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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: Zimmermann@iza.org

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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

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