Francesco Capuzzi Ph.D. Candidate in Political Studies ......1 Francesco Capuzzi Ph.D. Candidate in...
Transcript of Francesco Capuzzi Ph.D. Candidate in Political Studies ......1 Francesco Capuzzi Ph.D. Candidate in...
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Francesco Capuzzi
Ph.D. Candidate in Political Studies - University of Milan
Understanding popular Euroscepticism
Paper prepared for presentation at the PSA 66th Annual International Conference,
panel “The EU-ro crises and the end of the Good Life? Competing national understandings and
visions of the EU in times of crisis”- March 21-23 (2016)
Abstract
In this article, I study the dimensionality of the concept of EU support, which is expected to be
constituted by two latent dimensions: one that concerns political integration, in terms of European vs
National governance of strategic policy areas, and another one related to the instrumental evaluation
of country’s EU membership based on cost-benefit analysis. To assess the validity of this theory, I
separately analyse cross-national data from the Intune project 2009 and the European Election Study
2014, applying latent class analysis (LCA) as a statistical method to address the research question.
Using the Intune 2009 dataset the theory holds in 13 out of the 15 EU countries included in the study,
whereas, the same analysis for the EES 2014 dataset leads to an inconclusive result. These two
dimensions of EU support are modelled as discrete-ordinal factors, which allow outlining a typology
formed by six types of attitudes. Furthermore, several predictors of the class membership are
separately tested, and supporting evidence is found for the theories on the effect of affective and
identitarian factors, institutional distrust, and cognitive mobilization on the attitudes towards the EU.
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Introduction
There is a general accordance among political scientists and public opinion scholars regarding the
remarkable change in the elite and popular attitudes toward the European integration following the
signing of the Maastricht Treaty (Eichenberg and Dalton 2007), an agreement which moved an
economic union toward a more political one (Garry and Tilley 2009). That moment ended the era of
a so-called permissive consensus (Lindberg and Scheingold 1970) on the European integration
project, opening a new period of constraining dissensus (Hooghe and Marks 2005) on the direction,
the spread and the contents of the process of Europeanisation. This shift meant the re-politicization
of the EU issue, opening a public debate on EU legitimacy and feasibility, the current structure of
multilevel governance, and the fundamental political principles that have driven the unification.
This emerging debate has been the object of several studies in the last twenty years (e.g. Leconte
2010; Mair and Thomassen 2013; Schmitt and Thomassen 1999), which have addressed the causes
and consequences of the constraining dissensus on the EU integration process. Much of the literature
on the European issue is constituted by studies on the public opinion toward the European integration,
which have their roots in the Inglehart’s Silent Revolution (1977). Recently, many scholars have
widely worked on assessing the role of anti-integration stances in driving the political behavior of
voters (e.g. Evans 1998; de Vries 2007, 2010; Hobolt et al. 2009; Lubbers 2008; Tillman 2004; de
Vreese and Boomgaarden 2005), and parties (Taggart 1998; Kopecky and Mudde 2002; Taggart and
Szczerbiak 2004, 2008a, 2008b), while others have studied the voters-party interactive influence on
developing such attitudes toward the EU (Ray 2003a, 2003b; Steenbergen et al. 2007).
This article attempts to make a step back from those researches, shedding a light on the structure
of the EU related attitudes and starting a preliminary analysis of the factors that influence the opinions
regarding the EU. The literature on the citizen support for the EU suggests that there is not only one
form of Euroscepticism or EU support, and two dimensions combine in defining the individual stance
on the EU issue (Lubbers and Scheepers 2005). The first one regards the political evaluation of the
process of EU policy integration; the second one indicates the perceived instrumental benefits of
being an EU member. Therefore, in accord with the reference theory, the leading hypothesis is that
the concept of EU support is multi-dimensional, composed by two distinct dimensions of political
and instrumental support. If empirically confirmed, this bi-dimensional structure allows outlining a
typology that can split – theoretically as well as empirically – the European electorates in different
groups according to their understanding of the EU and its future development.
To sum up, the purpose of this paper is to understand and explain popular Euroscepticism, studying
the dimensionality of the concept itself and assessing the individual level features that might affect
the development of specific sets of opinions regarding the EU. To accomplish these tasks, I separately
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analyse cross-national data from the Intune project 20091 and the European Election Study 20142,
applying latent class analysis (LCA) as a statistical method to validate the theory.
Anticipating the results, the theory is validated by data analysis from the Intune 2009 dataset,
which shows a bi-dimensional structure of EU support to be present in 13 out of the 15 EU countries
included in the study3. Both latent factors that represent these dimensions have been modelled as
discrete and ordinal factors. In details, the factor that refers to the instrumental evaluation of the EU
membership is a two-level factor (Positive vs Negative evaluation), whereas the other one related to
the preferred political arena for policy decisions is a three-level factor, whose levels are “National or
sub-National arena”, “Mixed combination” and “European arena”. Consequently, in these thirteen
countries it is possible to define a typology based on these two latent dimensions of EU support,
which permits creating six clusters of citizens4.
On the contrary, applying the same analysis to the EES 2014 dataset leads to an inconclusive result,
most likely due to a shortage of indicators in this dataset, where only four could be the appropriate
indicators of the latent concepts, while in the Intune dataset they are nine. Therefore, most of the
article is devoted to analyse the Intune 2009 dataset, and the analysis of the EES 2014 data is placed
in the appendix, in order to give the reader the possibility to review it.
Conceptualizing Euroscepticism
The term Euroscepticism is generically referred to the scepticism toward the project of European
integration (Taggart 1998; Kopecky and Mudde 2002; Taggart and Szcerbiak 2004, 2008a, 2008b;
Lubbers and Scheepers 2005; Sanders et al. 2012). This word express doubt or disbelief in Europe
(Hooghe and Marks 2007), a multilayer object that can be conceived as a set of national states, polities
and policies. The most used definition of Euroscepticism is the Taggart’s seminal description of this
attitude as a “contingent or qualified opposition, as well as [… an] unqualified opposition to the
process of European integration” (Taggart, 1998: 366).
In literature, the use of the adjective Eurosceptic is primarily associated with two actors, notably
public opinion and political parties. The seminal work of Taggart (1998) and, later on, Taggart and
Szczerbiak (2004, 2008a, 2008b) aim to disentangle the party-based dimension, and they propose a
distinction between hard and soft Euroscepticism. The last formulation of their conceptualization
1 Cotta, M., Isernia, P. & Bellucci, P. (2009) “IntUne Mass Survey Wave 2, 2009. ICPSR34272-v2”, Ann Arbor, MI:
Inter-university Consortium for Political and Social Research [distributor], 2013-04-22. 2 EES (2014), European Election Study 2014, Voter Study, Advance Release, 1/1/2015,
(http://eeshomepage.net/voter-study-2014/). 3 From the analysis I have excluded in advance Austria, because of a limited sample size (approximately 500 cases),
and Turkey and Serbia due to the fact that they are not EU members. Hence, this study includes citizens from
Belgium, Bulgaria, Denmark, Estonia, France, Germany, Greece, Hungary, Italy, Poland, Portugal, Slovakia,
Slovenia, Spain, and the UK. 4 See figure 1 at page 8.
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(2008a, 2008b) defines the hard type as a principled opposition to the project as such, against the
very idea of transferring powers to any supranational institution. In contrast, the soft type of
Euroscepticism defines a qualified opposition to the actual EU core policies or an aversion towards
the planned trajectory of the further extension of EU competencies. This typology is contested by
Kopecky and Mudde (2002), who criticize its theoretical precision, proposing their own classification
criteria, which resemble David Easton’s distinction between diffuse and specific support for political
regimes (Easton 1965). They identify a typology based on two dimensions: the diffuse support, as an
approval for the general idea of European integration, and the specific support, namely the support
for the EU’s current structure and the planned future evolution of the European integration (Kopecky
and Mudde 2002). The diffuse support separates Europhiles from Europhobes, while the specific one
differentiates between EU-optimists and EU-pessimists. According to their theory, there are four
ideal-type categories of party attitudes regarding the EU (ibidem):
1) the Euroenthusiasts that combine Europhile and EU-optimist positions, supporting both
general ideas and the integration process;
2) the Eurosceptics that merge Europhilism and EU-pessimism approving general ideas but not
their current application;
3) the Eurorejects that are both Europhobe and EU-pessimist;
4) the Europragmatists, which disapprove general ideas but support the current EU integration
anyway, taking pragmatically into account that even if they are ideologically against the
European integration, they benefit from it.
In the literature on parties, one can see an effort to develop a theory that can be both sufficiently
precise to be operationalised and become useful for empirical studies, and, at the same time,
adequately valid for different party systems. Although in this article I study the citizens’ level, such
typologies, if properly refined, may be employed also for the analysis of individuals. Most of the
individual level empirical studies on this topic define Euroscepticism as merely the overall judgment
on the EU, measured as a self-placement on the anti-pro integration scale (e.g. Evans 1998; de Vries
2007; Hobolt et al. 2009, Tillman 2004). Although this operationalization is quite useful and flexible,
I think it may fail in taking into account in a substantial way popular Eurosceptic or pro-EU stances.
My hypothesis is that, similarly to parties, at the level of individuals there may be not just one form
of Euroscepticism, nor one single dimension underlying the support or rejection of the EU. In line
with this hypothesis, Lubbers and Scheepers (2005) propose to split the concept in two dimensions,
labelled instrumental and political Euroscepticism. These scholars define the latter as an opposition
to transferring policy competencies to the supranational level, whereas the former is an opposition to
a country’s membership in the EU based on a cost-benefit calculus. They report (ibidem: 224) that
instrumental Euroscepticism is mainly measured through the questions ‘Did your country benefit
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from membership of the EU?’, and it is a conceptualization of Euroscepticism that derives from the
cost-benefit approach (e.g. Eichenberg and Dalton 1993; Anderson 1998; Gabel and Whitten 1997;
Gabel 1998a, 1998b). On the contrary, the political Euroscepticism indicates a dimension related to
the distribution of legislative powers among the EU multilevel governance (Lubbers and Scheepers
2005), which links this dimension to the debate on the national sovereignty and the political
legitimacy of European institutions to legislate in strategic policy areas. In their study, they find
evidence supporting their theory, and the aim of the present article is to further validate it, using more
recent data and applying a different statistical technique.
Method and Data
To test the dimensionality of the EU support I use Latent Class Analysis (LCA), more precisely
Latent Class Cluster analysis (LCCA) and Latent Class Factor analysis (LCFA) (Hagenaars and
McCutcheon 2002). The leading hypothesis is that some items included in the dataset are the manifest
part of two unobserved concepts, defined as instrumental and political EU support. These items are
categorical variables considered as the indicators of two latent categorical variables, which represent
the above-cited dimensions. Hence, the observed association among the indicators of each concept is
expected to be spurious, since those items are affected by the same latent variable. In this application,
latent class analysis is conceived as a measurement model, where a latent variable is an antecedent
variable that determines the indicators.
As remarked by Madigson and Vermunt (2004) latent class (LC) modeling was introduced by
Lazarsfeld and Henry (1968) “as a way of formulating latent attitudinal variables from dichotomous
survey items” (Magidson and Vermunt 2004: 3). A few years later, Goodman (1974a, 1974b)
extended LC modeling to nominal variables, while other scholars concluded the work proposing LC
analysis for ordinals (Clogg 1988; Uebersax 1993; Heinen 1996), Poisson counts (Wedel et al. 1993),
continuous variables (Wolfe 1970) and for mixed-mode data (Everitt 1988; Lawrence and
Krzanowski 1996), in which indicators are of different scale types.
Therefore, it is potentially possible to include any kind of variable in the LC model, but for the
aim of this article, I just use nominal and ordinal variables as indicators of the reference concepts.
The purpose of this work is twofold. On the one hand, I empirically test the possibility to separate
instrumental and political EU support, at the same time, I endeavour to classify citizens according to
their attitudes to the EU, comparing cross-country differences. Hence, the first step is to develop a
measurement model that can function in a second phase as a classification model. After having
classified survey respondents into latent groups, it is possible to start a preliminary analysis of the
exogenous variables that may serve as predictors of the levels of the latent factors.
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There are important differences between the traditional techniques used to perform these tasks,
and the one that I apply here. Most of the studies on the consistency of a hypothesized measurement
model employ Confirmatory Factor Analysis (CFA), in which both factors and indicators are formally
required to be continuous, and the assumption of multivariate normality is needed to justify linear
modelling (Magidson and Vermunt 2003). On the contrary, in this work, the latent factors are
supposed to be categorical, where the categories of these latent dimensions correspond to different
types of attitudes towards the EU, and the responses at the item level are just the product of these
antecedent dispositions. In addition, Magidson and Vermunt (2001) reported that Latent Class Factor
Analysis (LCFA) makes solutions “uniquely identified and interpretable without the need for a
rotation” (ibidem: 237), which is often required in traditional factor analysis.
LCFA is also applied to achieve the second purpose of the research, namely to classify survey
respondents into latent groups and to analyse the exogenous membership predictors. Traditional
clustering techniques (e.g. K-Means) lack in rigorous statistical tests to choice the cluster criterion,
and, like CFA, they formally permit the inclusion of only quantitative variables. Conversely, as stated
above, LC analysis does not pose limit to the variables selection, and it does provide statistical test to
evaluate the model fit (e.g. information criteria like BIC and AIC). Furthermore, it produces estimates
for misclassification rates, because classification into clusters is based on posterior membership
probabilities estimated by maximum likelihood (ML) methods (Magidson and Vermunt 2002). For
what concerns the inclusion of exogenous variables to describe differences among the created groups,
those predictors may be included in the model, allowing “both classification and cluster description
to be performed simultaneously using a single uniform ML estimation algorithm” (ibidem: 77).
In conclusion, latent class factor modelling seems to be the most appropriate statistical tool to deal
with the theoretical framework introduced above, and for achieving the presented purposes. I apply
this analysis using two cross-national datasets, the Intune project 2009 and the EES 2014. The
analysis of the Intune dataset is included in the following text, whereas the one pertaining to the EES
data is presented in the appendix.
Measurement and classification model
In the Intune 2009 dataset, we have nine categorical variables that are used as the indicators
(endogenous variables) of the latent factors. Six of them define the political EU support, since they
refer to the preferred level of governance (Regional, National or European) for strategic policy areas,
namely fighting unemployment, immigration, environment, fighting crime, health care, and
agriculture policies:
Q26a. In most European countries today, political decisions are made at three different levels of
government: at the regional level, at the national level, and at the level of the European Union. In
your opinion who should be responsible for each of the following policy areas:
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Fighting unemployment (REGIONAL, NATIONAL OR EU LEVEL – ONE ANSWER) Immigration policy (REGIONAL, NATIONAL OR EU LEVEL – ONE ANSWER) Environment policy (REGIONAL, NATIONAL OR EU LEVEL – ONE ANSWER) Fight against crime (REGIONAL, NATIONAL OR EU LEVEL – ONE ANSWER) Health care policy (REGIONAL, NATIONAL OR EU LEVEL – ONE ANSWER)
Agriculture policy (REGIONAL, NATIONAL OR EU LEVEL – ONE ANSWER)
In order to fit the theoretical framework of support of or opposition to the European integration, I
decided to merge the regional and national levels, creating a dichotomous variable with two
categories: “National or subnational level” and “European level”.
Regarding the survey questions that are used to study the instrumental dimension of the EU
support, they are three. Two of them ask an interviewee to assess his/her country’s utility, and the
third one refers to his/her perceived personal benefit of being an EU member:
Q7a. Generally speaking, do you think that (OUR COUNTRY)'s membership of the European Union
is...? (ONE ANSWER)
A good thing
A bad thing
Neither good nor bad
Q8a. Taking everything into consideration, would you say that (OUR COUNTRY) has on balance
benefited or not from being a member of the European Union? (ONE ANSWER)
Has benefited
Has not benefited
Q9a. And what about of people like you? Have people like you on balance benefited or not from
(OUR COUNTRY)'s EU membership? (ONE ANSWER)
Have benefited
Have not benefited
For what concerns the treatment of missing values, for the indicators of political EU support I use
listwise deletion, excluding from the analysis the cases that present a missing value in any of these
items. The responses “Not an area to be dealt with by any level of Government” and “More than one”,
which are recorded as spontaneous answers, are excluded from the analysis. The reason for this is
that the former has been cited by less than 0.01% of the sample, while the latter does not provide
information about which and how many levels should be involved in the policy decisions.
Quite differently, for the questions related to the instrumental evaluation of EU membership, those
who answered “Do not know” (DK) are considered as having neither a positive nor a negative
assessment. Hence, if one responds DK about the overall country’s membership, then it will be
considered as having “neither a good nor a bad” opinion on that issue. If DK refers to the country’s
or the personal benefit of being an EU member, then one will be treated as thinking that the country
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or the respondent has “neither benefitted nor not benefitted” from the EU membership. Any response
coded as “refuse to give the answer”, except the DKs, has been excluded from the analysis5.
In conclusion, we have three levels for each indicator of instrumental EU support:
Country’s membership: “A good thing”, “Neither good nor bad”, “A bad thing”;
Country’s benefit: “Has benefited”, “Neither benefited nor not benefited”, “Has not benefited”
Personal benefit: “Have benefited”, “Neither benefited nor not benefited”, “Have not
benefited”;
and two levels for those items referred to political EU support:
Fighting unemployment: “National or subnational level” or “European level”;
Immigration policy: “National or subnational level” or “European level”;
Environment policy: “National or subnational level” or “European level”;
Fight against crime: “National or subnational level” or “European level”;
Health care policy: “National or subnational level” or “European level”;
Agriculture policy: “National or subnational level” or “European level”;
The measurement model in figure 1 displays graphically the relationship between the latent factors
and the relative indicators. The model allows correlation between the latent dimensions, because it is
supposed a limited covariation exists amid the two.
Figure 1 - Measurement model
The separate test of the measurement model in each country shows that this structure with two
ordinal factors fits the data well in 13 out of the 15 countries included in the Intune 2009 dataset (the
results for each country are in the appendix). Both p-value and BIC statistics are considered in the
model selection, also taking into account whether the chosen model presents a limited classification
5 In the Intune project 2009 dataset, they treat missing values either as “Do not know” (DK) or “Refusals”, giving a
researcher the possibility to distinguish between different kinds of missing.
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error. The analysis confirms that the theorized model, with one factor modelled with two levels
(Positive vs Negative instrumental evaluation), and another factor with three levels (National or sub-
National, Mixed and European Level for policy decisions), fits the data best6. To explain the process
of model selection and to provide evidence to the reader, table 1 reports the analysis of Belgian
respondents.
Table 1 – Measurement model Belgium
The theorized model is number 13, and it is contrasted with several alternative models. Models
from number 1 to 6 are LC cluster models in which there is only one nominal latent variable with an
increasing number of categories (from one to six), whereas models from 7 to 13 are LC factor models.
Models 7-9 are with solely one discrete-ordinal factor and an increasing number of levels (two, three
and four); models 10-13 have two factors and different features. More in details, number 10 resembles
an exploratory factor analysis (Magidson and Vermunt 2003), in which all the items load on both the
two-level factors and the correlation between the factors is fixed to zero. On the contrary, models 11,
12 and 13 are strictly confirmatory factor analysis, since the relationship between indicators and
factors is theory driven, as displayed in figure 1. These last three models have the same factorial
design, but they differ from each other for the number of factor levels: two for each factor in model
11, three for each factor for model 12, whereas in model 13 each factor has a different number of
levels, namely two for the instrumental factor and three for the political factor.
6 This is the best fitting model in Belgium, Denmark, Estonia, France, Germany, Greece, Italy, Poland, Portugal,
Slovakia, Slovenia, Spain, and the UK, whereas in Bulgaria and Hungary the model does not fit. In these 13 countries
the model combines statistical significance (in LCA p-value greater than 0.05), goodness of fit (according to BIC
statistics) and low classification error. All the analysis are performed with Latent GOLD 5.0 (Vermunt & Magidson,
2013).
N. MODEL BIC(LL) p-value Class.Err.
1 1 CLUSTER 11283 0.00 0.00
2 2 CLUSTER 10565 0.00 0.09
3 3 CLUSTER 10365 0.00 0.13
4 4 CLUSTER 10259 0.66 0.16
5 5 CLUSTER 10280 0.91 0.19
6 6 CLUSTER 10311 0.98 0.21
7 EXP 1F2L 10565 0.00 0.09
8 EXP 1F3L 10477 0.00 0.17
9 EXP 1F4L 10476 0.00 0.24
10 EXP 2F2L 10208 0.59 0.06
11 2F2L 10204 0.22 0.06
12 2F3L 10137 0.87 0.21
13 2F 2L-3L 10144 0.79 0.06
BELGIUM
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According to BIC statistics, Model 12 appears to be slightly better than the theorized model (Model
13), but it presents a much higher classification error (21% compared to the 6% for Model 13). Hence,
considering that both are substantially significant (p-value much greater than 0.05), and that they have
very similar BIC statistics but considerably different classification error, Model 13 is identified as the
best fitting model in Belgium as well as in other twelve EU countries included in the dataset:
Denmark, Estonia, France, Germany, Greece, Italy, Poland, Portugal, Slovakia, Slovenia, Spain, and
the UK.
In order to give a substantial meaning to the factor levels, it is necessary to look at the profile table
displayed in table 27, which reports the conditional item response probabilities and the class
proportions. In Belgium, those who are identified as having the “Negative” factor level on the
Instrumental dimension (29% of Belgians) are much more likely to assess negatively the country’s
EU membership, and the country’s and personal utility to be EU member, compared with the
probability of having negative opinion for people who belong to the “Positive” group8 (71% of
Belgians). Regarding the Political factor, three groups are defined, which differ from one another in
the preferences about the appropriate level of governance for six strategic policy areas. Those with
“National” preferences (27% of Belgians) are far more likely to prefer the National level for all the
six policy areas, whereas those who have “European” inclinations tend to favour the European level.
In the middle position between the two, there are those with “Mixed” preferences (58%), namely
Belgians that do not have unilateral preferences, for the reason that they tend to favour the EU level
for some policy areas (Immigration, Environment and Crime) and the National or sub-national level
for other ones (Fighting unemployment, Health care and Agriculture policy)9. Finally, being classified
into one group over a certain dimension allow predicting the most likely pattern of answers on those
survey items.
7 In the appendix Intune 2009 there are the profile tables of all the 13 countries. 8 In order to label the factor it is necessary to look only at the conditional probabilities of the items that are influenced
by that factor, as displayed by figure 1. For the Instrumental factor those items are Country’s membership, Country’s
benefit and Personal benefit, whereas for the Political factor they are Fighting unemployment, Immigration policy,
Environment policy, Fight against crime, Health care policy and Agriculture policy. Item response probabilities for
items that are not directly affected by that factor vary because of the two factors are moderately correlated. 9 As already highlighted, this model with two factors and, respectively, two/three levels holds in 13 out of 15 analysed
countries. The only structural difference among the countries is the conditional item response probabilities for the
“Mixed” level of the Political factor. For example, Belgian citizens with “Mixed” preferences tend to prefer the EU
level for such policies areas, whereas in other countries the “Mixed” preferences group tend to favour that other
policy areas be decided at the EU level. However, this variance does not undermine the model because there is a
clear and substantial difference among the conditional response probabilities of the three groups. To review the
conditional response probabilities see Intune 2009 appendix.
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Table 2 – Profile table Belgium
After having tested the model country by country, the following step is to assess whether the
defined factors have the same meaning in each context10. Applying latent class analysis to the pooled
dataset of 13 countries11, namely doing a multigroup extension of the LC modelling developed so far,
it is possible to detect what level of measurement invariance is present in this cross-national survey
dataset. Kankaraš et al. (2012) summarized the possible outcomes that can occur in comparing latent
structure across groups, when “they may turn out to be completely different (heterogeneous model),
partially different (partially homogenous model), or completely the same (homogeneous model)”
(Kankaraš et al. 2012: 360). The level of homogeneity that the model reaches using the pooled dataset
allows different kinds of cross-country comparison. Figure 2 shows four models that are tested and
compared to assess the measurement invariance.
10 This procedure has already been done studying separately the profile tables, but with this further test it is possible
to statistically analyse the homogeneity of the probability structure. 11 The expected latent structure emerges in the 13 out of 15 countries.
Negative Positive National Mixed European
Size 29% 71% 27% 58% 16%
Overall evaluation of country
membership
A good thing 34% 97% 63% 82% 92%
Neither good nor bad 28% 3% 17% 9% 5%
A bad thing 38% 0% 20% 9% 3%
Evaluation of Country benefit
Has benefited 32% 97% 62% 82% 92%
Neither benefited nor not benefited 10% 2% 6% 4% 3%
Has not benefited 58% 1% 32% 15% 5%
Evaluation of Personal benefit
Has benefited 8% 73% 38% 57% 68%
Neither benefited nor not benefited 4% 5% 4% 5% 5%
Has not benefited 88% 22% 58% 38% 27%
Unemployment
National or subnational level 78% 61% 94% 66% 18%
European level 22% 39% 6% 34% 82%
Immigration
National or subnational level 58% 38% 83% 36% 6%
European level 42% 62% 17% 64% 94%
Environment
National or subnational level 60% 37% 92% 33% 2%
European level 40% 63% 8% 67% 98%
Crime
National or subnational level 61% 43% 84% 42% 9%
European level 39% 57% 16% 58% 91%
Health
National or subnational level 77% 63% 90% 67% 30%
European level 23% 37% 10% 34% 70%
Agriculture
National or subnational level 72% 51% 96% 54% 5%
European level 28% 49% 4% 46% 95%
BELGIUMInstrumental Political (Policy level)
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Figure 2 – Models with different measurement invariance
In the heterogeneity (or heterogeneous) model, the grouping variable has a direct effect on the
manifest variables, which means that “the group variable influences indicators independently of the
latent variable” (Kankaraš et al. 2012: 362), and, much more important, that latent factors (F1 – F2)
and the grouping variable (G) interact with each other in influencing the indicators of each factor (M).
This implies that the effect of the latent structure on the manifest side (indicators) is modified by
group membership, and, thus, this interaction does not allow making any across group comparisons.
Instead, in the partially homogenous model, there is no group-latent variable interaction, and, if
established, partial homogeneity allows comparing countries differences in LC factor memberships
(Kankaraš et al. 2012). Kankaraš et al. (2012) remark the importance of this kind of models, since
partial homogeneity is similar to the metric equivalence in Multigroup-CFA, where only factor
loadings, and not intercepts, are restricted to be equal across groups. In probabilistic terms, the values
of the conditional response probabilities are different in each country, because the group variable
directly influence the indicators, but the effect of the latent variable(s) is the same across groups. The
third model in figure 2 is the structural homogeneous model, where the grouping variable does not
have any direct effect on indicators, but only indirect effect through the latent factors. If this model
fits the data better than the previous two, then the complete measurement invariance is fulfilled. This
corresponds to the scalar equivalent model in Multigroup-CFA in which both intercepts and factor
loadings are the same across groups (Kankaraš et al. 2012). If structural homogeneity is satisfied, the
conditional response probabilities are the same in every country. Finally, the last model is a complete
homogeneity, where both factors and indicators are insensitive of group membership, namely the
measurement model is the same in any of the 13 country considered.
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These models have a hierarchical order, which means that the model with partial homogeneity is
tested after the heterogeneous model has been confirmed by the data analysis12, the structural
homogeneous model is tested only if the former holds, and the same logic is applied for the last one,
the model with complete homogeneity.
To sum up, the pooled dataset includes data from 13 EU countries: Belgium, Denmark, Estonia,
France, Germany, Greece, Italy, Poland, Portugal, Slovakia, Slovenia, Spain, and the UK.
Furthermore, this dataset was weighted in order to equate the number of cases for each country, and
after the weighting process, there are 840 valid cases for each country13. Table 3 reports the analysis
of measurement invariance for the pooled dataset at the scale level, where the partial homogenous
model turns out to be the best-fitting model, according to BIC statistics, which is the most
appropriated selection criterion when the sample size is large as in this case (Hagenaars and
McCutcheon 2002).
Table 3 – Test of the measurement invariance
In conclusion, because the model with partial homogeneity is the best fitting model, it is possible
to evaluate the country specific class proportions that are displayed in table 414.
12 Although the heterogeneity model provides just the same result of the country specific analysis, it serves as baseline
to assess the goodness of fit of the other models. 13 Because of the treatment of missing data, there are countries with approximately 900 cases and country with 600
cases. This weighting process was necessary, thus, to avoid having biased estimates due to interstate differences in
number of cases. 14 The country specific class sizes are slightly different from those included in the country’s profile table, since the
model tested with the pooled dataset include the country as an active covariate, and this addition modifies the overall
structure of probabilities.
POOLED DATASET
MODEL LL BIC(LL) Npar L² df p-value Class.Err.
Heterogeneous -54917 112744 313 9969 10607 1.00 0.04
Partial homogeneous -54954 111813 205 10042 10715 1.00 0.05
Structural homogenous -56392 113435 70 12919 10850 0.00 0.04
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14
Table 4 – Class proportions in each country
As shown in the last row of the table, 31% of the sample has a negative opinion regarding the
utility to be a member of the European Union (instrumental EU support). The highest percentage of
negative assessment can be found in the UK and, the second highest, in Estonia. On the contrary,
southern European countries like Portugal, Italy, Spain and Greece have the lowest percentage of
negative evaluation (about 22%-25%). As far as the dimension of political integration is concerned,
the average percentages of pure Nationalists and pure Europeists are 37% and 11% correspondingly.
The remaining 53% is composed by people who think that some policy areas are better dealt with at
the National or sub-National level, while other ones at the European level. Hence, they are neither
strict Nationalists nor pure Europeists. These groups’ sizes widely vary country by country, but,
again, the UK and Estonia are the most anti-Europeist countries, since their percentages of people
who prefer the National policy level are well above the EU average (respectively 57% and 62%).
Although Denmark does not have a high percentage of Nationalists (34%), it has, together with the
former two, the lowest percentage of citizens who consider the European level as an appropriate arena
for policy decisions (6%). In contrast, Slovakia, Poland, Portugal, and Spain have the highest
percentage of political integration supporters (about 14%-17%). Poland seems to be a particular case,
because both the groups of Nationalists (43%) and Europeists (16%) are above the average, while
only the group with mixed preferences (41%) is below the mean (53%). Finally, France, Germany,
Belgium, Spain, Slovakia Republic and Denmark are the countries with the highest number of people
COUNTRY Negative Positive National Mixed European
UNITED KINGDOM 55% 45% 57% 36% 6%
ESTONIA 42% 58% 62% 32% 6%
GERMANY 35% 65% 27% 65% 9%
FRANCE 35% 65% 23% 69% 8%
POLAND 30% 70% 43% 41% 16%
SLOVENIA 30% 70% 40% 51% 9%
BELGIUM 29% 71% 25% 64% 11%
SLOVAKIA REPUBLIC 27% 73% 21% 62% 17%
DENMARK 26% 74% 34% 60% 6%
PORTUGAL 25% 75% 36% 48% 16%
ITALY 24% 76% 41% 49% 10%
SPAIN 23% 77% 23% 63% 14%
GREECE 22% 78% 46% 42% 12%
average 31% 69% 37% 53% 11%
Instrumental Political (Policy level)
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15
with mixed preferences (from 69% to 60%), namely citizens who only support the Europeanisation
of some policies areas15.
With these two dimensions of EU support, it is possible to create a typology of six types of EU
citizens, which, to some extent, resembles the Kopecky and Mudde party based typology (2002).
Hence, there are six classes of EU citizens (figure 3):
1) the Eurorejects that have both a negative evaluation of the utility to be an EU member and a
preference for the nationalization of the policy governance;
2) the moderate Eurolosers, who evaluate negatively the EU membership, but suppose that some
policy areas may be better governed at the EU level;
3) the Eurorealists, who combine a negative evaluation of the membership with a pro-European
attitude for a EU level policy governance, which is considered by them, in any case, the most
appropriate level;
4) the Euroenthusiasts that merge a positive instrumental consideration with a preference for the
EU political integration;
5) the moderate Eurogainers, who believe that being an EU member is useful but not all policy
decisions should be taken at the EU level;
6) and the last type, the Europragmatists that are Europeist just because they gain from an EU
membership, but they reject the loss of national sovereignty.
Figure 3 – Typology of EU support
Instrumental
Negative Positive
Po
litic
al
National Eurorejects Europragmatists
Mixed Moderate Eurolosers Moderate Eurogainers
European Eurorealists Euroenthusiasts
Therefore, the model of EU support defined so far allows classifying the citizens of the thirteen
countries included in the pooled dataset according to their stances on these latent dimensions. Table
5 reports the percentages of each type of citizens in each country. The moderate Eurogainers type is
the biggest class in every country, except for the UK (21%) and Estonia (23%), and, on average, 39%
of the sample can be classified in this way. It is the group, concurrently with the Eurorejects, where
there is the uppermost range of variation among the countries, from the 21% of the UK up to the 50%
of Spain. Indeed, for what concerns the Eurorejects, the percentage in the pooled dataset is 17%, with
the highest percentages in the UK (37%) and Estonia (32%), and the lowest in Spain (9%), Slovakia
(10%) and Belgium (11%). The average percentage of moderate Eurolosers is 13%, with its peak in
15 See note 9.
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16
France (21%) and Germany (20%), and its bottommost amount in Greece (7%). While the percentage
of Eurorealists is everywhere negligible (about 1-2%), the Europragmatists form a consistent group
almost in all countries (average of 20%), with the highest percentage in Greece (31%) and Italy (27%).
The Euroenthusiasts represent 10% of the sample, with the peak of 15% in Poland and Slovakia, 14%
in Portugal and 13% in Spain, and the lowest percentage in the UK, Estonia and Denmark (5%).
Table 5 – Size of the clusters in each country
To recap, different types of EU support are identified using LCFA, and two dimensions combine
in defining them. This typology permits classifying EU citizens and comparing national distributions.
Taking into account that this technique performs a probabilistic classification based on the
respondent’s pattern of answers, the identification of two separate latent dimensions of EU support
opens up questions about the antecedent factors that may determine these latent attitudes. A
preliminary analysis of those determinants is conducted in the next, final, section, where twelve
covariates are included in the model to predict the latent group membership.
Predictors of the class membership
The last part of this article aims to perform a preliminary test of the predictors of the class
membership. There are several theories in literature that address the mechanism of developing
attitudes towards the EU, and three kinds of theoretical perspectives are here considered. They
respectively look at the affective and identitarian factors, the institutional distrust, and the cognitive
mobilization. The first force that is alleged to affect EU attitudes is the sense of national identity,
which entails a refusal of the Europeanisation of the national polity seen as a threat to the national
Instrumental dimension Negative Negative Negative Positive Positive Positive
Political dimension National Mixed European National Mixed European
COUNTRY Eurorejects
Moderate
Eurolosers Eurorealists Europragmatists
Moderate
Eurogainers Euroenthusiasts
BELGIUM 11% 16% 1% 14% 48% 10%
DENMARK 13% 12% 1% 21% 48% 5%
GERMANY 14% 20% 1% 13% 45% 7%
GREECE 14% 7% 1% 31% 36% 11%
SPAIN 9% 13% 1% 14% 50% 13%
FRANCE 12% 21% 1% 11% 48% 7%
ITALY 15% 9% 1% 27% 40% 9%
PORTUGAL 14% 10% 1% 22% 39% 14%
UNITED KINGDOM 37% 16% 1% 20% 21% 5%
ESTONIA 32% 10% 1% 30% 23% 5%
POLAND 19% 10% 2% 24% 31% 15%
SLOVAKIA REPUBLIC 10% 16% 2% 12% 46% 15%
SLOVENIA 17% 12% 1% 23% 39% 8%
Average 17% 13% 1% 20% 39% 10%
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17
cultural integrity (Carey 2002; McLaren 2002, 2004; Hooghe and Marks 2005; Lubbers 2008).
Nevertheless, although the EU is supposed to be a real danger, the effect of perceiving the EU as a
symbolic threat appears to be fairly limited (McLaren 2004). A very large part of Europeans perceives
this risk, but many of them still favour the European integration (ibidem). Therefore, Hooghe and
Marks (2004) and McLaren (2007) theorize a refinement of this theory, claiming that people who
conceptualize their identities exclusively in terms of national identity are likely to be against the EU
project, whereas those who have either multiple identities that include the European dimension or a
fully European identity are likely to support the EU (McLaren 2007). In addition, the Intune dataset
contains also a measurement of the affective attachment to the EU, which can be regarded as a proxy
for a general inclination towards Europe (ibidem), and for this reason it is included as a predictor of
the group membership.
The second theoretical perspective is the one that looks at the institutional distrust to explain EU
attitudes. It is well recognized that people often use heuristics when they deal with politics, especially
when they form opinions on a distant political institution such as the EU (Sanders et al. 2012). The
underlying logic is that very few European citizens have enough knowledge about the EU for
developing a real informed opinion about it. Hence, when they need to cope with this topic, be it in a
referendum or a survey, they use proxies, like, for instance, support for the current national
government, satisfaction with the national democracy or trust in national institutions (e.g. Anderson
1998; Gabel 1998a; McLaren 2004, 2007; Rohrscheneider 2002; Ray 2003a, 2003b; Sanchez-Cuenca
2000). The central point here is that they use information about something they know, that is the
national politics and the national institutional system, to make judgment regarding something they
know less (McLaren 2002). If they positively evaluate their national environment, they positively
assess the EU, transferring their opinion from one domain to the other. However, what has also been
theorized is the opposite mechanism, namely that people who perceive their own national-level
political institutions as corrupted or inefficient are likely to see positively the EU institutions, since
they can limit the power of such national institutions (Sanchez-Cuenca 2000). Similar to this last
rationale, the one applied by citizens who do not trust EU institutions or who perceive EU democratic
deficit (Leconte 2015) is quite straightforward: they should oppose the EU integration.
The last theory to be considered here is cognitive mobilization, which is grounded on Inglehart’s
theory of the Silent Revolution (Inglehart 1977), which states that the individual attitudes towards the
European integration are highly influenced by the level of political skills (Inglehart 1977; Inglehart
and Rabier 1978). Inglehart looks at the education and the cultural and political knowledge to explain
support for supranational integration. He theorizes that, due to the high level of abstraction that the
European project possesses, only citizens with an elevated amount of political skills are able to deal
with the complexity of those processes, understanding political discourses about it and developing
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18
personal thoughts (Inglehart 1977). Inglehart supposes that having political skills is the antecedent
needed to produce positive attitudes toward Europe, since to higher skilled people the European
dimension is more familiar and less threatening than for poorer skilled ones (Jassen 1991). On the
contrary, those who do not have such skills should be more worried by the EU, since they are unaware
of what the EU actually is, which entails opposition to an EU membership (ibidem). Empirical
analyses have demonstrated that those who are better educated and frequently involved in political
discussions are more conscious of the EU and have more positive stances on the integration project
(ibidem).
From these theories, it is possible to draw the following exploratory hypotheses regarding the
relationship between those determinants and the latent class membership defined above:
Affective and identitarian factors: people who conceptualize their identities exclusively in terms
of national identity are likely to be against the EU project because it is perceived as a symbolic
threat; whereas, those who have an affective attachment to the EU are likely to have pro
integration attitudes;
Hp1: if a person identifies him/herself exclusively in terms of national identity, he/she is
expected to be against the EU and more political Europeanisation.
Hp2: if a person has an affective attachment to the EU, he/she is expected to be in favour
of the EU and more political Europeanisation.
Cueing rationality and institutional distrust: people often use heuristics when dealings with
opinions about EU issues. Here there are two potential alternative explanations, both involving
trust and satisfaction in national and European institutions. It is alleged that for the national
dimension:
Hp3a: if a person has positive levels of trust and satisfaction with national institutions and
the national democratic system, he/she is expected to be in favour of the EU and more
political Europeanisation.
On the other hand, the alternative mechanism is (see Sanchez-Cuenca 2000):
Hp3b: if a person has positive levels of trust and satisfaction with national institutions and
the national democratic system, he/she is expected to be against the EU and more political
Europeanisation.
Whereas for the European dimension the expected influence of trust and satisfaction is supposed
to be simply positive:
Hp4: if a person has positive levels of trust and satisfaction with European institutions and
the European democratic system, he/she is expected to be in favour of the EU and more
political Europeanisation.
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19
Cognitive mobilization: those who are able to understand complex political events are likely to
be more EU supportive;
Hp5: if a person has high levels of political knowledge and interest, he/she is expected to
be in favour of the EU and more political Europeanisation.
Hp6: The higher the personal education level, the more the favour towards the EU and
political Europeanisation.
All the reported theories are based on individual level mechanisms, but individuals are nested in
different national contexts, and each context may modify the effect of such determinants. Therefore,
the results presented here refer to the average effect of each exogenous variables in the pooled dataset.
The aforementioned predictors are included in the model as inactive covariates16, with the addiction
of gender, age and left-right self-placement as supplementary potential predictors17. This latter
covariate is included as a proxy for party support (see Gabel 1998a), since it is assumed that self-
placement and party support are strongly correlated (see McLaren 2002).
The first two hypotheses (hp1 and hp2) regarding the effects of exclusive National identities and
attachment towards the EU seem to be confirmed by the analysis of the class predictors (tables 6 and
7). The levels of instrumental as well as political support vary in the hypothesized direction, because
the probability of having a defined stance on the latent dimensions is highly influenced by the
exogenous variable. For example, the probability to have a “Negative” stance on the Instrumental
factor is 19% if the respondent has a European identity, but, if one has an exclusive national identity,
the probability raises to 51%. The same mechanism works also for the other factor, where preference
for an exclusive National policymaking increases its probability to emerge in case of strict national
identity from 29% to 50%. Similarly, the increment of the attachment towards the EU produces a
dramatic decrease in the probability to have both a “Negative” stance and nationalistic preferences.
Table 6 - Identity
16 Using the inactive covariates method means “computing descriptive measures for the association between covariates
and the latent variable after estimating a model without covariates” (Vermunt and Magidson 2013: 25). 17 Missing values are excluded from the analysis. EU knowledge is an additive index that is created using two questions
that test a respondent’s real knowledge about how many and what states compose the EU. This index has three
levels, “None” for no correct answers, “Limited” for one correct answer, “Full” for both correct. ‘Do not know’ is
considered as an incorrect answer, and missing data are excluded from the analysis. In a similar way, interest in EU
politics is an additive index of “Generally interest in politics” and “How far do you feel that what happens to Europe
in general has important consequences for people like you?”. If both answers are positive then one has “Full interest”,
if only one is positive then “Limited interest”, and “No interest” when both are negative. However, even in this case,
‘Do not know’ is considered as a negative answer, and missing data are excluded from the analysis.
Identity Negative Positive National Mixed European
Also European 19% 81% 29% 58% 13%
Nationality only 51% 49% 50% 43% 8%
Instrumental Political (Policy level)
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20
Table 7 – attachment to the EU
Hp3a regarding the transfer effect from the evaluation of the National democratic system is
confirmed by the analysis, while the alternative hypothesis 3b is rejected, since higher degrees of trust
in the National system tend to produce better evaluation of an EU membership and reduce the
probability of Nationalistic attitudes (tables 8-9-10-11). The effect of satisfaction with and trust in
the EU democratic system is in line with the expected result (hp4), that is those who believe that EU
democracy suffers from democratic deficit tend to prefer National or sub-national policy arena and
negatively evaluate the EU membership, compared to those with a positive opinion on the EU
democracy.
Table 8 – Satisfaction with national democracy
Table 9 – Trust in national Parliament
attachment to the EU Negative Positive National Mixed European
Not at all attached 71% 29% 57% 36% 7%
Not very attached 43% 57% 41% 51% 8%
Somewhat attached 22% 78% 33% 56% 11%
Very attached 13% 87% 28% 56% 16%
Instrumental Political (Policy level)
satisfaction_National_democracy Negative Positive National Mixed European
Very dissatisfied 49% 51% 44% 45% 12%
Somewhat dissatisfied 34% 66% 37% 52% 11%
Neither satisfied nor dissatisfied 42% 58% 47% 39% 14%
Somewhat satisfied 22% 78% 34% 56% 10%
Very satisfied 18% 82% 30% 59% 11%
Instrumental Political (Policy level)
Trust_National_Parliament Negative Positive National Mixed European
0-1 50% 50% 49% 39% 12%
2-3 37% 63% 41% 48% 12%
4-6 29% 71% 35% 55% 10%
7-8 17% 83% 30% 61% 9%
9-10 17% 83% 27% 60% 13%
Instrumental Political (Policy level)
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21
Table 10 – Satisfaction with EU democracy
Table 11 – Trust in EU Parliament
The last hypothesis concerns cognitive mobilization (hp5), which states that the higher the personal
political skills, the better the evaluation of the EU. The effect of cognitive mobilization seems to be
confirmed looking at the conditional distributions of probabilities for different levels of EU
knowledge, EU interest and education (tables 12-13-14). In fact, except the invariance of the
preference for an exclusive European policy governance (European level for the Political factor), the
probabilities of having “Negative” or “Positive” instrumental evaluation and “National” or “Mixed”
preferences for policy decisions are highly affected by the antecedent levels of political skills and
educational attainments.
Table 12 – EU knowledge
Table 13 – EU interest
satisfaction_EU_democracy Negative Positive National Mixed European
Very dissatisfied 69% 31% 51% 40% 9%
Somewhat dissatisfied 46% 54% 39% 51% 10%
Neither satisfied nor dissatisfied 47% 53% 56% 38% 7%
Somewhat satisfied 20% 80% 34% 55% 11%
Very satisfied 12% 88% 27% 57% 16%
Instrumental Political (Policy level)
Trust_EU_Parliament Negative Positive National Mixed European
0-1 64% 36% 53% 38% 9%
2-3 48% 52% 42% 49% 10%
4-6 27% 73% 34% 54% 11%
7-8 13% 87% 29% 60% 11%
9-10 13% 87% 27% 57% 16%
Instrumental Political (Policy level)
EU_know Negative Positive National Mixed European
None 35% 65% 40% 50% 10%
Partial 25% 75% 32% 56% 12%
Good 17% 83% 26% 62% 13%
Instrumental Political (Policy level)
EU_interest Negative Positive National Mixed European
None 44% 56% 44% 47% 9%
Limited interest 35% 65% 39% 50% 11%
Full interest 24% 76% 33% 56% 11%
Instrumental Political (Policy level)
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22
Table 14 - Education
Conversely, gender and left-right self-allocation do not determine a much different probability
distribution (tables 15 and 16), whereas the age of the respondent seems to be modestly influent, since
younger cohorts are more supportive of the EU and less nationally oriented than older cohorts, albeit
the dissimilarity in the predicted probabilities is not large (table 17).
Table 15 - Gender
Table 16 – Left-right self-placement
Table 17 - Age
To conclude, in the last part of the article I tested the above-cited hypotheses, although in a
preliminary way since considering the effect of each exogenous variable at time. I included inactive
covariates in the theorized model to predict the class membership and to profile the latent classes,
since covariates do not predict the manifest items but the level of the latent dimensions.
education Negative Positive National Mixed European
Low 40% 60% 42% 47% 12%
Mid 33% 67% 39% 50% 10%
High 22% 78% 30% 59% 11%
Instrumental Political (Policy level)
gender Negative Positive National Mixed European
Male 28% 72% 35% 54% 12%
Female 34% 66% 39% 51% 10%
Instrumental Political (Policy level)
Left_Right Negative Positive National Mixed European
0-1 (Far Left) 32% 68% 35% 51% 14%
2-3 (Left) 23% 77% 27% 61% 12%
4-6 (Centre) 32% 68% 37% 52% 10%
7-8 (Right) 25% 75% 35% 56% 9%
9-10 (Far Right) 32% 68% 42% 47% 10%
Instrumental Political (Policy level)
AGE_CLASSES Negative Positive National Mixed European
16-30 years 25% 75% 31% 58% 11%
31-45 years 30% 70% 36% 54% 10%
46-60 years 34% 66% 38% 50% 12%
61-75 years 35% 65% 42% 47% 11%
over 75 years 35% 65% 40% 49% 10%
Instrumental Political (Policy level)
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23
Overall, it seems that the probability to be Europeanist for matters of policy governance varies less
than the other latent levels when controlling for these predictors. Few exceptions regard the
conditional probabilities for different degrees of attachment towards the EU, trust in and satisfaction
with EU democratic system. Quite differently, both the levels of Instrumental EU support, as well as
the “National” and the “Mixed” levels of Political EU support have a probability structure that
confirms the stated hypotheses18. This preliminary analysis allows supposing that in order to obtain
further support for the EU political integration it is needed to implement further improvement to solve
the perceived democratic deficit and enhance citizens’ political representation in the EU institutions.
Conclusions and further researches
The purposes of this research have been driven by the consideration that the concept of
Euroscepticism and, more generally of EU support, could be more complex than what is frequently
reported in empirical studies. Using Latent Class Analysis, it has been possible to establish the
bidimensionality of this concept, thus validating Lubbers & Scheepers’ theory (2005). These authors
theorized, and then proved using Confirmatory Factor Analysis on Eurobarometer data, that EU
support is a phenomenon that lays on two dimensions: one concerning the political integration – in
terms of European vs National governance of strategic policy areas – and another one related to the
instrumental evaluation of a country’s EU membership based on cost-benefit analysis (Lubbers and
Scheepers 2005). The present article aimed to further validate this theory, separately using two
datasets, namely the Intune project 2009 data the European Election Study 2014. The Intune data
strongly supports the bidimensionality of the concept in 13 out 15 EU countries, whereas with the
EES 2014 data this structure is, on the contrary, not confirmed, most likely due to the lack of
appropriate indicators in the dataset.
Therefore, the survey data provided by the Intune project allow us to elaborate a typology of the
EU support, grounded on these two dimensions, modelled as discrete-ordinal factors. In the thirteen
countries where the bidimensionality holds, citizens are classified in six types, according to their
attitudes towards the EU. The clusterization is based on the comparison between the respondent’s
pattern of answers and the predicted one for each type of EU support. Hence, combining the two
latent dimensions, six types of clusters are defined: the Eurorejects, the moderate Eurolosers, the
Eurorealists, the Euroenthusiasts, the moderate Eurogainers, and the Europragmatists.
Finally, several predictors of the class membership are separately tested, and supporting evidence
is found for the theories regarding the effect of affective and identitarian factors, institutional distrust,
and cognitive mobilization on the attitudes towards the EU. Further improvements of the analysis
should be performed in order to simultaneously test the effect of those predictors, using multinomial
18 Except the alternative hypothesis hp3b.
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24
regression in the LCA framework, and, in addition, multiple imputation techniques may be used to
avoid losing information due to the missing values.
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25
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Appendix – Intune 2009
Measurement models19
19 Each national sample is weighted with WMID2 (SD Weight - national weight) provided by the Intune 2009 project
dataset.
MODEL BIC(LL) p-value Class.Err. BIC(LL) p-value Class.Err. BIC(LL) p-value Class.Err. BIC(LL) p-value Class.Err. BIC(LL) p-value Class.Err.
1 CLUSTER 11283 0.00 0.00 10073 0.00 0.00 10107 0.00 0.00 6210 0.00 0.00 11354 0.00 0.00
2 CLUSTER 10565 0.00 0.09 9434 0.00 0.03 9160 0.00 0.03 5767 0.00 0.06 10691 0.00 0.08
3 CLUSTER 10365 0.00 0.13 8971 0.00 0.06 8959 0.39 0.10 5542 0.02 0.10 10538 0.83 0.09
4 CLUSTER 10259 0.66 0.16 8904 0.00 0.10 8940 0.95 0.09 5534 0.41 0.13 10470 1.00 0.16
5 CLUSTER 10280 0.91 0.19 8887 0.00 0.15 8925 1.00 0.12 5555 0.78 0.16 10508 1.00 0.18
6 CLUSTER 10311 0.98 0.21 8880 0.00 0.14 8945 1.00 0.13 5579 0.96 0.18 10555 1.00 0.21
EXP 1F2L 10565 0.00 0.09 9265 0.00 0.19 9160 0.00 0.03 5656 0.00 0.12 10624 0.02 0.22
EXP 1F3L 10477 0.00 0.17 9260 0.00 0.12 9034 0.00 0.12 5696 0.00 0.13 10624 0.01 0.16
EXP 1F4L 10476 0.00 0.24 9265 0.00 0.19 9003 0.01 0.10 5656 0.00 0.12 10624 0.02 0.22
EXP 2F2L 10208 0.59 0.06 8872 0.00 0.05 8894 0.91 0.06 5504 0.18 0.06 10448 1.00 0.06
2F2L 10204 0.22 0.06 8843 0.00 0.05 8846 0.91 0.02 5460 0.19 0.08 10441 0.99 0.05
2F3L 10137 0.87 0.21 8680 0.00 0.05 8727 1.00 0.07 5406 0.80 0.08 10371 1.00 0.22
2F 2L-3L 10144 0.79 0.06 8613 0.05 0.13 8782 1.00 0.02 5407 0.84 0.22 10372 1.00 0.05
ESTONIA FRANCEBULGARIA DENMARKBELGIUM
MODEL BIC(LL) p-value Class.Err. BIC(LL) p-value Class.Err. BIC(LL) p-value Class.Err. BIC(LL) p-value Class.Err. BIC(LL) p-value Class.Err.
1 CLUSTER 10658 0.00 0.00 8550 0.00 0.00 11329 0.00 0.00 8926 0.00 0.00 9999 0.00 0.00
2 CLUSTER 10016 0.00 0.05 7700 0.00 0.06 10435 0.00 0.03 8199 0.00 0.08 8913 0.00 0.05
3 CLUSTER 9705 0.00 0.08 7445 0.05 0.08 10116 0.00 0.08 7964 0.00 0.10 8661 0.00 0.09
4 CLUSTER 9607 0.53 0.14 7372 0.96 0.10 9883 0.00 0.11 7928 0.12 0.14 8613 0.16 0.12
5 CLUSTER 9640 0.76 0.17 7359 1.00 0.14 9855 0.00 0.11 7911 0.77 0.15 8602 0.77 0.12
6 CLUSTER 9661 0.95 0.19 7379 1.00 0.15 9847 0.00 0.14 7940 0.93 0.15 8622 0.96 0.15
EXP 1F2L 10016 0.00 0.05 9003 0.01 0.10 10380 0.00 0.21 8075 0.00 0.19 8789 0.00 0.21
EXP 1F3L 9974 0.00 0.13 7594 0.00 0.12 10377 0.00 0.10 8094 0.00 0.12 8808 0.00 0.13
EXP 1F4L 9978 0.00 0.20 7567 0.00 0.21 10380 0.00 0.21 8075 0.00 0.19 8789 0.00 0.21
EXP 2F2L 9622 0.05 0.05 7360 0.70 0.06 9834 0.00 0.03 7888 0.06 0.09 8587 0.04 0.08
2F2L 9604 0.01 0.05 7338 0.45 0.04 9803 0.00 0.04 7884 0.01 0.05 8555 0.02 0.05
2F3L 9573 0.09 0.17 7236 1.00 0.19 9613 0.00 0.09 7743 0.90 0.11 8420 0.95 0.09
2F 2L-3L 9582 0.05 0.05 7237 1.00 0.04 9676 0.00 0.04 7771 0.66 0.05 8447 0.78 0.05
ITALY POLANDGERMANY GREECE HUNGARY
MODEL BIC(LL) p-value Class.Err. BIC(LL) p-value Class.Err. BIC(LL) p-value Class.Err. BIC(LL) p-value Class.Err. BIC(LL) p-value Class.Err.
1 CLUSTER 10476 0.00 0.00 11234 0.00 0.00 8908 0.00 0.00 9795 0.00 0.00 9323 0.00 0.00
2 CLUSTER 9514 0.00 0.06 10508 0.00 0.07 8295 0.00 0.08 9241 0.00 0.10 8126 0.00 0.03
3 CLUSTER 9183 0.00 0.07 10096 0.00 0.09 8053 0.00 0.09 8933 0.51 0.11 7831 0.94 0.05
4 CLUSTER 9019 0.47 0.09 10012 0.00 0.11 7957 0.12 0.12 8837 1.00 0.12 7751 1.00 0.09
5 CLUSTER 8993 0.98 0.13 9981 0.20 0.13 7949 0.71 0.13 8841 1.00 0.17 7755 1.00 0.12
6 CLUSTER 9008 1.00 0.15 10001 0.53 0.16 7982 0.89 0.14 8860 1.00 0.18 7761 1.00 0.14
EXP 1F2L 9410 0.00 0.22 10344 0.00 0.15 8217 0.00 0.25 9137 0.00 0.14 7952 0.01 0.10
EXP 1F3L 9412 0.00 0.12 10346 0.00 0.09 8221 0.00 0.11 9171 0.00 0.18 7981 0.00 0.09
EXP 1F4L 9410 0.00 0.22 10344 0.00 0.15 8217 0.00 0.25 9137 0.00 0.14 7952 0.01 0.10
EXP 2F2L 8961 0.48 0.06 9968 0.00 0.04 7900 0.13 0.05 8790 1.00 0.11 7708 1.00 0.03
2F2L 8937 0.28 0.04 9957 0.00 0.04 7871 0.07 0.05 8777 1.00 0.03 7723 1.00 0.02
2F3L 8802 1.00 0.13 9786 0.58 0.11 7781 0.87 0.16 8704 1.00 0.20 7587 1.00 0.10
2F 2L-3L 8829 0.99 0.04 9825 0.20 0.04 7797 0.72 0.05 8706 1.00 0.03 7634 1.00 0.02
SPAIN UKPOLAND SLOVAKIA SLOVENIA
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Profile tables
Negative Positive National Mixed European
Size 26% 74% 35% 58% 7%
Overall evaluation of country
membership
A good thing 12% 95% 54% 83% 92%
Neither good nor bad 9% 4% 6% 4% 4%
A bad thing 79% 2% 40% 13% 4%
Evaluation of Country benefit
Has benefited 21% 98% 59% 87% 96%
Neither benefited nor not benefited 10% 2% 6% 3% 2%
Has not benefited 69% 0% 34% 10% 2%
Evaluation of Personal benefit
Has benefited 5% 76% 41% 66% 74%
Neither benefited nor not benefited 5% 8% 7% 8% 8%
Has not benefited 90% 16% 52% 26% 18%
Unemployment
National or subnational level 90% 78% 97% 79% 31%
European level 10% 22% 3% 21% 69%
Immigration
National or subnational level 84% 65% 96% 62% 9%
European level 16% 35% 4% 38% 91%
Environment
National or subnational level 66% 40% 84% 30% 3%
European level 34% 60% 16% 70% 97%
Crime
National or subnational level 80% 61% 92% 57% 13%
European level 20% 39% 8% 43% 87%
Health
National or subnational level 94% 85% 98% 87% 43%
European level 6% 15% 2% 13% 57%
Agriculture
National or subnational level 81% 54% 99% 46% 1%
European level 19% 46% 1% 54% 99%
Political (Policy level)DENMARK
Instrumental
Negative Positive National Mixed European
Size 42% 58% 61% 33% 6%
Overall evaluation of country
membership
A good thing 26% 90% 59% 69% 77%
Neither good nor bad 50% 9% 30% 23% 18%
A bad thing 23% 0% 11% 8% 5%
Evaluation of Country benefit
Has benefited 45% 97% 71% 80% 86%
Neither benefited nor not benefited 16% 3% 9% 7% 5%
Has not benefited 39% 1% 20% 13% 8%
Evaluation of Personal benefit
Has benefited 11% 87% 50% 62% 71%
Neither benefited nor not benefited 4% 4% 4% 4% 4%
Has not benefited 84% 9% 46% 34% 24%
Unemployment
National or subnational level 90% 83% 98% 76% 14%
European level 10% 17% 2% 24% 87%
Immigration
National or subnational level 81% 73% 92% 58% 14%
European level 19% 27% 8% 42% 86%
Environment
National or subnational level 87% 79% 97% 67% 12%
European level 13% 21% 3% 33% 88%
Crime
National or subnational level 90% 82% 99% 73% 10%
European level 10% 18% 1% 27% 90%
Health
National or subnational level 92% 85% 100% 81% 7%
European level 8% 15% 0% 19% 93%
Agriculture
National or subnational level 91% 83% 99% 77% 11%
European level 9% 17% 1% 23% 89%
Instrumental Political (Policy level)ESTONIA
Negative Positive National Mixed European
Size 32% 68% 23% 69% 8%
Overall evaluation of country
membership
A good thing 33% 92% 53% 78% 89%
Neither good nor bad 8% 4% 7% 5% 4%
A bad thing 59% 4% 40% 18% 7%
Evaluation of Country benefit
Has benefited 20% 96% 47% 78% 92%
Neither benefited nor not benefited 5% 2% 4% 2% 2%
Has not benefited 75% 2% 50% 20% 6%
Evaluation of Personal benefit
Has benefited 5% 60% 24% 46% 57%
Neither benefited nor not benefited 1% 3% 2% 3% 3%
Has not benefited 93% 37% 74% 51% 40%
Unemployment
National or subnational level 81% 65% 98% 69% 9%
European level 19% 35% 2% 31% 91%
Immigration
National or subnational level 56% 35% 84% 32% 4%
European level 44% 65% 16% 68% 96%
Environment
National or subnational level 56% 33% 88% 30% 2%
European level 44% 67% 12% 71% 98%
Crime
National or subnational level 70% 50% 95% 50% 6%
European level 30% 50% 5% 50% 94%
Health
National or subnational level 82% 67% 97% 70% 14%
European level 18% 33% 3% 30% 86%
Agriculture
National or subnational level 73% 55% 94% 56% 10%
European level 27% 45% 6% 44% 90%
Instrumental Political (Policy level)FRANCE
Negative Positive European Mixed National
Size 35% 65% 36% 44% 20%
Overall evaluation of country
membership
A good thing 36% 97% 84% 75% 64%
Neither good nor bad 12% 2% 4% 6% 7%
A bad thing 52% 1% 12% 20% 29%
Evaluation of Country benefit
Has benefited 6% 87% 69% 57% 43%
Neither benefited nor not benefited 2% 3% 3% 3% 3%
Has not benefited 92% 10% 28% 40% 55%
Evaluation of Personal benefit
Has benefited 5% 69% 55% 45% 34%
Neither benefited nor not benefited 2% 4% 4% 3% 3%
Has not benefited 93% 27% 42% 51% 63%
Unemployment
National or subnational level 81% 72% 50% 86% 97%
European level 19% 28% 50% 14% 3%
Immigration
National or subnational level 65% 53% 27% 66% 91%
European level 35% 47% 73% 34% 9%
Environment
National or subnational level 42% 30% 9% 36% 77%
European level 58% 70% 91% 64% 23%
Crime
National or subnational level 45% 33% 12% 40% 76%
European level 55% 67% 88% 60% 24%
Health
National or subnational level 83% 72% 48% 90% 99%
European level 17% 28% 52% 10% 1%
Agriculture
National or subnational level 68% 53% 23% 71% 95%
European level 32% 47% 77% 29% 5%
InstrumentalGERMANY
Political (Policy level)
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31
Negative Positive National Mixed European
Size 22% 78% 45% 43% 13%
Overall evaluation of country
membership
A good thing 30% 95% 76% 83% 88%
Neither good nor bad 23% 4% 10% 8% 6%
A bad thing 48% 1% 14% 9% 6%
Evaluation of Country benefit
Has benefited 25% 98% 77% 85% 90%
Neither benefited nor not benefited 6% 1% 2% 2% 2%
Has not benefited 69% 1% 21% 13% 8%
Evaluation of Personal benefit
Has benefited 8% 75% 55% 62% 68%
Neither benefited nor not benefited 2% 2% 2% 2% 2%
Has not benefited 91% 23% 43% 35% 30%
Unemployment
National or subnational level 78% 66% 99% 56% 2%
European level 22% 34% 1% 44% 98%
Immigration
National or subnational level 67% 56% 89% 42% 6%
European level 33% 44% 11% 58% 94%
Environment
National or subnational level 69% 57% 92% 42% 5%
European level 31% 43% 8% 58% 95%
Crime
National or subnational level 80% 69% 97% 64% 10%
European level 20% 31% 3% 36% 90%
Health
National or subnational level 84% 74% 98% 72% 11%
European level 16% 26% 2% 28% 89%
Agriculture
National or subnational level 82% 73% 97% 70% 16%
European level 18% 27% 3% 30% 84%
GREECEInstrumental Political (Policy level)
Negative Positive National Mixed European
Size 24% 76% 39% 51% 10%
Overall evaluation of country
membership
A good thing 33% 96% 73% 85% 92%
Neither good nor bad 26% 4% 12% 8% 5%
A bad thing 41% 0% 16% 8% 3%
Evaluation of Country benefit
Has benefited 22% 93% 66% 80% 88%
Neither benefited nor not benefited 11% 4% 7% 6% 5%
Has not benefited 68% 3% 27% 14% 7%
Evaluation of Personal benefit
Has benefited 6% 68% 45% 57% 64%
Neither benefited nor not benefited 6% 9% 8% 9% 9%
Has not benefited 88% 22% 47% 34% 27%
Unemployment
National or subnational level 82% 67% 98% 62% 4%
European level 18% 33% 2% 38% 96%
Immigration
National or subnational level 60% 41% 87% 22% 1%
European level 40% 59% 13% 78% 99%
Environment
National or subnational level 73% 55% 95% 42% 3%
European level 27% 45% 5% 58% 97%
Crime
National or subnational level 78% 65% 94% 59% 13%
European level 22% 35% 6% 41% 87%
Health
National or subnational level 90% 79% 98% 81% 24%
European level 10% 21% 2% 19% 76%
Agriculture
National or subnational level 88% 76% 99% 77% 8%
European level 12% 24% 1% 23% 92%
ITALYInstrumental Political (Policy level)
Negative Positive National Mixed European
Size 32% 68% 43% 41% 16%
Overall evaluation of country
membership
A good thing 28% 95% 66% 78% 87%
Neither good nor bad 39% 4% 20% 13% 9%
A bad thing 32% 0% 14% 8% 4%
Evaluation of Country benefit
Has benefited 27% 98% 66% 80% 89%
Neither benefited nor not benefited 21% 2% 10% 7% 4%
Has not benefited 52% 0% 23% 13% 7%
Evaluation of Personal benefit
Has benefited 8% 64% 39% 49% 56%
Neither benefited nor not benefited 4% 7% 6% 6% 7%
Has not benefited 87% 30% 55% 44% 37%
Unemplo