Socio-economic insecurity perceptions and their societal determinants: Europe in the aftermath of the Great Recession

ABSTRACT

The 2008/09 economic crisis was – unsurprisingly – paralleled by noteworthy perceptions of socio-economic insecurity among Europeans. Respondents in different countries reported more or less grave fears of employment and income insecurity depending on their respective countries’ performance during the crisis. Against this background, we are interested in the manifestations of perceived socio-economic insecurity and their macro contextual determinants. After formulating hypotheses regarding socio-economic and welfare-institutional factors, we test them by using two rounds of the European Social Survey (fielded in 2008/09 and 2016/17). Based on three-level regressions with between-within models of 23,000 individuals (11,611 individuals in 2008/09 and 11,389 individuals in 2016/17) nested in 34 country-years and 17 European countries, we find that the level of perceived socio-economic insecurity in 2016/17 was generally lower than in 2008/09. Additionally, we reveal differences in the effects of the socio-economic factors and welfare-institutional factors between and within countries. These findings suggest that the inconsistency of the results of contextual factors among previous studies might stem from jumbling between- and within-country effects.

KEYWORDS:

• Comparison between and within countries
• economic crisis
• perceived socio-economic insecurity
• socio-economic inequality
• three-level analysis
• welfare institutions

Acknowledgements

The authors are grateful to the participants at the annual conference of the ‘social policy’ section of the German Sociological Association (GSA) in Cologne, 26–27 April 2018, and at the 4th International ESS Conference (‘Turbulent times in Europe: Instability, insecurity and inequality’) in Mannheim, 15–17 April 2019, for their valuable comments on the presentations. Moreover, they thank Michalis Lianos and the two anonymous reviewers for their insightful suggestions and comments. All remaining deficiencies and errors are the authors’ alone.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 Precisely, some previous studies have attempted to examine both the levels of some country characteristics and the changes in these variables within countries in the analysis of the between-country effects that adopts cross-sectional data with one wave (Anderson and Pontusson 2007; Chung and Van Oorschot 2011; Lübke and Erlinghagen 2014).

2 In some countries (i.e. Germany, the Netherlands, Norway, Spain and Switzerland), the field work for the fourth round of the ESS began a few weeks before 15 September 2008, the day the banking crisis commenced (Meuleman et al. 2020). Therefore, it may not be appropriate to treat the ESS 2008/09 as a survey that fully captures the damages of economic crises.

3 It can be assumed that the COVID-19 pandemic will also be reflected in people’s perceived socio-economic insecurity. However, because our latest data are from 2016/17, the crisis we refer to is the recent economic crisis (2008/09).

4 ESS4 edition 4.5 was released on 1 December 2018, and ESS8 edition 2.1 was released on 1 December 2018. The data for all rounds of the ESS, detailed information and documentation are made available for download for free on the ESS website (ESS-ERIC 2018).

5 According to the OECD, this 30-hour threshold serves as a common demarcation line for differentiating between part-time and full-time employment (Van Bastelaer et al. 1997: 6).

6 Cronbach’s alpha is 0.668 in the pooled dataset, meaning that the satisfactory value of this measure for internal consistency (generally > 0.7) was not quite met. However, these two items are significantly correlated – r = 0.502 (p = 0.000) and rho = 0.517 (p = 0.000) – which encourages us to use this index and perform linear multilevel regression analysis.

7 To compute the EGP class affiliation, we refer to the Trento version, which is available as SPSS syntax in Leiulfsrud et al. (2010).

8 Precisely, this is called ‘square root scale’ by Förster and D’Ercole (2009: 7). The missing values for equivalized household net income are imputed by using the multiple imputation method (2,618 cases).

9 A replication package for all analyses in this study is available online (Akaeda and Schöneck 2022).

10 We also created bivariate scatter plots to provide insights into each country’s position regarding the relation of socio-economic and welfare-institutional factors on the one hand and country-specific means of perceived socio-economic insecurity on the other hand (see Figures A1 to A6 in the Online Appendix, where an overview of the bivariate correlations of our six indicators can also be found in Tables A2a and A2b).

11 To clearly show the three segments (‘lowest: lower than mean – SD; middle: between mean ± SD; highest: higher than mean + SD’) in each ESS round and countries’ trajectories, we present the results for 2008/09 and 2016/17 in two separate graphs. However, to visualize changes in each country, a combined graph is provided in Figure A7 in the Online Appendix.

12 According to Hox et al. (2017: 21), the ICC at the country level is ${\displaystyle \frac{{\sigma }_{u2}^2}{{\sigma }_e^2+{\sigma }_{u1}^2+{\sigma }_{u2}^2}}$, and the ICC at the country-year level is ${\displaystyle \frac{{\sigma }_{u1}^2}{{\sigma }_e^2+{\sigma }_{u1}^2+{\sigma }_{u2}^2}}$.

13 In accordance with Snijders and Bosker (2012: 113), we calculate the explained variance R² as follows: the total variance in Model 0 is 0.237 + 0.094 + 1.678 = 2.009, and the total unexplained variance in Model I is 0.131 + 0.084 + 1.450 = 1.665. Thus, the explained variance R² is 1 – (1.665/2.009) = 0.171.

14 For a robustness check, we conducted an additional analysis of each of the two rounds of the ESS. Table A4 in the Online Appendix reports similar results to Table 2 regarding the between-effects of the macro variables on perceived socio-economic insecurity.

15 On this issue, Otto (2018) reported that the unemployment cash benefit expenditure is positively associated with access rate and the cash benefit right generosity with unemployment cash benefit.

Additional information

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Notes on contributors

Naoki Akaeda

Naoki Akaeda is an Associate Professor in the Faculty of Sociology at Kansai University in Japan. He conducts research on how social capital – including social trust, family, relatives, neighbours and friends – and well-being vary depending on the characteristics of countries through international comparative analyses. His recent work includes ‘Social Contact with Family and Relatives and Happiness: Does the Association Vary with Defamilialization?’ in the European Sociological Review.

Nadine M. Schöneck

Nadine M. Schöneck is a Professor of Sociology and Empirical Social Research at Niederrhein University of Applied Sciences in Germany. Her current research focuses on cross-national comparisons and diagnoses of contemporary (advanced modern) societies in relation to social inequality and welfare states, the sociology of time and work-life balance and job-related spatial mobility. Recently, she (with Florian R. Hertel) published the paper, ‘Conflict perceptions across 27 OECD countries: The roles of socio-economic inequality and collective stratification beliefs,’ in Acta Sociologica.