Abstract
The chapter analyses the sources of European identification and examines the impact of the EU’s enlargement to Central and Eastern Europe in 2004/2007 and the financial and economic crisis on feelings of European identity among EU citizens. The empirical analysis employs a cross-sectional design that analyses citizens’ identification with Europe at seven occasions between 2000 and 2012. The analysis relies on hierarchical linear regression models to predict individual identification with Europe from individual- and country-level factors. The statistical analysis shows that individual identification with Europe is influenced mainly by individual-level determinants while country-level characteristics have only limited effects on European identification. Economic aspects and redistributive considerations become more relevant for European identification in the wake of enlargement and the crisis.
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Notes
- 1.
Winter 2000, autumn 2003, autumn 2004, spring 2006, spring 2007, spring 2010, and spring 2012.
- 2.
The Treaty of Accession between the EU and the ten new member states to join the EU in 2004 was signed in Athens, Greece, on April 16th, 2003.
- 3.
Recession is defined here as a quarter-on-quarter contraction of real GDP for at least two consecutive quarters. By this definition, Hungary was in recession in the first and second quarters of 2007, Ireland in the second and third quarters of 2007, and Italy in the third and fourth quarters of 2007. For quarterly growth rates of real GDP and changes over previous quarter see the OECD National Accounts Statistics database (OECD 2014a).
- 4.
After protest marches in Athens and Thessaloniki on 01 May 2010, a nation-wide strike against the proposed austerity measures took place in Greece on 05 May 2010, drawing an estimated 100,000 people in Athens alone. Three people died in violent clashes between protesters and the police on 05 May (BBC News 2010a, b). Mass protests and violent riots erupted again in response to further austerity measures in 2011 and 2012 (BBC News 2011, 2012).
- 5.
Czech Republic, Greece Hungary, Italy, Portugal, Slovenia, Spain (OECD 2014a).
- 6.
There is a vast literature on the analysis of hierarchical data in social and political research, refering to multilevel/hierarchical models under a variety of terms, including multilevel linear models, hierarchical linear models, mixed-effects models, random-effects models, or random-coefficient regression models. The present analysis follows the notation and development of the multilevel model in Raudenbush and Bryk (2002) and Steenbergen and Jones (2002). Multilevel and hierarchical models are referred to interchangeably in the following. Technically, multilevel modelling is the more encompassing term in that the general multilevel framework also allows for non-nested models, i.e. data structures where units at level 1 are nested within level-2 units without a clear ordering or hierarchy between the two levels (cf. the discussion in Gelman and Hill 2006, p. 2).
- 7.
There is an ongoing discussion in the literature regarding the number of level-1 units j and level-2 units J needed to fit a multilevel model (see, e.g. Bryan and Jenkins 2015; Maas and Hox 2005; Paccagnella 2011; Snijders 2005; Stegmueller 2013). Maximum likelihood estimation requires sufficiently large sample sizes at both levels to produce accurate estimates and associated standard errors, raising questions of the acceptable lower limit for the number of level-2 units in particular. Simulation studies show, however, that individual-level estimates remain robust even for small numbers of level-2 units (N < 15) and level-2 estimates tend to biased only to a limited degree for level-2 samples of 15 and more (Maas and Hox 2005; Stegmueller 2013). Based on these findings, it seems justified to estimate multilevel models even for the smaller set of the EU15 member states.
- 8.
The relatively small n for 2006 is due to the split-ballot design of EB 65.2; only half of the sample was given the item used to operationalise the dependent variable European identification.
- 9.
The changes in country sample included in the analysis in different years raises questions with regard to the assessment of changes in the determinants of European identification in response to enlargement. To exclude that observed changes in the determinants are due to systematic differences in European identification between old and new member states, separate models for the EU15 and the CEE member states were estimated for the years 2004 and 2006 as well as the period 2007 to 2012 in the addition to the pooled models for the EU25/EU27. No significant differences appeared between the EU15 and the CEE group in terms of effect size and direction of effects.
- 10.
EB 54.1 (Nov/Dec 2000), EB 60.1. (Nov/Dec 2003), EB 62.0 (Oct/Nov 2004), EB 65.2 (March/May 2006), EB 67.2 (April/May 2007), EB 73.3 (March/April 2010), EB 77.3 (May 2012).
- 11.
Centring the country-level predictors around their respective grand mean eases interpretation of the regression coefficients and helps avoid nonsensical results if a value of 0 is essentially meaningless for the predictor variables. For example, while the variables trade and unemployment theoretically could take a value of zero, no EU member state has zero imports/exports or zero unemployment (see also the descriptive statistics for the independent variables in Table 6.5 in the appendix). By centring the country-level predictors around their grand means, we can interpret regression coefficients as predicted changes in European identification for one-unit changes in the respective independent variable with e.g. trade shares and unemployment rates held at their average values in the data rather than at levels zero, which do not occur empirically. In the case of party messages, in contrast, zero is a meaningful value insofar as we can imagine parties not addressing issues of identity at all in their election manifestos. Centring also eases the interpretation of cross-level interaction terms. Thus, for an interaction term in the form of a product w j x ij , the main effect of X is to be interpreted as the effect of X if W = 0 (while the main effect of W is to be interpreted as the effect of W for cases with X = 0). See also the discussion of centring choices in Raudenbush and Bryk (2002, pp. 31–35).
- 12.
The operating expenditures received by member states exclude administrative expenditures, e.g. for the maintenance of EU institutions. For more information on member states expenditures and payments and the calculation of member states’ operating budgetary balances, see the EU budget 2012 financial report (European Commission 2013, pp. 109–111).
- 13.
EU migrants refer to the number of foreigners coming from countries that were EU member states at the time, i.e. the EU15 in 2000, EU25 in 2004, EU27 in 2010 etc.
- 14.
Immigrants are defined as persons holding citizenship other than that of their country of residence.
- 15.
Eurostat defines the labour force as the total number of people employed and unemployed whereby unemployed refers to all persons aged 15 to 74 (16 to 74 in Spain, Italy, and the UK) who were not employed during the reference week, had actively sought work during the past 4 weeks and were ready to begin working immediately or within two weeks (EUROSTAT 2014b).
- 16.
While national unemployment rates are introduced as an indicator for how hard member states were hit by the economic crisis after 2008, all models include the variable for unemployment in order to control for the influence of the general state of the national economy on citizens’ identification with Europe.
- 17.
Another obvious choice would be to control for member states in Central and Eastern Europe, which joined the EU in 2004/2007. However, CEE membership highly correlates with some of the other country-level predictors, in particular net contributions to the EU budget and Eurozone membership. To avoid multicollinearity among the predictors, no additional control variable is included for CEE member states.
- 18.
Recall that identification with Europe is measured on a 4-point scale ranging from 0 (not at all attached to Europe/the European Union) to 3 (very attached to Europe/the European Union).
- 19.
- 20.
The model notation in the results tables follows the notation developed previously. That is, column M1 reports results for random intercept models with individual-level covariates; column M2 reports results for random intercept models with individual- and country-level covariates, and column M3 reports results for random coefficient models with cross-level interactions. Additional subscripts a, b etc. indicate alternative model specifications to avoid collinearity between predictors. For example, in 2003, column M1a reports results for the random intercept model including political interest as an individual-level predictor while column M1b reports results for the random intercept model including European political interest as an individual-level predictor.
- 21.
Issues of multicollinearity in multilevel modelling and the impact of multicollinearity on parameter estimates and standard errors in multilevel regression analyses are rarely discussed in the literature (for exceptions, see Kreft and de Leeuw 1998; Kubitschek and Hallinan 1999; Shieh and Fouladi 2003). Kubitschek and Hallinan (1999) find the standard errors of parameter estimates to increase with multicollinearity between level 1 predictors; Shieh and Fouladi (2003) examine the effects of varying degrees of correlations between level-1 predictors, finding moderate bias in the standard errors of the parameter estimates as well as the variance-covariance components at level 2 for correlations >0.7. Kreft and de Leeuw (1998) illustrate that multicollinearity complicates the interpretation of multilevel model coefficients, in particular the interpretation of cross-level interactions. To minimise the risk of multicollinearity, I examined correlations among level-1 and level-2 predictors for all years under analysis. Correlations among predictors do not exceed 0.5; in the great majority of cases, correlations are smaller than 0.2.
- 22.
Note that only the results for the years 2000 and 2003 on the one hand and for 2004 and 2006 on the other are directly comparable as the analyses for 2000/2003 and 2004/2006 rely on different country samples (EU15 for 2000/2003 and EU25 for 2004/2006). Separate analyses for the EU15 and the new CEE member states for 2004/2006 show largely similar effects as the analyses for the pooled sample of the EU25.
- 23.
Note that EB 77.3 was in the field in March/April 2010, i.e. during the period when the Eurogroup first negotiated bilateral loans for Greece. Greece officially requested to activate international financial support mechanisms on 23 April 2010; the formal agreement with the IMF was approved on 02 May 2010. The European Council, the Eurogroup, and ECOFIN, together with the IMF, the ECB and the European Commission, had been discussing the Greek financial situation at various meetings in the months leading up to the formal agreement.
- 24.
- 25.
See e.g. Angela Merkel’s defense of the first bailout measures for Greece before the Bundestag on 19 May 2010: ‘Wir helfen Griechenland, weil wir so der Stabilität unserer gemeinsamen Währung insgesamt helfen. Wir schützen das Geld der Bürgerinnen und Bürger unseres Landes—nicht mehr und nicht weniger ist der Auftrag der Bundesregierung genauso wie des Hohen Hauses hier.’ (Deutscher Bundestag 2010, p. 4125; emphasis added).
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Appendix
Appendix
6.1.1 Single-Equation Expressions for Random Coefficient Models with Cross-Level Interactions
Interaction of individual political interest and party messages on European community
Interaction of individual political interest and party messages on national community
Interaction of individual political interest and member states’ status as net recipient of EU funding
Interaction of individual political interest and member states’ share in international trade
Interaction of individual national identification and of party messages on European community
Interaction of individual national identification and salience of party messages on national community
Interaction of individual national identification and status as net recipient of EU funding
Interaction of individual national identification and share of immigrants
Interaction of individual national identification and EU/IMF conditionality
Interaction of individual national identification and euro zone membership
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Bergbauer, S. (2018). Explaining European Identification: The Impact of Enlargement and the Crisis on Feelings of European Identity Among EU Citizens. In: Explaining European Identity Formation. Springer, Cham. https://doi.org/10.1007/978-3-319-67708-8_6
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