Data, Method, and Operationalization

To test the hypotheses, I rely on individual-level survey data collected in 2008 by the European Values Study in the 27 EU member states. The survey sample has 40,965 respondents, with a median country response rate around 1,500. The primary territorial level of interest is the NUTS 1, 2 or 3 regional level identified earlier in second section. There are 325 regions represented in the analysis. The minimum number of respondents per regional

cluster is 1, while the maximum is 865. The average number of respondents across 325 regions is 64.

The data used in this analysis are hierarchical in nature, consisting of multiple nested units. As a result, a multilevel model is used to estimate support for integration at both the regional level and at the individual level. Proponents of multilevel models argue that classical regression analysis treats differences between and within these nested clusters as a nuisance, rather than important information (Rabe-Hesketh and Skrondal 2005; Steenbergen and Jones 2002). Multilevel models, on the other hand, attempt to explain variation on the dependent variable by including information from all relevant levels in one model, without assuming a single level of analysis (Steenbergen and Jones 2002). The crux of my argument is that support for integration is likely to vary both across regions and individuals. The data are collected at the individual level, but these individuals reside within regions with a distinctive context that may (or may not) affect individual-level attributes differently.

Support for or opposition to integration can be operationalized using a number of different measures (see Brinegar and Jolly 2004). The EVS survey does not include a question that explicitly asks about integration. Instead, I construct an EU Support Scale that combines a respondent’s answers to seven EU-related questions. The first five questions ask respondents how much they personally fear the building of the EU because of 1) Loss of social security; 2) Loss of national identity and culture; 3) Our country paying more and more into the EU; 4) A loss of power in the world for our country; 5) The loss of jobs in the country. The response option range from 1 = very afraid to 10 = not afraid at all. For each respondent, I take the average of these 5 items, and construct a EU Fear Scale, which forms one of the three components of the EU Support Scale. The second component is a question

that asks respondents how much confidence they have in the EU, with 1 = none at all and 4 = a great deal. The third component is a question that asks respondents whether they feel that enlargement has gone too far (=1) or whether it should go further (=10). Because the three component questions are on two different scales, I standardize each variable to have a mean of 0, and take the average of the three variables to form the EU Support Scale. The scale ranges from a minimum value of -1.58 to a maximum value of 1.93.27

My key independent variable is the individual-level territorial identity configurations

measure described above. The measure is a series of eight categorical variables. Because I am primarily concerned with the difference between exclusive and inclusive regionalists, I use the lower-level identitarians as the baseline category. To assess the impact of regional distinctiveness, I include a dummy variable for minority nations and for distinctive regions. Economic distinctiveness is operationalized via the measure outlined in the second section. Additionally, I include an interaction effect between minority nations and economic distinctiveness. The logic behind this is support for integration should be doubly strong for minority nationalists for wealthier regions, as they profit both politically and economically from European integration.28

27 _{To assess how well these seven survey questions form a single scale measuring the same concept, I look at} their Cronbach’s Alpha coefficient. The coefficient is .8272, indicating that the questions do a generally good job of capturing one concept. Furthermore, I conduct a factor analysis to check whether the three EU Support Scale components were capturing one latent concept. All three components were found to load on only one factor with an eigen value of 1.56015. The individual rotated factor loadings were .7231 for fear of the EU, .6995 for confidence in the EU, and .7403 for preference for further enlargement.

28 _{In the preliminary analysis I also explored the possibility of including an interaction effect for exclusive} regionalists living in minority nations, with the expectation that residing in a minority nation might temper the anti-European effect generated by parochialism. Surprisingly, exclusive regionalists in minority nations averaged -0.145 on the EU support scale, while exclusive regionalists elsewhere averaged -0.112, However, this difference in means was not found to be statistically significant, and the interaction effect was subsequently dropped from the analysis.

My model also includes a number of control variables. An alternative explanation, proposed by Anderson (1998), is that individuals use their feelings about their own governments to form opinions about integration. Most citizens have only poor working knowledge of the EU and its institutions. Instead, they are likely to use information about something they know well (their own governments) to formulate opinions about something they know less well (integration). Previous research has shown that respondents who feel satisfied with the their country and their country’s democracy are more likely to support the EU institutions and their country’s participation in them (Anderson 1998; Gabel 1998). To measure the “proxy” hypothesis, I include a measure for how much confidence a respondent feels in their government (proxy argument).

Utilitarian accounts of public opinion have found that individuals who are more likely to benefit economically from integration are likely to be more supportive of the process (Gabel 1998, McLaren 2006; Fligstein 2008). The literature identifies the traditional winners of integration as better educated, wealthier, and younger (McLaren 2006, Fligstein 2008). Therefore, this analysis controls for levels of education, levels of income, and age. Individuals who have encountered extensive unemployment may be more likely to associate that unemployment with the effects of economic integration, and are expected to support the EU less. Women have also been typically indentified as the “losers” of market liberalization, and are less likely to support integration. Furthermore, the cognitive mobilization hypothesis (see Gabel 1998; Inglehart 1970), posits that individuals who are more interested in politics and more in tune to the news are more familiar with the concept of integration, and are likely to fear it less. Finally, I include a variable for left-right self-placement, as left-wing ideology

has been associated with a rejection of the neo-liberal policies of the EU. Table A in Appendix Three shows the descriptive statistics for the variables in this analysis.