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ABSTRACT
Euroscepticism has been a persistent part of the European integration process over the last several decades (Usherwood and Startin [2013]. Euroscepticism as a persistent phenomenon. JCMS: Journal of Common Market Studies, 51, 1–16). Scholars have proposed various theories to explain resistance to integration by drawing upon economic, political and cultural theories. In this paper, I ask whether Euroscepticism is a by-product of rural–urban divisions, which influences one’s perception of the economy, one’s trust of transnational politics and one’s cultural identity. However, geographical variables have never been tested in the presence of these more well-established Euroscepticism predictors. Here, I use the European Social Survey to compare the predictive power of economic, political, cultural and geographical variables on two forms of Euroscepticism: trust in European Parliament and the belief that integration has gone too far. Multilevel mixed-effects regression models demonstrate that trust in the European Parliament is indeed higher in big cities, but overall, place of residence is not a strong predictor of Euroscepticism. Among other theories, I find that cultural variables are more meaningful than economics or politics. I finally conclude with a discussion of the EU’s future.
Acknowledgements
I would like to thank my research assistant, Taylor Anhalt, for her keen eye and unique insight as my editor while writing this manuscript. I also thank the anonymous reviewers for their extremely valuable suggestions.
Notes
1 With some notable exceptions, especially Lubbers and Scheepers (Citation2005, Citation2010).
2 At the time of this writing, the United Kingdom has formally begun their exit from the EU but has not actually exited yet.
3 For most countries, this is the NUTS1 region. NUTS2 and NUTS3 are smaller units of analysis, but as one begins to use these as levels for the model, countries begin to drop out. I therefore use the ESS Region to ensure the greatest number of countries is available for analysis.
4 The European Social Survey is only one of several data sources I could have selected for this study. In particular, various Eurobarometer waves ask similar questions. Kołczyńska and Schoene (Citation2018), in a study of the quality of cross-national survey documentation, find that the European Social Survey is of higher quality than other competitors. I therefore use the ESS.
5 Any one of these countries could have served as a reference group. I chose the UK because it is the only EU member to begin the exit process, making them a unique comparison.
6 Of course, people are also nested within countries, but I avoid three level models due to their immense complexity in calculating standard errors and producing coefficients (Raudenbush & Byrk, Citation2002).
7 Additionally, a Hausmann test demonstrates that multilevel models are the best fit for the data structure.
8 Since this is an attitudinal scale, my dependent variables are not strictly continuous. However, by and large, 11-point scales are being treated as continuous for analytical purposes (Leung Citation2011).
9 Both regression models include weights.
10 Who, to be clear, are not to be confused with immigrants. These categories overlap only in part.
11 With weights applied.