest shares of self-employed with tertiary education can be found in the Nordic countries (between 58% and 85% of the self-employed), whereas the lowest shares of self-employed respondents with tertiary education (between 7% and 17% of the self-employed) are observed in Continental European countries (Germany and Austria) and in parts of Southern Europe (Italy, Portugal, Malta, Cyprus) as well as in parts of Eastern Europe (Slovakia, Romania, the Czech Republic).
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Acknowledgements
Previous versions of this article have been presented at the 2014 conference of the German Sociological Association in Trier, the 2015 SASE conference “Inequality in the 21st century” in London, and at the Centre for the Study of Europe at Boston University in March 2016. I thank the participants on these occasions as well as Debora Eicher, Sebastian Koos, Evelyn Sthamer and the anonymous reviewers for helpful comments and criticism. Furthermore, I am indebted to Reinhard Schunck for methodological advice.
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Sachweh, P. Conditional Solidarity: Social Class, Experiences of the Economic Crisis, and Welfare Attitudes in Europe. Soc Indic Res 139, 47–76 (2018). https://doi.org/10.1007/s11205-017-1705-2
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DOI: https://doi.org/10.1007/s11205-017-1705-2