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Euroskepticism, Income Inequality and Financial Expectations
Jo Ritzena, Caroline Wehnera and Klaus F. Zimmermannb
November 11, 2015
Abstract
Before the Great Recession, the rising income inequality within the “old” European Union has been
suggested as an important driver of the increase in Euroskepticism. We revisit this finding for the 27
EU member states from 2006 to 2011, introducing individual negative financial expectations as a
further driving factor. We also distinguish between Western and Eastern European countries. In the
period of Eastern EU enlargement after 2005, Euroskepticism increased by one third while income
inequality on average remained stable. Negative financial expectations are positively related to
Euroskepticism in the West and non-significantly negatively related in the East. This suggests that
Westerners interpret European integration as a threat, while Easterners view it as a chance. In addition,
income inequality lost its role in “old” Europe. An increase of one Gini point decreases the probability
of Euroskepticism by half a percentage point in the West, while it has no impact in the East.
JEL Classification: D31, J31, O43, O52, P48, Z18.
Keywords: Euroskepticism, income inequality, expectations, economic growth, unemployment.
* We wish to thank Arnaud Chevalier, Corrado Giulietti, Spyros Konstantopoulos, Frank Vella and Rainer Winkelmann as well as the participants of the 9th ECPR General Conference at University of Montreal for helpful discussions on earlier drafts and Victoria Finn for editorial suggestions.
a Institute for the Study of Labor (IZA), Germany and Maastricht University, The Netherlands b Institute for the Study of Labor (IZA), Germany and University of Bonn, Germany
Klaus F. Zimmermann IZA P.O. Box 7240 53072 Bonn Germany Phone: +49-228-3894-0 Fax: +49-228-3894-180 E-mail: [email protected]
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1. Introduction
Euroskepticism is the European catchword for skepticism about the European Union.1 Our
attempt at understanding Euroskepticism is driven by its policy relevance. The policy relevance comes
from the potential impact of Euroskepticism on the member states’ willingness to agree on further
cooperative steps or enlargement. Euroskepticism also translates into the growth of anti-European
parties and in a shift of traditionally pro-European parties towards a less pro-European cooperative
point of view, thus undermining the foundations for strengthened cooperation between European
countries.
We aim to contribute to the literature by shedding light on Euroskepticism formation in times of
the recent financial and economic crisis and by answering the following questions: How stable are the
trends from the past in explaining Euroskepticism? What is the relation between negative financial
expectations and Euroskepticism? And are there differences between Western and post-communist
EU member states? Our measure of Euroskepticism is based on the positive, neutral or negative
answer to the Eurobarometer (EB) question, “Generally speaking, do you think that (your country)’s
membership of the European Union is …?” We distinguish between Western Europe and the post-
communist Eastern EU member states, as fundamental variation in the political and economic
pathways in Western and former communist EU member states make it likely that the two regions
have different decision making processes about Euroskepticism.
We analyze Euroskepticism formation by following the utilitarian explanatory approach. In this
approach individual attitudes are driven by socio-economic background variables such as gender, age,
education, profession, and the degree of urbanization, as well as macro-economic variables pertaining
to the country, namely GDP per capita, unemployment, inflation, income distribution and transfers
from or to the EU. Before the Great Recession that started in 2007, the rising income inequality within
the “old” European Union has been suggested as an important driver of the increase in Euroskepticism
(Kuhn et al., 2014). This is in line with the concerns about the rise in inequality expressed in the broad
public debate about the recent book by Piketty (2014). However, re-examining the data for the
comprehensive period of 2006 to 2011 for the EU-27 countries, we cannot confirm that higher
1 “Euroskepticism” does not necessarily refer to the European currency as some people infer. Hence, it would probably be better to call it “EU skepticism.” However, an established literature uses the term “Euroskepticism,” which we follow in this article. Although EU membership does not necessarily imply EMU membership, increasing problems with the governance of the Euro area may have also been a concern of many participants in the study.
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inequality drives Euroskepticism. To the contrary, we find the relationship to be negative in the West,
while for the East there is no effect at all.
We further add to the literature by analyzing differences in the relation between utilitarian
considerations and Euroskeptic attitudes in post-communist and Western EU countries within the
period 2006 to 2011 by using the transmission mechanism of negative financial expectations. We
thereby assume that those who are pessimistic about their financial future are particularly hit hard by
the recent economic and financial crisis and that this translates into Euroskepticism differently
depending on the region where people live. There is increasing awareness that economic behavior in
the Great Recession cannot be fully explained with traditional models. Summers (2013) uses the
variable “financial panic,” while Mau et al. (2012) discuss the concept of socio-economic insecurity.
Here we use the variable “financial expectations” as the individual evaluation of the socio-economic
environment, testing whether this serves as a transmission mechanism for socio-economic variables
towards Euroskepticism. We find that negative financial expectations significantly increase
Euroskepticism in Western EU member states, but not in the newly accessed Eastern EU states.
The paper is structured as follows: In section 2, we discuss the related literature, general trends, and
our theoretical considerations. In section 3, we present and discuss the model. The data are introduced
in section 4 and the model is empirically investigated in section 5. Section 6 concludes and discusses
implications for EU integration policies.
2. Explaining Euroskepticism
In a first step, we present the main explanatory approaches for Euroskepticism2 and discuss
differences between post-communist and Western EU member states with regard to public support
for the EU. We also examine the implications of the recent economic and financial crisis as well as
redistributive concerns for Euroskeptic attitudes. In a second step, we provide the motivation of our
empirical strategy by discussing general trends and theoretical considerations.
2 Loveless and Rohrschneider (2011) provide an extensive and informative literature review with regard to Euroskepticism explanatory approaches on which this section is partly based.
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2.1 Previous Findings
Euroskepticism is typically defined as “negative attitudes towards the EU and/or European
integration” (Serricchio et al., 2013, p. 52). Literature on Euroskepticism distinguishes between three
main explanatory approaches for Euroskepticism: National identity, national institutional
performance, and utilitarian theory (Loveless and Rohrschneider, 2011).3 The national identity
approach explains resistance against European integration via feelings of an exclusive national identity
and the fear of losing one’s own cultural identity (Diez Medrano, 2010; Hooghe and Marks, 2005,
2007; Lubbers and Jaspers, 2011; McLaren, 2002, 2007; de Vreese and Boomgaarden, 2005). However,
there is also evidence that strong national identity can exist in accordance with public support for
European integration, which is known as inclusive national identity (Bruter, 2005).
The national institutional performance approach explains attitudes towards the EU through the
individual’s trust level towards national institutions. Thus, trust in national institutions is seen as a
proxy for trust in European institutions, because citizens are much more informed about national
politics than about the EU (Anderson, 1998). In contrast, Sanchez-Cuenca (2000) argues that low trust
in national political institutions can be substituted by high trust in EU institutions.
Finally, according to utilitarian theory, individual support for EU integration is positively associated
with individual economic benefits that could be gained from EU market liberalization. Sociotropic
utilitarianism regards national economic performance measured in GDP growth, inflation,
unemployment, intra-EU trade or country net benefits from EU membership as decisive in shaping
attitudes toward the EU (Anderson and Reichert, 1995; Anderson and Kaltenthaler, 1996; Eichenberg
and Dalton, 1993). Egocentric utilitarianism considers the individual socio-economic position
measured by age, education level, and occupation as pivotal for the decision, because these
characteristics are considered to be essential for being an economic winner or loser from EU
integration (Gabel, 1998a; 1998b; Gabel and Palmer, 1995; Gabel and Whitten, 1997). However, there
is evidence that indeed the self-characterization as an economic winner or loser determines
Euroskeptic attitudes, but that this self-characterization only partly overlaps with the individual socio-
economic position. Thus, in this respect, individual attitudes towards Europe are more based on an
assessment about how the EU affects someone personally (Mau, 2005).
3 We refrain here from the explanatory approach of social location that is related to post-materialism, cognitive mobilization or religion, because we do not consider it essential to our argumentation. For an overview, see Loveless and Rohrschneider (2011).
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Before the Maastricht Treaty, the EU was primarily considered a project of economic integration.
In this context, utilitarian theory was regarded as the dominant explanatory approach. With the Treaty,
the popularity of the EU was used to enforce economic discipline among member states (Rotte and
Zimmermann, 1998). After establishing it, the EU expanded its competences into non-economic
policy areas and then saw public support for EU integration decline, despite favorable economic
conditions (Franklin and Wlezien, 1997). At this point, the importance of national identity and national
institutional performance explanatory approaches increased in Western European countries (Loveless
and Rohrschneider, 2011).
Looking at post-communist EU member states, Loveless and Rohrschneider (2011) state that the
strongest determinants for positive attitudes towards EU integration in post-communist countries
before EU accession are attitudes towards democracy and capitalism as well as the belief that the EU
guarantees reforms (Kucia, 1999; Cichowski, 2000; Rohrschneider and Whitefield, 2004). After EU
accession, the importance of economic considerations increased. In accordance, Herzog and Tucker
(2010) find that economic winners of the transition process are less Euroskeptic than losers.
Comparing attitudes towards EU integration between East and West, de Vries (2013) argues that
individuals in Western EU member states are more ambivalent towards EU integration than Eastern
citizens. She explains that this difference stems from citizens in Western EU countries being more
experienced with regard to positive and negative consequences of EU integration over the years.
After the onset of the financial crisis in 2007, Euroskeptic attitudes have increased considerably
among the EU-27 countries. Following proxy mechanism theory by Anderson (1998) and mainly
denying the importance of utilitarian considerations, Armingeon and Ceka (2014) find that EU
attitudes are mainly derived from evaluating the national government. They conclude that if national
governments are successful in solving economic problems based on the crisis, the support for the EU
will increase again. In addition to this, Serricchio et al. (2013) consider exclusive national identity as
decisive for Euroskepticism. Levy and Phan (2014) take a more integrative point of view by stating
that the sociotropic assessment of the national economic situation drives EU attitudes, particularly
among those with an exclusive national identity. They conclude that if the economic crisis produces a
resurgence of nationalism, the national economic situation becomes even more important to assure
the project of European integration. Finally, Braun and Tausendpfund (2014) show that contrary to
the predominant opinion, utilitarian considerations again play an important role in explaining attitudes
towards the EU.
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Besides the explanatory approaches discussed above, Eichenberg and Dalton (2007) argue that
redistributive concerns are crucial for attitudes towards the EU. There is evidence that European
economic and political integration is one important driver of an increase in income inequality
(Beckfield, 2006; 2009) and that the increase in income inequality is negatively associated with public
support for EU integration (Burgoon, 2013; Kuhn et al., 2014). Recent findings furthermore indicate
that individuals with lower levels of education are particularly sensitive to income inequality with regard
to Euroskeptic attitudes (Kuhn et al., 2014) and that the positive impact of low education on
Euroskepticism has even increased in the last decades (Hakhverdian et al., 2013).
2.2 General Trends and Theoretical Considerations
Figure 1 shows that Euroskepticism has increased considerably from 2006 to 2011. Based on the
reviewed literature, we conclude that utilitarian evaluations are important for individual attitudes
towards the EU, particularly during the European economic and financial crisis (Braun and
Tausendpfund, 2014). However, we hypothesize that individual financial evaluations affect attitudes
towards Europe in post-communist and Western EU countries differently. We aim to contribute to
the literature by further shedding light on this issue by answering the following questions: How stable
are the trends from the past in explaining Euroskepticism? What is the relation between negative
financial expectations and Euroskepticism? And are there differences between Western and post-
communist EU member states? To our knowledge there is no article that analyzes differences in the
relation between utilitarian considerations and Euroskeptic attitudes in post-communist and Western
EU countries during the crisis period by using the transmission mechanism of negative financial
expectations. We aim to fill this research gap.
Figure 1: Development of Euroskepticism between 2006 and 2011
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First we look at the question of general trends with particular emphasis on income inequality.
Motivated by the finding that European integration divides European citizens into economic winners
and losers (Beckfield, 2006), one prominent explanatory approach for an increase in Euroskeptic
attitudes is the increase in income inequality (Atkinson, 2013; Burgoon, 2013; Kuhn et al., 2014; Ritzen
and Zimmermann, 2014). However, for the period of 2006 to 2011 (i.e. the period covering the
economic crisis), we do not observe a strong increase in income inequality in Western EU countries,
while post-communist countries even experienced a decrease in income inequality (see Figure 2). This
development is presumably related to the fact that within the crisis the employment rates declined
across the income distribution. This means that former middle and high wage earners who were
previously winners of European integration might have been negatively affected by the EU financial
and economic crisis. Thus it is not so surprising if income inequality is not related to, or even negatively
related to, Euroskepticism.
Figure 2: Development of income inequality (measured by Gini) between 2006 and 2011
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With regard to financial expectations, we assume that those who have negative financial
expectations are hit particularly hard by the crisis. This group not only comprises citizens who had
already been economically disadvantaged before the crisis, but presumably also includes former
economic winners of European integration. To generalize from this, below we introduce and explain
the variable negative financial expectations as a transmission mechanism in our analysis. Economic
sentiments are often seen as a transmission mechanism between real world variables and economic
decisions (e.g. Beckmann et al., 2011). Introducing negative financial expectations is a way to
incorporate the sentiment of economic uncertainty into the Euroskepticism explanation. Thereby,
financial expectations are hypothetically driven by economic circumstances, which are partly affected
by the crisis and translate into Euroskepticism. Figure 3 descriptively supports our approach by
showing that negative financial expectations strongly increase after the onset of the financial and
economic crisis in 2007.
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Figure 3: Development of negative financial expectations between 2006 and 2011
The Great Recession that started in 2007 implies a huge degree of economic uncertainty for EU
citizens. We assume that in this context individual financial future expectations, which are based on
individual and national economic circumstances, are important for citizens’ attitudes towards the EU.
This is because EU politics, in addition to national politics, have played an important role in managing
the financial and economic crisis (Serricchio et al., 2013).
We hypothesize that in Western EU member states citizens with negative financial expectations are
more likely to report Euroskeptic attitudes than those who believe that their economic situation will
not change and those who have positive financial future expectations. People with negative financial
expectations are mostly from countries that have been hit hardest by the crisis such as Greece and
Portugal. Under the pressure of EU policies, these countries have had to impose stability and reform
measures that have been highly unpopular. Such measures, sometimes summarized critically as
austerity can be perceived by some, at least for the short term, as a further threat to the financial future,
particularly for those already negatively affected by the crisis. This may result in an increase in
Euroskeptic attitudes (see Braun and Tausendpfund, 2014, but also Mau, 2005). However, we do not
deny that this relation is possibly also affected by the lack of trust in national and EU institutions
(Armingeon and Ceka, 2014; Serricchio et al., 2013).
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In contrast, we expect financially pessimistic individuals in post-communist countries to be much
more reluctant with regard to Euroskepticism than citizens from Western EU member states. We
neither want to deny that people in Eastern EU countries with lower education or lower occupation
statuses are more likely to have Euroskeptic attitudes than for instance highly educated managers
(Herzog and Tucker, 2010), nor do we claim that citizens from post-communist countries have not
also had to face hard austerity policies. However, we believe that in post-communist countries, Europe
is still linked to popular political and economic reforms (Cichowski, 2000; Herzog and Tucker, 2010;
Kucia, 1999; Rohrschneider and Whitefield, 2004) as well as to the experience of economic
convergence and growth based on liberalized markets and EU transfers (Gill and Raiser, 2012).
Furthermore, Eastern EU member state citizens have less experience with the disadvantages of EU
policies (de Vries, 2013). We therefore expect that in post-communist countries negative financial
expectations are much less related to Euroskeptism than in the Western EU, and that the EU is still
seen as a solution for rather than a source of economic problems. Figures 1 and 3 support our
argumentation since they clearly show that although post-communist countries have a higher share of
citizens with negative financial expectations than those in the West, they are less likely to express
Euroskeptic attitudes. We empirically test our hypothesis throughout the next sections.
3. Model
Our empirical investigation seeks to identify the impact of individual negative financial expectations
and a vector of micro- and macro-economic variables on Euroskepticism. The variable “negative
financial expectations” is a binary variable for either having or not having negative financial
expectations. Euroskepticism is also a binary variable for being or not being Euroskeptic. It is possible
that there are unobserved variables that make people both more likely to have negative financial
expectations and to be Euroskeptic. To account for the recursive structure of our approach and for
possible unobserved jointly exogenous variables, we estimate a recursive bivariate probit model (RBP).
Based on the theoretical considerations, we hypothesize that negative financial expectations have a
positive impact on Euroskepticism in Western EU countries and no (or a negative) effect in Eastern
EU member states. Furthermore, we assume that these expectations serve as the transmission
mechanism for the mood created by economic circumstances. The transmission process means that
both Euroskepticism and individual negative financial expectations are jointly determined by variables
within a recursive structure so that the error terms of Eq. 3.1 and 3.2 might be correlated:
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Euroskepticism (i, j) α0 +
α1 negative financial expectations (i, j) +
α2 national macro variables (j) +
α3 EU budget transfers(j) +
α4 socio-economic background (i, j) +
ε1 (3.1)
Negative
financial expectations (i,j) = β 0 +
β 2 national macro variables (j) +
β 3 EU budget transfers (j) +
β 4 socio-economic background (i, j) +
ε2 (3.2)
National variables are income inequality (measured by the Gini coefficient), unemployment, GDP
per capita and inflation (HICP). The EU budget transfers are net calculations and in relation to the
country’s gross national income (% GNI). We hypothesize that increases in income inequality lead to
an increase in Euroskepticism. Unemployment and inflation are indicators of income uncertainty and
are hypothesized to lead to increased Euroskepticism. An increase in GDP per capita is an indicator
of income gains and is expected to decrease Euroskepticism. Higher net transfers received from the
EU are expected to lead to more support for the EU and to less Euroskepticism.
There are likely to be future variables explaining negative financial expectations in equation (3.2),
but those are either not available or if available they would be highly endogenous. We prefer to
concentrate on predetermined variables explaining the future, and control for the unobserved
heterogeneity by modeling the correlation across both equations of the system. In that sense, equation
(3.2) “instruments” the endogenous variable negative financial expectations in equation (3.1), and
allows us to estimate the effects of the predetermined variables on both endogenous variables
separately.
Modeling the feedbacks from the effects of Euroskepticism to the sub-system of respondents’
attitudes admittedly would go beyond the purpose of this paper. However, there is the potential that
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the general rise in Euroskepticism across Europe can affect individual attitudes and reach variables
such as negative financial expectations and even have an effect on real world factors like
unemployment and growth.
Negative financial expectations will be determined by information about the future, which variables
included in the past do not cover. However, this lack of coverage is an advantage since this reduces
the degree of endogeneity the “instrumented” variable negative financial expectations exhibits. This
also holds for a potential effect of negative financial expectations on future macro variables, which
equation (3.2) does not cover. We also consider residual correlation to take into account unobserved
heterogeneity. Euroskepticism may well drive “financial expectations” because it may decrease the
possibility of debt mutualization in the Euro area. However, individuals know that those effects are
pretty slow due to the sluggish political process. Hence, effects might only be marginal in the short
term.
For the period before the economic and financial crisis and regressing the level of Euroskepticism
on the change of income inequality in the EU-12 countries, Kuhn et al. (2014) found a positive
relationship. This implies that a positive change of income inequality in one period resulted in a higher
level of Euroskepticism. Differently, we model the levels of Euroskepticism and negative financial
expectations as a function of inequality measured by the Gini coefficient. Both different specifications
have well-known different empirical implications. A one period increase in the Gini has only a
temporary effect on Euroskepticism in the Kuhn et al. (2014) specification, if the increase does not
repeat. However, in our specification, an increase in the level of inequality induces a permanent rise in
Euroskepticism even if the Gini remains fixed over the next periods. We find that our level
representation is more appropriate for our data, but further discuss and examine the empirical
differences in section 5.2.
Our structural estimation approach directly models the endogeneity generated by potentially
correlated error terms in the recursive equation system (3.1) and (3.2). The model also allows us to
decompose the impact of the exogenous regressors on Euroskepticism in a direct effect through
equation (3.1) and an indirect effect through equation (3.2). This also enables us to examine the
estimates’ consistency from the recursive model with those from a final form probit model directly
estimating the total effects, replacing negative financial expectations in equation (3.1) by equation (3.2)
and collecting the regressor terms.
To calculate unbiased joint estimates of the two processes, we estimate a RBP model that
simultaneously estimates the probability of being Euroskeptic conditional on the probability of having
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negative financial expectations. This bivariate probit model (Maddala, 1983, 122–123) is formulated as
follows:
y*1i = xi τ1 + y2iπ + µ1i y1i = 1, if y*1i > 0, 0 otherwise, (3.3)
y*2i = xi τ2 + µ2i y2i = 1, if y*2i > 0, 0 otherwise, (3.4)
where y*1i is the latent variable associated to the binary dependent variable Euroskepticism of
Equation 3.1; y*2i is the latent variable associated to the binary dependent variable negative financial
expectations of Equation 3.2, which is included in Equation 3.1 as an binary endogenous variable; xi
includes the two regression equations’ exogenous regressor vectors; and µ1i and µ2i are the error terms.
We assume that the error terms µ1i and µ2i are standard normally distributed (N (µ, σ2) = N (0, 1)) and
that covariance of the error terms equals Cov(µ1i, µ2i | x1i, x2i) = ϱ. If the error terms of the two equations
are uncorrelated, i.e., ϱ = 0, then both equations can be estimated separately. But, if the error terms
are correlated and ϱ ≠ 0, separately estimated results would be biased. Identification in the RBP model
as formulated above can rely alone on the functional form based on non-linearity (Green, 2003; Wilde,
2000).
The model is separately applied to the EU-27 as a whole, to Western (17) and to Eastern EU (10)
countries in order to trace whether the political and economic pathways in Western and former socialist
EU member states imply different decision making processes regarding Euroskepticism. Western EU
countries include Austria, Belgium, Cyprus, Denmark, Finland, France, Germany, Greece, Ireland,
Italy, Luxembourg, Malta, The Netherlands, Portugal, Spain, Sweden, and the United Kingdom. The
former socialist EU member states are Bulgaria, Czech Republic, Estonia, Hungary, Latvia, Lithuania,
Poland, Romania, Slovakia, and Slovenia. The Eastern EU countries are all recent members to the EU,
while most of the Western countries were already part of the EU in 2004 (except for Malta and Cyprus,
which joined in 2004). Eight Eastern European countries (the Czech
Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Slovakia, and Slovenia) accessed the EU in
2004, while Bulgaria and Romania joined in 2007.
4. Data
The analysis is based on a pooled cross-sectional dataset with detailed micro and macro data for the
27 EU member states for the period 2006 to 2011. Individual data includes attitudes towards one’s
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own country’s EU membership, individual financial expectations, as well as demographic and socio-
economic information provided by the cross-sectional Standard Eurobarometer (EB) survey. Eurostat
provides the country-specific macro indicators. These consist of the Gini coefficient as a measure for
income inequality, annual unemployment rate averages, gross domestic product (GDP) per capita, the
harmonized consumer price index (HICP) measuring inflation, and EU net transfers in relation to the
gross national income (% GNI).
Our key variables are Euroskepticism and Negative Financial Expectations. Euroskepticism is
measured by the EB question, “Generally speaking, do you think that your country's membership of
the European Union is …?” with answer categories: (1) a good thing, (2) a bad thing, (3) neither good
nor bad, or (4) don't know (DK). We create the binary variable Euroskepticism with response
categories (1) a bad thing and (0) a good thing or neither good nor bad. Category (4) responses are
treated as missing values. Financial expectations are inquired by the EB question, “What are your
expectations for the next twelve months: will the next twelve months be better, worse or the same,
when it comes to the financial situation in your household?” with the response options: (1) better, (2)
worse, (3) same, or (4) don't know (DK). We recode the binary variable Negative Financial Expectation
with categories (1) worse and (2) better or same. “Don’t know” answers are treated as missing values.
After merging the eight relevant EB waves (65.2, 67.2, 70.1, 71.1, 71.3, 72.4, 73.4 75.3), the dataset
originally consisted of 213,633 observations. Focusing on the economically active population reduces
the number of observations to 147,129. We dropped citizens who retired early and those older than
64 because they may have had a different process of Euroskepticism formation. Missing values for our
key variables Euroskepticism and Negative Financial Expectations account for a loss of 7,878
observations. Missing values in any other of the micro variables lead to a loss of a further 1,913
observations. Therefore, our analysis is based on 137,338 observations that consist of 85,881
observations for Western European countries and 51,457 for former socialist EU member states.
In Table 1, we present our sample’s descriptive statistics for the EU-27. This information is
separated for the two regions distinguished in the Appendix, Tables A1 and A2. The average age of
individuals in the Western countries sample is slightly older, they have more years of education, there
are more house persons, less unemployed, and less live in large towns. The occupation variable “house
persons” describes individuals who are responsible for the household and domestic tasks and who are
inactive in the labor market. The macro variables show, in particular, the sizeable difference in GDP
per capita (higher in the West) and in transfers from the EU (higher in the East).
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Table 1: Descriptive statistics, EU-27, 2006–2011 (N=137,338)
Mean SD Min MaxMicro Variables Euroskepticism 0.1386 0.3456 0 1Neg. Financial Expectations 0.2117 0.4085 0 1Male 0.4572 0.4982 0 1Age 39 13 15 64Education 15-, no full-time education 0.1156 0.3198 0 116-19 0.4559 0.4981 0 120+ 0.4285 0.4949 0 1Occupation Self-employed 0.0992 0.2989 0 1Managers 0.1470 0.3541 0 1Other white collars 0.1583 0.3650 0 1Manual workers 0.2869 0.4523 0 1House persons 0.0871 0.2819 0 1Unemployed 0.1047 0.3062 0 1Students 0.1168 0.3212 0 1Type of Community Rural area or village 0.3512 0.4773 0 1Small or middle-sized town 0.3571 0.4791 0 1Large town 0.2901 0.4538 0 1Macro Variables Gini coefficient (times 100) 29.7 4.0 22.7 39.2Unemployment 8.5 3.7 3.1 21.7GDP 22896 13281 3400 80300HICP 112.45 9.53 101.28 143.73EU Net Transfers (% GNI) 0.82 1.37 -.49 5.51
Source: European Commission (2012), European Commission Eurobarometers 67.2, 70.1, 71.1, 71.3, 72.4, 73.4, 75.3 (2008–2011), Eurostat Database (2013a, 2013b, 2013c, 2013d).
4.1 Euroskepticism
Euroskepticism in the EU increased from 12% in 2006 to 17% in 2011. In 2011, the Euroskeptics
still formed less than one fifth of the population. At the same time, the group has increased by almost
one third. The EU-wide figures mask substantial differences between countries. In 2011, the countries
with the most Euroskepticism were Greece (32%), Portugal (29%), Cyprus (27%) and the United
Kingdom (26%); those with the least included Bulgaria, Estonia, Poland, Slovakia, and Belgium (below
11%). Between 2006 and 2011, only Finland, Sweden and Estonia saw a slight decrease in
Euroskepticism (maximum decline of 3 percentage points). In contrast, between 2006 and 2011, many
countries showed a sharp increase (presented in percentage points): notably, Greece (20), Slovenia (16),
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Portugal (14), Spain (11), Hungary (11), Cyprus (9), Latvia (7), Italy, Lithuania, Luxembourg and the
United Kingdom (6). All of the countries that had applied for EU Emergency Support are among
those showing a sharp increase. More moderate increases are found in Denmark, France, Germany,
Ireland, Malta, The Netherlands, Poland, Romania, and Slovakia (maximum increase of 4 percentage
points).
4.2 Income Inequality
The increase in the Gini coefficient witnessed in the OECD (2008, 2011) for the period 1975–2005
did not take place in most EU countries in the period 2006–2011. The Gini (multiplied by 100)
increased in the following countries: Austria (by 1 Gini point), Bulgaria (3.8), Cyprus (0.4), Germany
(2.2), France (3.5), Malta (0.4), Romania (0.2), Slovenia (0.1), Spain (2.6), Sweden (0.4) and the United
Kingdom (0.5). The highest increase was in Denmark (4.1). The Gini decreased in: Belgium (by 1.5
Gini points), the Czech Republic (0.1), Estonia (1.2), Finland (0.1), Greece (0.8), Ireland (2.1), Italy
(0.2), Latvia (3.8), Lithuania (2), Luxembourg (0.6), the Netherlands (0.6), Poland (2.2), Portugal (3.5)
and Slovakia (2.4). The highest decrease, 6.5 Gini points, was observed in Hungary.
4.3 Financial Expectations
The share of people in the EU-27 who believe that their personal financial situation will worsen
increased from 17% in 2006 to 27% in 2008 and then decreased again to 19% in 2011. In 2011, negative
financial expectations were highest in Greece (54%), Portugal (42%), Hungary (32%) and Romania
(31%). Countries with the lowest share of pessimistic citizens in 2011 comprise the Scandinavian
countries and Luxembourg, with percentages below 9%. On average, around 20% of Europeans in the
sample express gloomy financial prospects. The share of citizens with pessimistic financial expectations
in the pooled dataset is slightly higher in Eastern Europe (25%) than in Western EU member states
(19%) over the six-year period between 2006 and 2011.
4.4 Increasing Unemployment
Many (more than expected) EU-27 countries managed a decrease in unemployment between 2006
and 2011: Austria (from 4.8% in 2006 to 4.2% in 2011), Belgium (8.3% to 7.2%), the Czech Republic
(7.1% to 6.7%), Germany (10.3% to 5.9%), Malta (6.9% to 6.5%) and Poland (13.9% to 9.7%). Yet
the crisis hit employment hard in many other countries. The highest unemployment rate and also the
17
highest increase of unemployment was found in Spain (8.5% in 2006 to 21.7% in 2011), followed by
Greece (8.9% to 17.7%), Latvia (6.8% to 16.2%) and Lithuania (5.2% to 15.4%).
4.5 GDP per Capita
GDP per capita rose in almost all of the EU-27 countries in the period 2006–2011, with a relatively
fast and steady growth in the Eastern European countries. There was a decline only in Ireland and the
United Kingdom, while the Southern and North-Western European countries remained more or less
at a standstill. In 2011, the highest GDP per capita was observed in Luxembourg (€80,300), followed
by Denmark (€43,200), while the lowest was in Bulgaria (€5,200) and Romania (€6,100).
5. Results
5.1 Euroskepticism Explained by Financial Expectations
In Table 2, we present the marginal effects of the RBP regressions for the overall sample and
separately for Western and former communist Eastern EU member states. We control for country and
year fixed effects to account for country and time specific factors. We also calculate robust and country
clustered standard errors to account for heteroskedasticity and correlated error terms within countries.
The country and time dummies remove a large amount of the variance, allowing us to concentrate on
the factors that are related to the effects we want to study. We also investigate the robustness of our
findings using alternative estimation techniques in section 5.2.
We hypothesize that financial expectations act as a transmitter of socio-economic circumstances
towards Euroskepticism. The results support our approach by showing that negative financial
expectations have a highly significant positive effect on Euroskepticism in the Western EU countries,
but a non-significant negative effect on Euroskepticism in the post-communist countries. This result
suggests that there are different mechanisms leading to Euroskepticism in the two regions.
Euroskepticism has always been higher in the West than in the East (see Figure 1), and the reverse has
been true for negative financial expectations (see Figure 3). But while citizens in the West were also
blaming Brussels for the threat of an increased financial burden, members in the East were not
expecting additional burden from the EU, but perhaps support due to the economic situation in those
countries.
The last row of Table 2 (Wald test) shows considerable variation in the correlations of the
disturbances of the equations 3.1 and 3.2 between the two regions. For each region the first number
18
shows the estimated correlation of the disturbances of Equations 3.1 and 3.2 while the second number
presents the significance level of the likelihood-ratio test (which tests the null-hypotheses of
uncorrelated error terms of ϱ=0). In Western EU countries we find a non-significant negative
correlation of -.1627 (p < 0.2136). Therefore, Euroskepticism and negative financial expectations may
not be jointly determined in Western EU countries and two separate probit regression models should
give similar results. However, in Eastern EU countries the disturbances of Equations 3.1 and 3.2 are
borderline significantly positively correlated (p < 0.1372). The fact that the estimated correlation of
the disturbances is with 0.6615 substantially different from zero suggests that it is important to control
for endogeneity in Eastern EU countries. The estimation of a RBP model is therefore appropriate to
get unbiased estimates. The result further supports our expectation that Euroskepticism in the two
regions follow different decision making processes.
First, we inspect the macro explanatory variables. Contrary to our expectations and to all previous
studies, income inequality measured by the Gini coefficient has a statistically negative impact on
Euroskepticism and on negative financial expectations in Western EU countries. An increase in income
inequality by one Gini point decreases the probability of being Euroskeptic by 0.5 percentage points
(pp) and of having negative financial expectations by 1.7 pp. This finding shows that the results
obtained by Kuhn et al. (2014) for the EU-12 for the period 1976-2008 no longer hold for income
inequality in Western Europe.
Income inequality has no significant effect in post-communist countries on Euroskepticism or
negative financial expectations. The unemployment rate boosts negative financial expectations only in
Western European countries, not in Eastern Europe. A one percentage point increase in
unemployment increases the probability of having negative financial expectations by 1.2 pp in Western
Europe. Inflation slightly increases negative financial expectations in both Western and Eastern EU
countries, but has no significant association with Euroskepticism in either region. GDP per capita is
an important determinant of Euroskepticism and negative financial expectations in Eastern Europe,
but has no effect in Western EU countries. An increase in GDP per capita of one percent decreases
the probability of being Euroskepticism by 0.25 pp and of having negative financial expectations by
almost 0.40 pp in former socialist EU countries. Finally, EU net transfers are negatively associated
with Euroskepticism in Eastern EU countries. Yet in Western EU countries, which on average are net
payers of EU transfers, EU net transfers have no significant association to Euroskepticism.
Looking at the micro explanatory variables we find that in particular low education and low
occupation status are both positively associated with Euroskepticism and negative financial
19
expectations both for Western as for Eastern EU countries. The results for the occupation dummies
provide similar evidence. For example, in comparison to the reference group of managers, being
unemployed increases the probability of being Euroskeptic by 9.5 pp in Western EU countries and by
8.8 pp in former socialist countries. Furthermore, being unemployed boosts the probability of having
negative financial expectations by 8.2 pp in Western EU countries and by 13 pp in Eastern EU
countries.
The data set has a hierarchical structure, where respondents are cross-nested in countries as well as
years. We have just dealt with this by including dummies and by calculating country clustered and
robust standard errors to permit heteroskedasticity and within-cluster error correlation. However,
when the number of clusters is small (say 5 - 30) as in our samples, the standard asymptotic tests may
over-reject resulting in too large standard errors for the variables at the cluster level. As Cameron et al.
(2008) found in Monte Carlo estimations, bootstrap-based procedures can improve inference and lead
to much lower rejection rates than standard methods. Table A3 provides a robustness check comparing
for key variables the original standard error with those using the robust, cluster and bootstrap methods.
As expected, clustered and robust standard errors are much larger than without, and the bootstrap
standard errors are all close to the robust standard errors. Hence, the cluster-based evidence used in
Table 2 is conservative, but using the less strict bootstrap standard errors would not lead to different
conclusions in our case.
The hierarchical structure of our data could be also fruitfully analyzed by a multilevel analysis
(MLM, see Gelman and Hill, 2009, for instance). One reason why we do not present such analysis here
is that we are not concerned with the additional information multilevel analysis may provide. Also
clustering and multilevel analysis are basically equivalent methods as has been shown in Monte Carlo
simulations by Harden (2009). However, as has been demonstrated recently in Monte Carlo studies by
Stegmueller (2013) and Bryan and Jenkins (2015), estimates of parameters and standard errors for the
cluster-level variables can be seriously biased when the cluster size is small (e.g. below 30), while the
individual-level effects are reliable. Therefore, MLM is no panacea.
20
Table 2: Recursive Bivariate Probit Regressions, 2006–2011, Euroskepticism (EUS) and Negative Financial Expectations (NFE)
EU-27
Western EU
Former Socialist EU
EUS
NFE EUS NFE EUS
NFE
Neg. Financial Expectations (d)
0.111** (0.053)
0.193**
(0.077) -0.113
(0.097) Gini coefficient (times 100)
-0.003* (0.002)
-0.012***
(0.003) -0.005**
(0.002) -0.017***
(0.005) -0.001
(0.003) -0.004
(0.005) Unemployment rate
0.002 (0.001)
0.008**
(0.003) 0.003
(0.003) 0.012**
(0.006) 0.002
(0.002) 0.006
(0.004) HICP 0.001
(0.001) 0.006***
(0.002) 0.000
(0.002) 0.009*
(0.005) 0.001
(0.002) 0.005*
(0.003) Log(GDP) -0.047**
(0.023) -0.210*
(0.122) -0.055
(0.056) -0.089
(0.204) -0.253**
(0.122) -0.396**
(0.171) EU Net Trans-fers (% GNI)
-0.004 (0.006)
-0.017(0.012)
0.002(0.009)
-0.016(0.011)
-0.015* (0.008)
-0.024(0.017)
Education, reference group: 20+ 15-, no full-time education (d)
0.076*** (0.009)
0.075*** (0.007)
0.090*** (0.008)
0.067*** (0.008)
0.067** (0.029)
0.108*** (0.013)
16-19 (d) 0.036*** (0.005)
0.030***
(0.004) 0.050***
(0.006) 0.028***
(0.005) 0.024***
(0.008) 0.032***
(0.004) Male (d) 0.008**
(0.003) -0.014***
(0.004) 0.007
(0.005) -0.011**
(0.005) 0.006*
(0.004) -0.019***
(0.007) Age 0.001***
(0.000) 0.002***
(0.000) 0.001**
(0.000) 0.002***
(0.000) 0.002**
(0.001) 0.003***
(0.000) Type of Community, reference group: City Small, middle- sized town (d)
-0.002 (0.005)
-0.002(0.004)
-0.005(0.008)
-0.003(0.006)
0.001 (0.006)
0.001(0.006)
Large town (d) -0.008 (0.008)
-0.007(0.006)
-0.016(0.012)
-0.002(0.008)
-0.003 (0.010)
-0.012(0.012)
Occupation, reference group: Managers Self-employed (d) 0.033***
(0.006) -0.001
(0.007) 0.039***
(0.009) -0.001
(0.008) 0.020**
(0.009) -0.003
(0.013) Other white collars (d)
0.031*** (0.006)
0.014**
(0.006) 0.037***
(0.008) 0.010
(0.006) 0.024***
(0.007) 0.021*
(0.012) Manual workers (d)
0.056*** (0.007)
0.032***
(0.008) 0.072***
(0.010) 0.021**
(0.008) 0.044***
(0.013) 0.051***
(0.015) Household care takers (d)
0.058*** (0.008)
0.025**
(0.011) 0.068***
(0.012) 0.018*
(0.010) 0.054***
(0.016) 0.057**
(0.025) Unemployed (d) 0.077***
(0.007) 0.103***
(0.013) 0.095***
(0.009) 0.082***
(0.012) 0.088***
(0.030) 0.130***
(0.027) Students (d) 0.024***
(0.008) -0.007
(0.007) 0.023***
(0.008) -0.007
(0.008) 0.017
(0.012) -0.003
(0.011) Observations 137,338 85,881 51,457 Wald test of ϱ=0 -.02502 0.8228 -.1627 0.2136 .6615 0.1372
Source: European Commission (2012), European Commission: Eurobarometers 67.2, 70.1, 71.1, 71.3, 72.4, 73.4, 75.3 (2008-2011), Eurostat Database (2013a, 2013b, 2013c, 2013d).
Note: We control for country and year fixed effects and calculate country robust clustered standard errors. We present marginal effects and standard errors in parentheses. Significance levels are * p < 0.10, ** p < 0.05, *** p < 0.01. The symbol (d) stands for discrete change of dummy variable from 0 to 1.
21
5.2 Reduced Form Estimates
To test our results based on the RBP model, we estimate the reduced form for Euroskepticism and
negative financial expectations based on separate probit models. The results of the probit regressions
for the EU-27 as well as Western and Eastern EU member states are presented in Table 3. The probit
regression results for the dependent variable Negative Financial Expectations vary only marginally
from the RBP estimates. However, in comparison to the RBP results, the marginal effects of the
explanatory variables on Euroskepticism alter their effect size in the probit regression, as expected,
because they also take over the effect of the negative financial expectations, namely the factors driving
them. Hence, these estimates are the explanatory variables’ total effects, while Table 2 had decomposed
those into direct and indirect effects as discussed in section 2.
We can regard the comparison between the first, third and fifth column of the probit analysis (Table
3) without negative financial expectations and the recursive RBP model’s results in Table 2 as
confirmation of the transmission mechanism of financial expectations for the effect of financial
expectations towards Euroskepticism. For instance, when we financial expectations, the size and
significance of the Gini estimated effects on Euroskepticism for the total sample and the West in the
regressions without financial expectations (Table 3) decreases in the RBP model (Table 2). For the
post-communist EU, there is no effect in the reduced form (see Table 3, fifth column), and it remains
insignificant in the system estimate (see Table 2, fifth column). Another example is unemployment,
which is very significant in the Western EU in determining Euroskepticism in the reduced form (see
Table 3, third column), but is statistically insignificant in the system estimate (see Table 2, third
column). The fourth column in Table 2 reveals that the effect operates entirely through negative
financial expectations, which unemployment significantly affects.
Again we first look at the macro explanatory variables. Without taking the possible endogenous
negative financial expectations into account, the statistically significant negative relation between the
Gini coefficient and Euroskepticism is even increasing in Western EU countries. An increase in income
inequality by one Gini point decreases the probability of being Euroskeptic by 0.8 pp (0.5 pp in RBP).
In former socialist EU member states, we still find no significant effect. An increase of one percent in
the unemployment rate now significantly boosts Euroskepticism by 0.5 pp in Western EU countries
(insignificant in RBP) but has no effect in Eastern EU countries. Inflation still has no significant effect
on Euroskepticism in either region. In Eastern Europe, GDP per capita is still significantly associated
with Euroskepticism. However, the effect size is decreasing. An increase in GDP per capita of one
percent decreases the probability of being Euroskeptic by 0.16 pp (0.25 pp in RBP). The same is found
22
for EU net transfers, which are negatively associated with Euroskepticism in Eastern EU countries. A
one percentage point increase of EU net transfers (% GNI) decreases the probability of being
Euroskeptic by 1 pp (1.5 pp in RBP).
Looking at the micro explanatory variables, we confirm the results that, in particular, low education
and low occupation status are positively associated with Euroskepticism. However, we again find that
neglecting negative financial expectations leads to a larger effect size in Western EU countries and a
smaller effect size in Eastern EU countries. The results of the education dummies show that in
comparison to the reference group that has obtained at least 20 years of education, having less than 16
years of education increases the probability of being Euroskeptic by 10.5 pp (9 pp in RBP) in Western
EU countries and by 4.4 pp (6.7 pp in RBP) in Eastern EU countries. The results for the occupation
dummies provide similar evidence. In comparison to the reference group of managers, being
unemployed increases the probability of being Euroskeptic by 11.2 pp (9.5 pp in RBP) in Western EU
countries and by 5.9 pp (8.8 pp in RBP) in former socialist countries.
Table 3: Probit Regressions, 2006–2011, Euroskepticism (EUS) and Negative Financial Expectations (NFE)
EU-27
Western EU
Former Socialist EU
EUS
NFE EUS NFE
EUS NFE
Gini coefficient (times 100)
-0.005** (0.002)
-0.012***
(0.003)-0.008***
(0.002)-0.017***
(0.005)-0.001
(0.002) -0.005
(0.005)Unemployment rate
0.003** (0.001)
0.008** (0.003)
0.005** (0.002)
0.012** (0.006)
0.001 (0.001)
0.006 (0.004)
HICP 0.001 (0.001)
0.006***
(0.002) 0.002
(0.003) 0.009*
(0.005) -0.000
(0.001) 0.005*
(0.003) Log(GDP) -0.063***
(0.024) -0.210*
(0.122)-0.056
(0.040)-0.090
(0.204)-0.168**
(0.085) -0.399**
(0.170)EU Net Trans-fers (% GNI)
-0.007 (0.006)
-0.017(0.012)
-0.001(0.008)
-0.016(0.011)
-0.010*** (0.004)
-0.024(0.017)
Education, reference group: 20+ 15-, no full-time education (d)
0.086*** (0.008)
0.075***
(0.007) 0.105***
(0.007) 0.067***
(0.008) 0.044***
(0.014) 0.108***
(0.013) 16-19 (d) 0.039***
(0.005) 0.030***
(0.004) 0.055***
(0.006) 0.028***
(0.005) 0.018***
(0.004) 0.032***
(0.003) Male (d) 0.007*
(0.003) -0.014***
(0.004) 0.006
(0.006) -0.011**
(0.005) 0.009***
(0.002) -0.019***
(0.007) Age 0.001***
(0.000) 0.002***
(0.000) 0.001***
(0.000) 0.002***
(0.000) 0.001***
(0.000) 0.003***
(0.000) Type of Community, reference group: CitySmall or middle-sized town (d)
-0.002 (0.005)
-0.002(0.004)
-0.005(0.008)
-0.003(0.005)
0.001 (0.005)
0.001(0.006)
Large town (d) -0.009 -0.007 -0.016 -0.002 -0.001 -0.013
23
(0.008) (0.006) (0.012) (0.007) (0.010) (0.012)Occupation, reference group: Managers Self-employed (d) 0.033***
(0.007) -0.001
(0.007) 0.039***
(0.009) -0.000
(0.008) 0.020***
(0.007) -0.003
(0.013) Other white collars (d)
0.032*** (0.006)
0.014**
(0.006) 0.039***
(0.008) 0.010
(0.006) 0.019***
(0.004) 0.021*
(0.012) Manual workers (d)
0.059*** (0.007)
0.032***
(0.008) 0.076***
(0.010) 0.021**
(0.008) 0.033***
(0.006) 0.051***
(0.015) House persons (d) 0.061***
(0.009) 0.025**
(0.011) 0.072***
(0.013) 0.018*
(0.010) 0.041***
(0.007) 0.057**
(0.025) Unemployed (d) 0.090***
(0.008) 0.103***
(0.013) 0.112***
(0.011) 0.082***
(0.012) 0.059***
(0.009) 0.130***
(0.027) Students (d) 0.024***
(0.008) -0.007
(0.007) 0.023***
(0.008) -0.006
(0.008) 0.017
(0.011) -0.003
(0.011) Observations 137338 137338 85881 85881 51457 51457Pseudo R2 0.0590 0.0711 0.0577 0.0715 0.0505 0.0678
Source: European Commission (2012), European Commission: Eurobarometers 67.2, 70.1, 71.1, 71.3, 72.4, 73.4, 75.3 (2008-2011), Eurostat Database (2013a, 2013b, 2013c, 2013d).
Note: We control for country and year fixed effects and calculate robust country clustered standard errors. We present marginal effects and standard errors in parentheses. Significance levels are * p < 0.10, ** p < 0.05, *** p < 0.01. The symbol (d) stands for discrete change of dummy variable from 0 to 1.
5.3 Further Robustness Checks
To test the robustness of our results, we follow Kuhn et al. (2014) and re-estimate the RBP model by
using changes in macroeconomic variables. The procedure shows that Gini changes are not
significantly related to Euroskepticism in Western EU countries, and slightly negatively in post-
communist countries. Furthermore, removing the year dummies from the initial RBP model yields
similar results as presented in Table 2, see Appendix Table A4. These findings confirm that there is no
relationship between an increase in the Gini coefficient and Euroskepticism in our sample. The results
deviating greatly from former findings may be because the Gini coefficient in most of the countries
did not vary as much, or even decreased, in the observation period compared to before the Great
Recession.
Second, we check robustness by including those who retired early and those who are older than 64
into the working sample. This changes the size of the working sample from about 140,000 to about
200,000 observations. However, Table 4 shows that our RBP estimation results are largely robust.
24
Table 4: Robustness Check: Recursive Bivariate Probit, 2006–2011, including retired people, Euroskepticism (EUS) and Negative Financial Expectations (NFE)
EU-27
Western EU
Former Socialist EU
EUS
NFE EUS NFE
EUS NFE
Neg. Financial Expectations (d)
0.139*** (0.050)
0.204***
(0.070) 0.010
(0.235) Gini coefficient (times 100)
-0.003** (0.001)
-0.010***
(0.003) -0.005**
(0.002) -0.013***
(0.005) -0.001
(0.002) -0.005
(0.005) Unemployment rate
0.002 (0.001)
0.008**
(0.003) 0.003
(0.003) 0.012**
(0.006) 0.001
(0.002) 0.006
(0.004) HICP 0.001
(0.001) 0.006***
(0.002) -0.001
(0.003) 0.008*
(0.004) -0.000
(0.002) 0.004*
(0.003) Log(GDP) -0.062***
(0.020) -0.196
(0.122) -0.114**
(0.055) -0.048
(0.186) -0.183
(0.115) -0.498***
(0.166) EU Net Transfer -0.004
(0.006) -0.014
(0.011) 0.001
(0.010) -0.013
(0.011) -0.012*
(0.007) -0.026
(0.016) Education, reference: 20+15-, no full-time education (d)
0.096*** (0.008)
0.066***
(0.008) 0.117***
(0.007) 0.057***
(0.009) 0.062*
(0.032) 0.093***
(0.015) 16-19 (d) 0.046***
(0.005) 0.034***
(0.005) 0.063***
(0.006) 0.027***
(0.006) 0.025**
(0.012) 0.045***
(0.007) Male (d) 0.002
(0.004) -0.014***
(0.003)-0.003
(0.006)-0.011**
(0.005)0.008**
(0.003) -0.012***
(0.005)Age 0.001***
(0.000) 0.001***
(0.000) 0.001*
(0.000) 0.001**
(0.000) 0.001
(0.001) 0.002***
(0.000) Observations 198233 198233 123210 123210 75023 75023Wald test of ϱ=0 -.0631 0.5140 -.1621 0.1478 .2496 0.7418
Source: European Commission (2012), European Commission: Eurobarometers 67.2, 70.1, 71.1, 71.3, 72.4, 73.4, 75.3 (2008-2011), Eurostat Database (2013a, 2013b, 2013c, 2013d).
Note: We control for country and year fixed effects and calculate robust country clustered standard errors. We present marginal effects and standard errors in parentheses. Significance levels are * p < 0.10, ** p < 0.05, *** p < 0.01.
The symbol (d) stands for discrete change of dummy variable from 0 to 1.
Finally, we estimate our RBP model for the time periods before and within the economic crisis
separately. We consider the years 2006 to 2008 as the period before the economic crisis and the years
2009 to 2011 as the period within the economic crisis. Our results are presented in Tables 5 and 6.
Looking at Western EU countries, the robustness check shows that having negative financial
expectations increases the probability of being Euroskeptic by 15.6 pp before the economic crisis,
while it is 19.5 pp during the crisis. In Eastern EU member states, the non-significant negative effect
of having negative financial expectations on Euroskepticism in the pre-crisis period becomes
significantly negative in the within-crisis period. Table 6 indicates that during the crisis, having negative
25
financial expectations decreases the probability of being Euroskeptic by 14 pp in former socialist
countries.
To test whether the difference between the coefficients for negative financial expectations before
and within the crisis is statistically significant, we estimate the RBP model for the period 2006 to 2011
by including an interaction term between a crisis dummy and negative financial expectations. The
coefficient for the interaction terms for both Western and former socialist countries reveals that the
effect of negative financial expectations before the economic crisis (2006–2008) is significantly
different from the effect of negative financial expectations within the economic crisis (2009–2011).
This result further supports our argument that financial expectations act as a transmitter of socio-
economic circumstances towards Euroskepticism. The result suggests that there are different
mechanisms leading to Euroskepticism in the two regions and that financially pessimistic people in
Western Europe might interpret European integration as a threat to their financial situation, while
Eastern European people might view it as a chance to improve their economic situation, particularly
in times of economic decline. People in Northern European EU member states may be worried by
high financial transfers to the European Union, while Southern EU countries might fear austerity
measures. In contrast, Eastern EU countries may appreciate European integration due to positive net
transfers or improved employment opportunities based on an integrated European labor market.
However, the robustness check further shows that all other results stay mostly robust.
Table 5: Robustness Check: Recursive Bivariate Probit, 2006–2008, Euroskepticism (EUS) and Negative Financial Expectations (NFE)
EU-27
Western EU
Former Socialist EU
EUS NFE EUS NFE
EUS NFE
Neg. Financial Expectations (d)
0.128** (0.065)
0.156*
(0.091) 0.075
(0.124) Gini coefficient (times 100)
-0.002 (0.002)
-0.013***
(0.004) -0.001
(0.004) -0.005
(0.004) -0.002
(0.002) -0.011***
(0.004) Unemployment rate
0.006** (0.003)
0.009(0.006)
0.008(0.005)
0.013(0.008)
0.005 (0.005)
0.021**
(0.010) HICP 0.002
(0.002) 0.008***
(0.002) 0.006
(0.007) 0.006
(0.010) 0.000
(0.002) 0.004*
(0.002) Log(GDP) -0.017
(0.058) -0.323***
(0.122) -0.089
(0.096) -0.571***
(0.149) 0.005
(0.148) -0.468***
(0.156) EU Net Transfer -0.006
(0.013) -0.010
(0.020) 0.009
(0.013) -0.018
(0.015) -0.021
(0.018) -0.014
(0.048) Education, reference: 20+
26
15-, no full-time education (d)
0.071*** (0.010)
0.069***
(0.008) 0.090***
(0.011) 0.070***
(0.010) 0.030*
(0.017) 0.084***
(0.016) 16-19 (d) 0.030***
(0.006) 0.027***
(0.005) 0.044***
(0.008) 0.031***
(0.008) 0.012***
(0.005) 0.018***
(0.007) Male (d) 0.009**
(0.004) -0.016***
(0.004) 0.006
(0.006) -0.014**
(0.006) 0.011***
(0.004) -0.019***
(0.007) Age 0.001***
(0.000) 0.003***
(0.000) 0.001**
(0.000) 0.002***
(0.000) 0.001*
(0.000) 0.003***
(0.000) Observations 51663 51663 32503 32503 19160 19160Wald test of ϱ=0 -.0646 0.6254 -.1171 0.4758 .0721 0.8318
Source: European Commission (2012), European Commission: Eurobarometers 67.2, 70.1, 71.1, 71.3, 72.4, 73.4, 75.3 (2008-2011), Eurostat Database (2013a, 2013b, 2013c, 2013d). Note: We control for country and year fixed effects and calculate robust country clustered standard errors. We present marginal effects and standard errors in parentheses. Significance levels are * p < 0.10, ** p < 0.05, *** p < 0.01.
The symbol (d) stands for discrete change of dummy variable from 0 to 1.
Table 6: Robustness Check: Recursive Bivariate Probit, 2009–2011, Euroskepticism (EUS) and Negative Financial Expectations (NFE)
EU-27
Western EU
Former Socialist EU
EUS
NFE EUS NFE
EUS NFE
Neg. Financial Expectations (d)
0.041 (0.060)
0.195**
(0.096) -0.140**
(0.068) Gini coefficient (times 100)
-0.001 (0.003)
-0.011(0.010)
-0.004(0.004)
-0.023***
(0.006)0.008
(0.009) 0.014
(0.015)Unemployment rate
0.004 (0.003)
-0.003(0.008)
0.004(0.005)
0.013**
(0.006) -0.001
(0.006) -0.015
(0.014) HICP -0.001
(0.002) 0.012**
(0.006) -0.004
(0.003) 0.017***
(0.005) 0.001
(0.005) 0.011
(0.009) Log(GDP) -0.164*
(0.085) -0.905**
(0.382)-0.173**
(0.087)-0.134
(0.242)-0.209
(0.234) -0.444
(0.623)EU Net Transfer -0.010
(0.009) -0.009
(0.023) -0.003
(0.016) 0.023
(0.024) -0.009
(0.010) -0.000
(0.030) Education, reference: 20+15-, no full-time education (d)
0.085*** (0.010)
0.076***
(0.008) 0.092***
(0.008) 0.065***
(0.009) 0.079***
(0.026) 0.119***
(0.015) 16-19 (d) 0.040***
(0.005) 0.032***
(0.004) 0.053***
(0.006) 0.025***
(0.006) 0.030***
(0.006) 0.042***
(0.005) Male (d) 0.007*
(0.004) -0.014***
(0.005) 0.007
(0.006) -0.010*
(0.005) 0.004
(0.004) -0.019**
(0.008) Age 0.001***
(0.000) 0.002***
(0.000) 0.001*
(0.000) 0.002***
(0.000) 0.002***
(0.000) 0.003***
(0.000) Observations 85675 85675 53378 53378 32297 32297 Wald test of ϱ=0 .1370 0.3654 -.1530 0.3534 .6975 0.0184
Source: European Commission (2012), European Commission: Eurobarometers 67.2, 70.1, 71.1, 71.3, 72.4, 73.4, 75.3 (2008-2011), Eurostat Database (2013a, 2013b, 2013c, 2013d).
Note: We control for country and year fixed effects and calculate robust country clustered standard errors. We present marginal effects and standard errors in parentheses. Significance levels are * p < 0.10, ** p < 0.05, *** p < 0.01.
The symbol (d) stands for discrete change of dummy variable from 0 to 1.
27
6. Discussions and Conclusions
Our study analyzes Euroskepticism formation within the period of 2006 to 2011 by emphasizing
differences between Western and Eastern EU member states and by using the “negative financial
expectations” transmission mechanism, thus covering those who have been hit particularly hard by the
crisis. We provide evidence that having negative financial expectations determines Euroskeptic
attitudes differently in Eastern and Western EU countries. In Western EU countries, we find a positive
relation between negative financial expectations and Euroskepticism, while there is no significant
relation in post-communist countries. This result suggests that in the period around the recent
economic and financial crisis, Western EU citizens who are negatively affected by the crisis interpret
European integration as a threat because they likely fear that austerity policies imposed by the EU
further worsen their financial situation. In contrast, people from Eastern EU countries who are hit
hard by the crisis are much more reluctant to adopt Euroskeptic attitudes because they still consider
Europe as a source of solutions for economic problems. Thus, in post-communist countries, Europe
is still much more connected to popular political and economic reforms as well as a source of economic
convergence and growth based on liberalized markets and EU transfers. Eastern EU member state
citizens have less experience with the disadvantages of EU policies, further contributing to higher
levels of public support for the EU compared to those from Western EU countries.
With regard to sociotropic utilitarian evaluations, economic variables have explained
Euroskepticism well, mainly for the “old EU” countries and in the pre-crisis period. For instance,
Kuhn et al. (2014) study the EU-12 countries from 1976 to 2009 and show that Euroskepticism
increased in a statistically significant manner, with more income inequality and higher unemployment
but not with greater inflation. In this paper, we find that Euroskepticism formation has changed with
regard to income inequality in the enlarged European Union, in both the old EU as well as in the new
Eastern EU member states. Compared to Kuhn et al. (2014), we find a change in the sign of income
inequality’s impact on Euroskepticism in Western Europe during the period of 2006 to 2011. In post-
communist countries, income inequality has no significant relation to Euroskepticism. A possible
explanation for this result is that inequality has on average only slightly increased in this period across
the Western part of the EU but was, on average, falling in the Eastern part. The strong rise in
Euroskepticism over this period does not match the more modest development of inequality. In spite
of Piketty’s (2014) book, which has recently stirred a broad interest in inequality, our study does not
show inequality as the direct driver of Euroskepticism. However, this is not Piketty’s line, nor does it
28
rule out that inequality may moderate growth and hence contribute to weaker growth and rising
unemployment, therefore affecting financial expectations and Euroskepticism.
We further find for Western EU countries that unemployment feeds negative financial expectations
and affects Euroskepticism only indirectly. This result further supports our argumentation because it
shows that the individual perspective of financial uncertainty arising out of increasing unemployment
is more relevant for Euroskepticism than the unemployment rate per se. GDP per capita and transfers
to other EU countries are of no concern. In post-communist EU countries, profiting from EU
transfers and the rise in per capita GDP directly reduce Euroskepticism, reflecting positive perceptions
of the EU’s ability to solve economic problems.
With regard to egocentric utilitarian considerations, the previous literature consistently shows that
a disadvantaged socio-economic position is positively related to Euroskepticism. Looking at egocentric
utilitarian variables, our results confirm these findings. More education is associated with a lower
probability of having both negative financial expectations and Euroskeptic attitudes; contrarily, being
unemployed is related to a higher probability of having negative financial expectations and of being
Euroskeptic. However, the results show that unemployed people in the East are more likely to have
negative financial expectations, but are less likely to be Euroskeptic than those from the West.
What are our predictions for the years after 2011? Member states are slowly emerging from the
crisis with positive GDP growth rates in 2014 for all EU countries except Cyprus, Italy, and Finland.
In Italy negative growth at least slowed down between 2012 and 2014 (Eurostat Database, 2015). This
development is likely to improve economic sentiments and moderate Euroskepticism, particularly in
Southern EU member states (for initial descriptive evidence, see European Commission, 2015a or Pew
Research Center, 2015). However, in line with our argumentation, as the economic situation in Cyprus
still remains difficult, 42 percent of respondents have a negative image of the EU, which is the highest
share of citizens with this attitude among all EU member states in 2015 (European Commission, 2015a,
p. 9). In Finland, a Euroskeptic political party came into office in 2015, perhaps advantaged by the
persistent negative GDP growth.
Recently, EU membership popularity seems to have also increased as the conflict with Russia makes
the EU more of a “safe haven,” particularly for some Eastern European countries. However, concerns
about financial assistance to crisis countries may affect Eastern public support for the EU. For
instance, facing growing anti-European sentiments, the Slovak government collapsed in 2011 because
of its contributions to the European Financial Stability Facility (EFSF). In addition, the refugee crisis
and its subsequent discussions regarding distributing asylum-seekers across EU countries, as well as
29
the ongoing debates about possibly limiting intra-EU labor mobility, have also put substantial strain
on positive European feelings. There is a need to further research each of these topics. Hence, our
predictions are that economic evaluations will remain important in explaining high levels of
Euroskepticism.
There are also policy implications emerging from our empirical results. Certainly we demonstrate
that economic factors drive concerns about Europe. Hence, focusing on sustainable economic growth,
reducing unemployment, and lowering financial insecurity will likely reduce those concerns. A rise in
income inequality seems less likely to fuel fear about European membership. This aligns with a recent
European Commission study that asks which topics should be emphasized in order to cope with global
challenges and finds that “progress and innovation is gaining ground at the expense of social equality
in many countries” (European Commission, 2015b, p. 11). Finally, it is a problem that citizens have
the feeling that their voice does not count in the EU political decision making process, especially those
in countries such as Cyprus or Greece where the economic situation is under pressure (European
Commission, 2015a, p. 11). Cramme et al. (2013) state therefore that Europe should not be considered
to be the new locus of government, but instead it should provide institutions to support the individual
EU member states’ reform efforts. In order to increase public support for the EU, the authors urge
pro-European reformers to develop a new agenda that “exhibits a greater clarity about policy priorities,
a sharper view of where the EU can actually add real value, and a new institutional compromise that
can increase the responsiveness of democratic politics in Europe” (p. 1).
30
Appendix
Table A1: Descriptive Statistics, Western EU Member Countries, 2006–2011 (N=85,881)
Mean SD Min MaxMicro variables Euroskepticism 0.1567 0.3635 0 1Neg. Financial Expectations 0.1899 0.3922 0 1Male 0.4574 0.4982 0 1Age 40 13 15 64Education 15-, no full-time education 0.1502 0.3573 0 116-19 0.4041 0.4907 0 120+ 0.4457 0.4970 0 1Occupation Self-employed 0.1041 0.3054 0 1Managers 0.1533 0.3602 0 1Other white collars 0.1556 0.3624 0 1Manual workers 0.2807 0.4494 0 1House persons 0.1068 0.3088 0 1Unemployed 0.0920 0.2891 0 1Students 0.1076 0.3098 0 1Type of Community Rural area or village 0.3518 0.4775 0 1Small or middle-sized town 0.3679 0.4822 0 1Large town 0.2786 0.4483 0 1Macro variables Gini coefficient (times 100) 29.3 3.3 23.4 37.7Unemployment 7.9 3.4 3.1 21.7GDP 30647 10688 12800 80300HICP 108.15 3.93 101.28 121.35EU Budget 0.10 0.71 -.49 2.78
Source: European Commission (2012), European Commission: Eurobarometers 67.2, 70.1, 71.1, 71.3, 72.4, 73.4, 75.3
(2008-2011), Eurostat Database (2013a, 2013b, 2013c, 2013d). Note: Western EU countries include Austria, Belgium, Cyprus, Denmark, Finland, France, Germany, Greece, Ireland,
Italy, Luxembourg, Malta, The Netherlands, Portugal, Spain, Sweden, and the United Kingdom.
31
Table A2: Descriptive Statistics, Eastern EU Member Countries, 2006–2011 (N=51,457)
Mean SD Min MaxMicro variables Euroskepticism 0.1086 0.3111 0 1Neg. Financial Expectations 0.2481 0.4319 0 1Male 0.4568 0.4981 0 1Age 38 13 15 64Education 15-, no full-time education 0.0578 0.2334 0 116-19 0.5425 0.4982 0 120+ 0.3997 0.4898 0 1Occupation Self-employed 0.0909 0.2875 0 1Managers 0.1366 0.3434 0 1Other white collars 0.1629 0.3693 0 1Manual workers 0.2973 0.4571 0 1House persons 0.0542 0.2265 0 1Unemployed 0.1259 0.3317 0 1Students 0.1322 0.3387 0 1Type of Community Rural area or village 0.3502 0.4770 0 1Small or middle-sized town 0.3391 0.4734 0 1Large town 0.3094 0.4622 0 1Macro variables Gini coefficient (times 100) 30.4 5.0 22.7 39.2Unemployment 9.5 3.9 3.8 18.7GDP 9959 3535 3400 18400HICP 119.64 11.58 101.3 143.73EU Budget 2.04 1.34 .26 5.51
Source: European Commission (2012), European Commission: Eurobarometers 67.2, 70.1, 71.1, 71.3, 72.4, 73.4, 75.3 (2008-2011), Eurostat Database (2013a, 2013b, 2013c, 2013d).
Note: Former socialist EU member states include Bulgaria, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia, and Slovenia.
32
Table A3: Robustness Check of Standard Errors of Table 2 on Euroskepticism (EUS) and Negative Financial Expectations (NFE)
EU-27
Western EU
Former Socialist EU
EUS
NFE EUS NFE EUS
NFE
Neg. Financial Expectations (d)
1. Robust 0.975*** 0.985*** 0.989** 2. Bootstrap 0.949*** 1.009*** 0.673 3. Country Cluster 0.621** 0.527*** 0.601 Gini coefficient (times 100)
1. 0.998*** 1.006*** 1.000*** 1.000*** 0.997 1.007***2. 0.937*** 1.006*** 1.073*** 0.896*** 1.054 1.039***3. 0.588* 0.313*** 0.633** 0.312*** 0.398 0.314 Unemployment rate
1. 0.994*** 1.000*** 1.000** 0.967*** 0.989** 1.006***2. 0.948*** 1.014*** 1.017** 0.850*** 0.824* 1.056***3. 0.493 0.220** 0.380 0.174** 0.729* 0.305 HICP
1. 0.988* 0.991*** 0.994 1.002*** 0.989 0.986***2. 0.962* 1.080*** 0.923 0.996*** 0.944 0.985***3. 0.439 0.232*** 0.450 0.227* 0.360 0.234*Log(GDP)
1. 0.929** 0.992*** 1.010* 0.981** 0.984*** 0.988***2. 0.998** 1.007*** 1.036* 0.994** 0.779*** 0.967***3. 0.894** 0.196* 0.600 0.194 0.437** 0.299**EU Net Trans-fers (% GNI)
1. 1.001** 1.002*** 1.002 1.007** 1.008*** 0.985***2. 0.906* 0.935*** 1.064 0.939** 1.000*** 0.984***3. 0.370 0.210 0.687 0.582 0.560*** 0.250 Observations 137,338 85,881 51,457
Source: European Commission: Eurobarometers 67.2. 70.1. 71.1. 71.3. 72.4. 73.4. 75.3 (2008-2011). Eurostat Database (2013a. 2013b. 2013c. 2013d).
Note: The relative standard errors refer to the selected respective estimated parameters, which are the same for all methods compared here. The relative standard errors in this table are calculated as the standard errors of a particular method in relation to the basic standard errors without adjustment. For each variable, there are three numbers: The first one refers to the relative size of the robust standard error, the second one denotes to the relative bootstrap standard error, and the third one shows the relative country clustered standard error. Significance levels of the respective parameter estimates are * p < 0.10. ** p < 0.05. *** p < 0.01.
33
Table A4: Robustness Check: Recursive Bivariate Probit, 2007-2011 including Macro Changes, Euroskepticism (EUS) and Negative Financial Expectations (NFE)
EU‐27 Western EU
Former Socialist
EUS
NFE EUS NFE
EUS NFE
Negative Financial Expectations (d)
0.138** (0.058)
0.293***
(0.051)-0.135** (0.055)
Δ Gini 0.001 (0.002)
-0.003 (0.003)
-0.001 (0.003)
0.004 (0.005)
-0.000 (0.002)
-0.007*** (0.001)
Δ Unemployment -0.001 (0.002)
0.017***
(0.004) 0.004
(0.006) 0.023***
(0.005) 0.003
(0.002) 0.019***
(0.005) Δ pHICP 0.000
(0.001) 0.013***
(0.003)0.003
(0.003)0.020***
(0.004)0.003** (0.001)
0.012***
(0.004)Δ Log(GDP) 0.024
(0.051) -0.217***
(0.073) 0.193**
(0.086) -0.206(0.143)
-0.140* (0.084)
-0.236**
(0.104) Δ EU Budget 0.002
(0.005) -0.001(0.005)
0.010(0.008)
0.010(0.008)
-0.007 (0.006)
-0.010**
(0.004) Education, reference group: 20+ 15-, no full-time education (d)
0.073*** (0.009)
0.073***
(0.007) 0.082***
(0.009) 0.067***
(0.008) 0.071*** (0.017)
0.103***
(0.014) 16-19 (d) 0.035***
(0.005) 0.029***
(0.004) 0.048***
(0.007) 0.027***
(0.006) 0.025*** (0.006)
0.032***
(0.005) Male (d) 0.008**
(0.003) -0.014***
(0.004)0.008
(0.006)-0.010**
(0.005)0.005* (0.003)
-0.021***
(0.007)Age 0.001***
(0.000) 0.002***
(0.000) 0.001*
(0.000) 0.002***
(0.000) 0.002*** (0.000)
0.003***
(0.000) Type of Community, reference group: City Small or middle sized town (d)
-0.001 (0.005)
-0.001 (0.004)
-0.004 (0.009)
-0.001 (0.005)
0.002 (0.006)
0.002 (0.007)
Large town (d) -0.008 (0.008)
-0.009(0.007)
-0.015(0.012)
-0.003(0.007)
-0.004 (0.013)
-0.015(0.013)
Occupation, reference group: Managers Self-employed (d) 0.036***
(0.007) -0.001(0.008)
0.045***
(0.010)-0.001(0.009)
0.018** (0.008)
-0.003(0.013)
Other white collars (d) 0.031*** (0.006)
0.011*
(0.006) 0.037***
(0.009) 0.007
(0.006) 0.024*** (0.008)
0.019(0.012)
Manual workers (d) 0.058*** (0.008)
0.031***
(0.008) 0.075***
(0.011) 0.019**
(0.008) 0.044*** (0.011)
0.049***
(0.015) House persons (d) 0.060***
(0.009) 0.021*
(0.012)0.071***
(0.012)0.015
(0.011)0.052*** (0.014)
0.048*
(0.026)Unemployed (d) 0.081***
(0.006) 0.102*** (0.013)
0.096*** (0.010)
0.080*** (0.012)
0.094*** (0.021)
0.130*** (0.027)
Students (d) 0.024*** (0.008)
-0.011(0.007)
0.023**
(0.010) -0.011(0.008)
0.016 (0.012)
-0.003(0.013)
Observations 120001 120001 74849 74849 45152 45152Wald test of ϱ=0 -.0843 0.4781 -.1342 0.4411 .4001 0.0490
Source: European Commission (2012), European Commission: Eurobarometers 67.2, 70.1, 71.1, 71.3, 72.4, 73.4, 75.3 (2008‐2011), Eurostat Database (2013a, 2013b, 2013c, 2013d).
Note: We control for country fixed effects and calculate robust county clustered standard errors. We present marginal effects and standard errors in parentheses. Significance levels are * p < 0.10, ** p < 0.05, *** p < 0.01. The symbol (d) stands for discrete change of dummy variable from 0 to 1.
34
References
Anderson, C. J., 1998. When in Doubt, Use Proxies: Attitudes towards Domestic Politics and
Support for European Integration. Comparative Political Studies 31 (5), pp. 569–601.
Anderson, C. J. and Kaltenthaler, K. C., 1996. The Dynamics of Public Opinion toward European
Integration, 1973-1993. European Journal of International Relations 2 (2), pp. 175–199.
Anderson, C. J. and Reichert, M. S., 1995. Economic Benefits and Support for Membership in the
EU: A Cross-National Analysis. Journal of Public Policy 15 (3), pp. 231–249.
Armingeon, K. and Ceka, B., 2014. The Loss of Trust in the European Union during the Great
Recession since 2007: The Role of Heuristics from the National Political System. European Union
Politics 15 (1) pp. 82–107.
Atkinson, A. B., 2013. Reducing Income Inequality in Europe. IZA Journal of European Labor
Studies, 2:12.
Beckfield, J., 2006. European Integration and Income Inequality. American Sociological Review 71,
pp. 964–985.
Beckfield, J., 2009. Remapping Inequality in Europe: The Net Effect of Regional Integration on
Total Income Inequality in the European Union. International Journal of Comparative Sociology 50
(5–6), pp. 486–509.
Beckmann, J., Belke, A. and Kühl, M., 2011. Global Integration of Central and Eastern European
Financial Markets – The Role of Economic Sentiments. Review of International Economics 19 (1),
pp. 137–157.
Braun, D. and Tausendpfund, M., 2014. The Impact of the Euro Crisis on Citizen‘s Support for
the European Union. Journal of European Integration 36 (3), pp. 231–245.
35
Bruter, M., 2005. Citizens of Europe? The Emergence of a Mass European Identity. Basingstoke:
Palgrave Macmillan.
Bryan, M. L. and Jenkins, S. P., 2015. Multilevel Modelling of Country Effects: A Cautionary Tale.
European Sociological Review, doi: 10.1093/esr/jcv059.
Burgoon, B., 2013. Inequality and Anti-Globalization Backlash by Political Parties. European Union
Politics 14 (3), pp. 408–435.
Cameron, A. C., Gelbach, J. B. and D. L. Miller, 2008. Bootstrap-based Improvements for Inference
with Clustered Errors. The Review of Economics and Statistics 90 (3), pp. 414-427.
Cichowski, R., 2000. Western Dreams, Eastern Realities. Support for the European Union in
Central and Eastern Europe. Comparative Political Studies 33 (10), pp. 1243–1278.
Cramme, O., Meyer, A. and Ritzen, J., 2013. A New Promise for Europe. How the Elections to the
European Parliament can Stop Eurosion. Policy Network Paper. http://www.policy-
network.net/publications/4453/A-New-Promise-for-Europe (accessed June 2015).
de Vreese, C. H. and Boomgaarden, H., 2005. Projecting EU Referendums: Fear of Immigration
and Support for European Integration. European Union Politics 6 (1), pp. 59–82.
de Vries, C.E., 2013. Ambivalent Europeans? Public Support for European Integration in East and
West. Government and Opposition 48 (3), pp. 434–461.
Diez Medrano, J., 2010. Europe’s Political Identity: Public Sphere and Public Opinion, in: Lacroix,
J. and Nicolaides, K. (Eds.), European Stories: Intellectual Debates on Europe in National Contexts.
Oxford University Press, Oxford, pp. 315–333.
Eichenberg, R. C. and Dalton R. J., 1993. Europeans and European Community: The Dynamics of
Public Support for European Integration. International Organization 47 (4), pp. 507–534.
36
Eichenberg, R. C. and Dalton, R. J., 2007. Post-Maastricht Blues: The Transformation of Citizen
Support for European Integration 1973-2004. Acta Politica 42 (2–3), pp. 128–152.
European Commission, 2012. EU Budget 2012. Financial Report. Table: Operating budgetary
balances, 2000–12.
European Commission, 2015a. Public Opinion in the European Union, First Results. Standard
Eurobarometer 83.
European Commission, 2015b. Future of Europe, Summary. Special Eurobarometer 394.
European Commission: Eurobarometers 67.2, 70.1, 71.1, 71.3, 72.4, 73.4, 75.3 (2008–2011). GESIS
Data Archive, Cologne.
Eurostat Database, 2013a. Gini Coefficient of Equivalised Disposable Income (source: SILC)
(2006–2011).
Eurostat Database, 2013b. Gross Domestic Product at Market Prices (2006–2011).
Eurostat Database, 2013c. Harmonized Indices of Consumer Prices (HICPs) (2006–2011).
Eurostat Database, 2013d. Unemployment Rates by Sex and Age Groups (2006–2011).
Eurostat Database, 2015. Real Gross domestic product (GDP) growth rate - volume (2004–2014).
Franklin, M. and Wlezien, C., 1997. The Responsive Public: Issue Salience, Policy Chance and
Preferences for European Unification. Journal of Theoretical Politics 9 (3), pp. 347–363.
Gabel, M., 1998a. Interests and Integration: Market Liberalization, Public Opinion, and European
Union. University of Michigan Press, Ann Arbor.
37
Gabel, M., 1998b. Public Support for European Integration: An Empirical Test of Five Theories.
The Journal of Politics 60 (2), 333–354.
Gabel, M. and Palmer, H., 1995. Understanding Variation in Public Support for European
Integration. European Journal of Political Research 27 (1), 3–19.
Gabel, M. and Whitten, G. D., 1997. Economic Conditions, Economic Perceptions, and Public
Support for European Integration. Political Behavior 19 (1), pp. 81–96.
Gelman, A. and Hill, J., 2009. Data Analysis Using Regression and Multilevel/Hierarchical Models,
11th printing, Cambridge University Press, Cambridge et al.
Gill I. S. and Raiser M., 2012. Golden Growth. Restoring the Lustre of the European Economic
Model. The World Bank, Washington, DC.
Green, W. H., 2003. Econometric Analysis, 5th ed., Pearson Education, New Jersey.
Hakhverdian, A., van Elsas, E., van der Brug, W. and Kuhn, T., 2013. Euroscepticism and
Education: A Longitudinal Study of Twelve EU Member States, 1973-2010. European Union Politics
14 (4), pp. 522–541.
Harden, J. J., 2009. A Comparison of Methods for Estimating Linear and Generalized Linear
Models with Multilevel Data. Presentation Paper at the 26th Annual Meeting of the Society for Political
Methodology, July 23–25, 2009, New Haven, CT.
Herzog, A. and Tucker, J. A., 2010. The Dynamics of Support: the Winners-Losers Gap in Attitudes
toward EU Membership in Post-Communist Countries. European Political Science Review 2 (2), 235–
267.
Hooghe, L. and Marks, G., 2005. Calculation, Community and Cues. Public Opinion on European
Integration. European Union Politics 6 (4), 419–443.
38
Hooghe, L. and Marks, G., 2007. Sources of Euroscepticism. Acta Politica 42, 119–127.
Kucia, M., 1999. Public Opinion in Central Europe on EU Accession: The Czech Republic and
Poland. Journal of Common Market Studies 37 (1), pp. 143–152.
Kuhn, T., Van Elsas, E., Hakhverdian, A. and van der Brug, W. 2014. An Ever Wider Gap in an
Ever Closer Union: Rising Inequalities and Euroscepticism in 12 West European Democracies, 1975-
2009. Socio-Economic Review. Advanced online publication: doi: 10.1093/ser/mwu034.
Levy, N. and Phan, B., 2014. The Utility of Identity: Explaining Support for the EU after the Crash.
Polity 46 (4), pp. 562–590.
Loveless, M. and Rohrschneider, R., 2011. Public Perceptions of the EU as a System of
Governance. Living Reviews in European Governance 6 (2), http://www.livingreviews.org/lreg-2011-
2 (accessed June 2015).
Lubbers, M. and Jaspers, E., 2011. A Longitudinal Study of Euroskepticism in the Netherlands:
2008 versus 1990. European Union Politics. 12 (1), 21–40.
Maddala, G., 1983. Limited-Dependent and Qualitative Variables in Econometrics. Cambridge
University Press, Cambridge.
Mau, S., 2005. Europe from the Bottom: Assessing Personal Gains and Losses and its Effects on
EU Support. Journal of Public Policy 25 (3), pp. 289–311.
Mau, S., Mewes, J. and Schöneck, N. M., 2012. What Determines Subjective Socio-economic
Insecurity? Context and Class in Comparative Perspective. Socio-Economic Review 10 (4), pp. 655–
682.
McLaren, L, 2002. Public Support for the European Union: Cost/Benefit Analysis or Perceived
Cultural Threat? The Journal of Politics 64 (2), pp. 551–566.
39
McLaren, L., 2007. Explaining Mass-Level Euroskepticism: Identity, Interests, and Institutional
Distrust. Acta Politica 42 (2–3), pp. 233–251.
OECD, 2008. Growing Unequal? Income Distribution and Poverty in OECD Countries. OECD
Publishing, Paris.
OECD, 2011. Divided We Stand: Why Inequality Keeps Rising. OECD Publishing, Paris.
OECD, 2014. National Accounts at a Glance 2014, OECD Publishing, Paris.
Pew Research Center, 2015. Faith in European Project Reviving.
http://www.pewglobal.org/files/2015/06/Pew-Research-Center-European-Union-Report-FINAL-
June-2-20151.pdf (accessed July 2015).
Piketty, T., 2014. Capital in the Twenty-First Century. Harvard University Press, Cambridge, MA.
Ritzen, J. and Zimmermann, K. F., 2014. A Vibrant European Labor Market with Full
Employment, IZA Journal of European Labor Studies, 3:10.
Rohrschneider, R. and Whitefield, S., 2004. Support for Foreign Ownership and Integration in
Eastern Europe: Economic Interests, Ideological Commitments, and Democratic Contexts.
Comparative Political Studies 37 (3), pp. 313–339.
Rotte, R. and Zimmermann, K. F., 1998. Fiscal Restraint and the Political Economy of EMU. Public
Choice 94 (3), pp. 385–406.
Sanchez-Cuenca, I., 2000. The Political Base of Support for European Integration. European Union
Politics, 1 (2), pp. 147–171.
Serricchio, F., Tsakatika, M. and Quaglia, L., 2013. Euroscepticism and the Global Financial Crisis.
Journal of Common Market Studies 51 (1), pp. 51–64.
40
Summers, L. H., 2013. IMF Fourteenth Annual Research Conference in Honor of Stanley Fischer.
Washington, DC, November 8, 2013.
Stegmueller, D., 2013. How Many Countries for Multilevel Modeling? A Comparison of Frequentist
and Bayesian Approaches. American Journal of Political Science 57 (3), pp. 748-761.
Wilde, J., 2000. Identification of Multiple Equation Probit Models with Endogenous Dummy
Regressors. Economics Letters 69, pp. 309–312.