Rebuilding a Tarnished Reputation

Rebuilding a tarnished reputation

European image: the exploration of which variables affect pro- and anti-European Union sentiments in the European Union member states.

Master thesis

Author: Floris den Biggelaar

Student number: S878999

Date: August 2016

Supervisor: dr. A.R.C.M Luijkx

Second reader: P.A. Blokker PhD Abstract

The European Union seems to have an image problem; Euro-skeptic political parties gain support across Europe and questions about the level of democracy in the EU seem to resonate ever louder. In this thesis we have investigated what kind of image the European Union conjures up among EU citizens along a five point Likert scale ranging from very positive to very negative and we will analyze the impact that various individual level and country level variables have on this image using a multilevel model. We have used data derived from the Eurobarometer, in particular two waves: Eurobarometer 62.0 (2004) and Eurobarometer 82.3 (2014) in order to determine whether the EU-image has changed over the course of ten years and to see whether there has been a change in which variables affect this image. The following individual level variables are included: gender, political preference, education level, age, socio-economic status, life satisfaction and to what degree respondents held the EU responsible for austerity measures. The following four country level variables were included: whether or not a country had to be bailed out by third party aid, the length of the EU membership, the GDP per capita and the mean EU-image in 2004. Our results indicate that there is a reversed composition effect; when we account for country level variables, the differences in EU-image are getting larger. The most important individual level predictor appeared to be one's (self reported) social class, while the mean EU-image of 2004 was the most influential country level variable. We found evidence that the interaction of living in a country that had to accept forced austerity measures and believing that the EU is responsible for the imposed austerity measures leads to a more negative image of the European Union while separately these variables had no statistical significant impact.

Acknowledgement

I would like to thank my thesis supervisor dr. Ruud Luijkx of the Tilburg School of Social and Behavioral Sciences at Tilburg University. He was always available for feedback whenever I encountered problems or had a question about my research. As I have been far from a regular student, he still consistently allowed this paper to be my own work, but steered me in the right the direction whenever he thought I needed it.

I would also like to acknowledge Paul Blokker of the department of Sociology at the University of Trento, as the second reader of this thesis. His very valuable comments on this thesis, especially his suggestions and insights on the theoretical background, have been of great value in writing this thesis.

Finally I would like to thank M. Waijers MSc for reviewing the text and furthermore I would like to thank my girlfriend, my parents, brother and sisters for their moral support throughout my (fairly long) studies.

Introduction

In the aftermath of the Second World War, ideas of transnational cooperation in Europe became more prominent. As an important head of state at the very beginning of the European conjunction, Winston Churchill was convinced that only a united Europe could guarantee peace. His aim was to eliminate the European ills of nationalism and readiness to go to war once and for all. With this, Churchill was one of the first to call for the creation of a 'United States of Europe'. Initially, in 1949, the Council of Europe was founded as a first intergovernmental organization that was aimed to promote democracy, human rights and rule of law across Europe. In 1950 the French minister of foreign affairs Schuman proposed to form a community to integrate the coal and steel industries of European countries. A year later France, Italy, Belgium, Luxembourg, the Netherlands and Western Germany signed the treaty of Paris which created the European Coal and Steel Community. These first steps were to foster economic cooperation with the idea being that countries who trade with one another become economically interdependent and in this way had created an incentive to avoid conflict. At this point however, the ideas of what this pan- European cooperation should lead to were highly divergent.

Over the course of the following decades, the European Union not only has expanded the number of member states. Also both the extent to which political decisions are made and the manner in which this was done, have changed greatly. This rudimentary problem of the highly diverse ideas on how to form the European Union has never sufficiently been dealt with, leading to a patchwork of part- and half solutions put together instead of tackling this large debate right at the beginning of the pan-European cooperation in the 1950s. The fact that these cultural differences inhibited the start of a federalization of the pan-European cooperation has been identified as the design flaw of this cooperation; "... the federal approach is the common structure we see when countries want to jointly tackle certain problems or work together in general" (G. Mak, 2016). The cultural differences between for example France and Germany or England and Italy lay at the root of how member states regard the European cooperation and of course how they deem their self-interest best served. At the start of the pan-European cooperation, the strategy on European integration was set by Jean Monnet. According to Featherstone (1994), the approach

2 chosen by Monnet was characterized by technocracy and elitism and over time this lead to a rather thin democratic legitimacy. This has been identified as a key cause for the so called democratic deficit of the European Union and we will elaborate more on this subject later on.

In order to mend certain structural problems of the European Union, a review of the constitutional framework of the Union was deemed necessary by the European Council. With ten new member states standing on the threshold of their membership in 2004, the 2001 Laken declaration proposed a commitment to improving the EUs democratic structure, transparency and efficiency. These ambitions were drafted into the 'Treaty establishing a Constitution for Europe' (Constitutional Treaty) and this treaty was aimed to replace all existing European Union treaties with a single, consolidated constitution for the EU. Furthermore, the preamble of this treaty listed the reasons for founding the European Union as well as the circumstances in which the EU was founded.

However, when this European constitution had to be ratified by the member states, there were two forms chosen to gain public support for ratification of the treaty: ten countries chose to hold a referendum to ask their citizens, while 15 countries chose for parliamentary approval. Spain approved ratification via both options. After a 'Yes' in the Spanish referendum, the two next referendums in France and the Netherlands respectively resulted in a rejection of the treaty. These rejections postponed the further ratification procedures in member states that had not yet approved ratification (the Czech Republic, Denmark, Poland, Portugal, the Republic of Ireland and the United Kingdom had a referendum planned and the parliaments of Finland and Sweden had not yet approved the ratification of the treaty) and rendered the ratification approval of the countries that had already completed the ratification process obsolete: the Constitution was cancelled in 2005 and the process of restructuring the EU was put on a hold. In order to spark this restructuring back to life, the so called Amato Group was appointed. This action committee for European Democracy, as it was formally named, consisted of 14 high-level European politicians such as former prime ministers, former European commissioners and former foreign ministers and the group backed by the European Commission in their efforts to rewrite the Treaty establishing a Constitution for Europe in such a way that all member states could ratify the new treaty. This new 'Reform Treaty' was finished in 2007, became known as the Lisbon treaty. It was designed to take the steps towards making the EU more democratic, efficient and

3 transparent and thereby able to tackle global challenges such as climate change, security and sustainable development. These changes were aimed at the European Union´s further development into a world power that can compete with the United States of America and the upcoming world powers that the BRIC countries (Brazil, Russia, India and China) were becoming.

The Lisbon treaty still was a slimmed down version of the Treaty establishing a Constitution for Europe and after the previously mentioned initial rejection by referendums of the previous constitution, the Republic of Ireland was the only member state to hold a referendum before ratifying the Lisbon treaty. This referendum resulted in a 53.4% versus 46.6% rejection of the treaty with a 53.1% turnout. A 'Post-referendum survey in Ireland', conducted by the Eurobarometer in June 2008 (within two days from the referendum date), indicated that "over half of the people who did not vote in the referendum said this was due to a lack of understanding of the issues" (Flash Eurobarometer 245, 2008). This time the result of a negative referendum did not lead to a nullification of the treaty: negotiations lead to small changes in the treaty and a second Irish referendum resulted in a 67.1% versus 32.9% result in favor of the treaty with a turnout of 59%. In spite of this 'hiccup' the Lisbon Treaty was ratified by all member states in 2009. This ratification was one of the biggest steps that the European Union had taken since the enlargement of the European Union in 2004. However, this leaves room for a critical review of at least the part of the aim to make the European Union more cohesive and more understandable to the general public (publication text treaty establishing a constitution for Europe, 2009) might need revision when the public's opinion is considered inconvenient rather than as an indication that steps that are being taken. Follesdal and Hix (2006) foresaw an upsurge of articles on the democratic deficit in the European Union following this rejection of the Constitutional Treaty, anticipating at least a partial democratic deficit even though they expected that change was on the way. New incentives for national party leaders to promote the European- level issues rather than purely national concerns are the way to popularize the EU as transnational government with legitimate power. Schmidt (2013) however, finds that the large majority of scholars conclude that the EU suffers from a democratic deficit, which are "made worse by the fact that the EU elites have done little in recent years to produce the narratives and discourses that would serve to legitimize most of its policies or to build identity." (Schmidt, 2013, p11). The financial and economic crisis of 2008 has inspired and refueled the debate on the

4 democratic deficit in the EU and over the course of the years (since the 2006 Fossedal and Hix study) the desired changes in promoting EU-level policy have not taken place. Exactly the opposite has developed: large Euro-skeptic movements have been elected in the 2014 European parliament elections in member states throughout Europe. In his 2012 paper, Jürgen Habermas has pointed out that the crisis has drawn the attention away from the underlying problem. According to him, the fundamental construction flaw in the EU is that a monetary union has been created "without a corresponding political union" (Habermas, 2012, p. 336). He considers this imbalance to be at the root of the EU-image problem: the EU currently does not have the ability to establish a balance in the levels of competitiveness of the member states' economies because it simply lacks the political power to establish and enforce rules and regulations.

How do European citizens appreciate the European Union? This question is key when assessing whether or not the EU-politicians are reaching their goals of making the European Union more cohesive and understandable. The motto of the European Union, which first came into use in 2000, "United in diversity" emphasizes two dimensions of identity: the uniqueness of the member states, but at the same time the connection between the member states (economically and politically). Immerfall and Therborn (2009) state that national identity not always and not only forms an obstacle to European attachment, but "...[are]..national identity and European attachment only correspondent as long as European integration is believed to bring about tangible benefits" (Immerfall and Therborn, 2009, p. 348). This yields that there is an opening that can be pursued when politicians are able to point out such tangible benefits and explain how they result from European attachment.

Another point of interest with respect to the appreciation of the European Union, which is more applicable here, was presented by the economic crisis. The economic growth of the Western world in the nineties had faded and for the first time after the introduction of the European Monetary Union with the Euro as the common currency in sixteen EU countries (and another three countries on track of adopting the Euro as their currency), major cutbacks and reforms were needed across the Eurozone. Europeans are learning that the implications of an economic and monetary union mean taking responsibility for each other not just in good times, but also in bad. Sentiments are fueled by populist/nationalist politicians in both the wealthier and poorer countries stating that the other are keeping or dragging 'us' down (depending on where they are).

What we attempt to investigate is whether these anti-EU sentiments are a result of the economic crisis in the Eurozone or if they are in fact a result of other sociological mechanisms (which are affecting the development of a European identity that is supported throughout the Union). This leads us to the following research question: which individual and country level characteristics influence the image of the European Union among its citizens?

In this study we will investigate in what way the worldwide financial and economic crisis has had an impact on the support for the European Union. More specifically we attempt to investigate the image that EU-citizens have of the European Union. In order to do so, we are going to study the image of the European Union among its citizens as researched by the Eurobarometer (which is conducted by the Public Opinion Analysis sector of the European Commission) and we will compare the data of 2004, well before the financial and economic crisis manifested itself, and 2014, when there had been a few years of recovering from the crisis. We would like to determine the sentiments in the individual EU member states in order to investigate if there is a broad consensus among citizens of the various member-states or whether they are divided on these topics. In a multi level model we will test whether the differences we expect to find between the EU countries will hold up after controlling for a set of individual level and context level variables (such as gender, level of education and GDP per capita, we will specify the used variables in our operationalization chapter) as well as the interaction between certain individual and country level variables. Before we can adequately answer these research questions we first need to elaborate on the concepts of identity and European identity which we subsequently link to the image that citizens have of the European Union.

Theory and hypotheses

As mentioned above, the ideas of what the European unification should be aimed at and how the governing body - what we now call the European Union - should be designed were quite diverse. This entails that the ideas on what can be considered European, let alone what can be considered as European identity, is looked at differently as well. The nature and political role of the EU and how this role is appreciated is vital when the image of the European Union is being discussed. In the following section we will explore the basis of European collaboration and how this has evolved over the years.

One of the first main contributions to the study of possible ways in which men someday might be able to abolish war through pan-European collaboration was by Deutsch et al. (1957). They investigated this along the formation of what they called 'security-community' which they considered to be a group of people which has become integrated. Integration and amalgamation were key aspects to the approach of the authors and they formulated them as follows: "[...] integration does not necessarily mean only the merging of peoples or governmental units in a single unit. Rather, we divide security-communities into two types: amalgamated and pluralistic" (Deutsch et al., 1957, p. 124) where integration meant that a sense of community had emerged. Amalgamation is the merging of previously independent units with some kind of unitary or federal government. The pluralistic communities are those where legal independence of separate governments is retained. When we look at the beginning of the EU, we can say that the sense of community had been achieved in the shared idea that collaboration would prevent the participating countries from declaring war amongst each other, that these countries were better off striving for prosperity alongside of each other rather than to pick up arms and fight. The previously independent sovereign countries agreed to the formation of the European Economic Community (the EEC) by which they had created a customs union along with the creation of common transport and agriculture policies and even a European social fund. Shortly, the security community that later expanded and grow to become the EU as we know it today. In the general findings, Deutsch et al. (1957) reported that for a newly amalgamated security- communities to become stable and successful over time, it is highly important that common laws

7 and courts are established in order to maintain a balance of power. Among the six founding countries of the European Economic Community this balance of power had constantly been a hot topic. The wounds of two world wars were still fresh and the three largest participants - France, Italy and West Germany - obviously did not want any of the other two to become much stronger. Even though Deutsch et al. (1957) did not find evidence pointing at the need for balance per se, their conclusions are largely with respect to the need for a police forces and with a situation where one state is far stronger than the others combined. With adequate police forces in all participating states and the power balance being reasonably well guaranteed with the aforementioned three largest countries protecting their own interests, these points seemed to be tackled at the start of the European collaboration.

Deutsch et al. (1957) describe nine essential conditions for a successful amalgamated security- community, of which a mutual compatibility of main values, expectations of stronger economic ties or gains and predictability of behavior are most applicable to the early European collaboration. Starting with the above mentioned formation of an international coal and steel community, which essentially was the start of the EEC, can thus be looked at from a perspective of community formation where member states become increasingly interconnected. Since the authors did not find evidence that modernization of life (i.e. through faster transportation or communication) did not automatically stimulate a trend towards more internationalism, there needed to be a more active role for the security community to emphasize the necessity of international collaboration. Since at the beginning of the collaboration there was no agreement on the (degree of) federalization of the European security community. At the start of the collaboration the political elites who were at the helm of the formation of the EEC focused their efforts on ad hoc problem solving through treaties that amended flaws in the system they had put together. There was no clear plan set that lead to a complete (and pluralistic) security community via specific steps.

This became more apparent over the years and Featherstone (1994) concluded that the initially adopted elitist and technocratic structure of the EEC was no longer adequate as it was vulnerable to attacks/criticism because of a lack of democratic legitimacy. Further expansion of the number of member states in the 1990s and the start of the European Monetary Union required a stronger political base as European integration on these topics shifted legislative authority from countries

8 to the community. It is at this pivot point that the earlier communities were merged and turned into the European Community and where the political aspect of the community became the most important aspect for (further) integration. Follesdal and Hix (2006) pointed out that the political elites will have to facilitate the politicization of the EU agenda in terms of making it less distant from the citizens. They state that alignment between national and European parties is key in lowering that gap i.e. promoting EU-wide problems to be solved by the EU government rather than by the national governments separately. With this shift from merely economical interests of the separate countries combined in one EU wide market without borders for people, goods or services, the successful political elitist approach of the early days of European collaboration now stands in the way of a strong European Parliament that represents the people. This democratic deficit is widely used by Euro-skeptic parties by raising questions of the legitimacy of a government that is not directly chosen by the people it governs. Habermas (2012) shares the view of Follesdal and Hix (2006) insofar that he states that the lack of political power can be solved by a stronger EU when it comes to European problems and in turn will a stronger, more decisive EU have more support among its citizens.

Identity

The concept of identity has been diffuse and, probably therefore, much debated. The word identity itself is derived from the Latin identitas which translates into 'sameness' and basically, it refers to the definition of who one is. Broadly, there are two aspects to identity: the personal aspect and the social aspect. The personal aspect is concerned with "how one perceives and defines oneself as a human being" (Immerfall & Therborn, 2009, p325). The second aspect is social identity and here the identity is a representation to which group or groups one feels attached to. It draws on the social nature of humans, the innate need for belonging to a group, and this social identity provides people with self-esteem. The attachment to a group also provides advantages for the group itself as collective identification leads to an increase in the group cohesion (Immerfall & Therborn, 2009).

Identity is a concept that is much studied from a philosophical point of view, as it is entangled with existential questions about 'who we are' and 'why we are'. Currently, personal identity in philosophy is positioned as an integral part in human motivation, cognition and affection. From this we take the need to draw on the philosophical work of scholars like John Locke, David

Hume and William James, who investigated and described personal identity, more specifically the self. These scholars form the basis of the personal identity that Immerfall and Therborn (2009) describe, which is mainly derived from the works of William James. According to James, the personal identity consists of three components that balance one another in such a way that the person can function in society. The first component is the empirical self or 'me' and this contains all experiences and knowledge that one accumulates in life. This stream of information that is accumulated by the 'me' component is being checked and filtered by the pure ego or 'I', which is the second component. The ongoing process of filtering results in the third component; the self. Because of the ongoing accumulation of one's 'me' during one's life, Immerfall and Therborn (2009) argue that the 'self' is an ever evolving concept. When looking at the social aspect of identity, which in itself is an ever changing context, this evolving nature of the self becomes interesting, since it is the interaction with other people that require the input that the 'me' accumulates to be filtered by the 'I' in the first place. The inclusion in a larger, social context calls for ongoing adaptation and thereby a continuous exchange between the personal identity and the social identity takes place.

Smith (1992) recognized that humans have multiple identities that either co-exist or cross-cut but do not exclude each other. These multiple identities are becoming apparent in context of a specific situation and if the situation changes the identity may shift accordingly. For this research, European identity is to be considered through the layered nature of these identities. A person identifies oneself on several social levels: regional (city or province), national and European, which makes it possible to define European identity as a sense of belonging to Europe as a citizen of the European Union and therefore having a stake in voting for the European parliament. The problem of the cultural impact of European unification lies in the potential conflict of multiple identities. This has played a major part in European debates ever since the start of the coal and steel community. At issue has been the possibility and subsequent legitimacy of a 'European identity' that seemingly opposes the existing national identities. Nationalists who see the nation as the pinnacle of political power and therefore as only legitimate government, are contesting a European identity precisely because of this; they see a higher political position of Europe as an attack on the(ir) national identity.

European identity

The concept of European identity dates back to the Copenhagen conference of December 1973, when the (at that time) nine foreign ministers of the member states of the European Community published their Document on the European Identity (Bulletin of the European Communities, 1973). The six countries of the Coal and Steel Community had accepted Denmark, the United Kingdom and Ireland in their community as of the first of January of that year. Defining the European identity, according to the press release, involved three main aspects:

- Reviewing the common heritage, interests and special obligations of the member states, as well as the degree of unity that thus far had been achieved within the community. - Assessing the extent to which the member states already act together in relation to the rest of the world and the responsibilities that result from this. - Taking into consideration the dynamic nature of European unification.

The principle of unity clearly was the strong basis of the proposition of a European identity, which of course still draws on the initial start of the Coal and Steel Community of post WWII Europe, when unification was deemed indispensable to ban war. The document on European identity consists of 22 points that cover the unity of the nine member states, the relation between European identity and the world and the dynamic nature of the construction of a United Europe. In the first section there is already stated that for the construction of a United Europe is open to other European nations who share the same ideals and objectives. With respect to the rest of the world, it was stressed that the European unification was not inspired by a desire for power, rather the ministers were convinced that this union will benefit the whole international community. In spite of these assuring words, Stråth (2003) points out that this world view was a hierarchical one. A hierarchy where the other European nations with whom friendly relations already had been established by the member states. Second to this came the responsibility towards the Mediterranean, Africa and the Middle East, while the relations with the United States came third followed by the cooperation with Japan and Canada. And the easing of hostile relations with the Soviet Union came, only just, before the lowest ranked group of countries: China and Latin America. Stråth (2003) explains the relatively low position of the United States to be due to the oil and dollar crisis of the early 1970s. Europe formulated a basis for an active role as a mediator and bridge builder and internally the sight was set for further unification. The following decades

11 there has been a gradual shift towards more integration between the member states and on three occasions the European Community was expanded to fifteen member states in 1995. The new millennium was embarked with a new slogan: 'Unity in Diversity'. For this specific period, Immerfall and Therborn (2009) looked at Eurobarometer data in order to determine the support for the European Union. What they found was that after a continuously shifting level of support during the 1970s, the amount of respondents who answered positively to the question "Generally speaking, do you think that [our country]'s membership of the European Union is ...? -a good thing, a bad thing or neither good nor bad" started to show a steady upward trend of around 32% with a positive answer in 1981 to a peak of nearly 65% of the respondents who answered positively in 1991. This trend was broken in the 1990s, with a downward slope in the rate of people who considered the EU membership of their country a good thing. In 1997 the lowest ratings of just over 30% were recorded and since then the rate of people who were positive on this statement fluctuated around 35%. For the ten countries that have joined the EU on May 1st 2004, the scores are slightly higher than for these countries but these differences faded within a few years. Immerfall and Therborn found out that even though the support is fluctuating, the differences between countries over time prove to be rather stable. A more in depth investigation of which values are associated with the EU lead Therborn and Immerfall to conclude that the EU represents hope for new member states in terms of peace and stability, which respondents in the old member states already take for granted. Noteworthy as well is that the EU’s famous “four freedoms,” the free movement of goods, services, capital, and labor, are not associated with the EU as representation of individual freedom. "In all three groups of respondents the USA is thought to represent individual freedom better than the EU" (Immerfall & Therborn, 2009, p. 334). To sum up their conclusions, there are four main variables that directly influence the support for European integration: benefits (receiving direct benefits from the EU), low salience of EU issues and European attachment are positively influencing the support for European integration and the national attachment is negatively influencing this support.

Immerfall and Therborn (2009) draw from the work of Boehnke and Fuss (2008) that Europeans identify in a way of distinction on the personal level first: how they differ from the people around them in their social sphere. Identification with respect to social belonging appear to be less important. In the social sphere, groups of immediate social context are more important than the more distant context: the family and place of birth have a stronger place in the identity than

12 the nation or Europe. An important conclusion of Boehnke and Fuss (2008) is that there is no tension between these levels of social identification; "...identification with Europe goes well with identification with one’s birthplace, one’s region of residence or one’s country" (Boehnke & Fuss, 2008, p. 477).

In this study, we follow the dual conceptualization of identity as identified by Smith (1992) and Caporaso & Kim (2009) who found that multiple identities exist among the people of the EU member states, including identification with the EU. These identities are layered, in the sense that a person identifies oneself on several levels: regional (city or province), national and European. Caporaso and Kim (2009) state that there is tension between these levels of identification, tension which becomes apparent when a forced choice is imposed on people to pick between these levels of identification. They state that the gap between identification with the EU and the belief that the EU is the right level to make many policies lies at the root of this tension. This is in line with Kohli (2000) who stated that up till then, the possibility of ambivalent and hybrid identity patterns had been a hiatus in the literature on European identity. "European identity has instead taken its inspiration from the political science concept of identity as a sense of belonging to some larger political unit, especially as developed in the analysis of nationalism and national identity" (Kohli, 2000, p117). Moreover, Strårth (2003) pointed out that the concept of European identity has been subject of debate ever since it was first introduced in the political agenda in 1973. Even though the concept as such was widely accepted, the meaning and content of a European identity has been the subject of endless debate. Bearing this in mind, we operationalize European identity as a transnational identity that is an addition to ones' national identification. In line with Strårth (2003), Caporaso & Kim (2009) and Immerfall & Therborn (2009) we approach European identity as a concept that is built on the civic component of identification. The stepwise realization of treaties over the years have provided free movement of people, goods, services and capital and hereby the European citizens have been increasingly affected by the European Union. The data that is analyzed for this study provides insights on how the respondents feel about the European Union and these feelings can be broken down roughly into three aspects: sense of safety, economical aspects and the political issues and institutions.

Smith (1992) wrote that the established cultures essentially block or inhibit the development of a European identity that is supposedly substituting national identity. Rather, a European identity should compare to the currently present national identity in a similar way that the national identity compares to regional/local identity. In this way, a European identity can fit in the earlier discussed layers of identities. This is confirmed by Caporaso and Kim (2008), from which it becomes apparent that the national identity only 'blocks' a European identity when people are asked to make a choice between the two, but this does not mean that there is no room for identification with the EU. They see this in the light of high levels of support for EU-decision making, which are much higher than the levels of support for European identity. Trade and investment levels within the EU are quite high, and the EU economies show a convergence in terms of inflation and unemployment rates. This increasing alikeness may very well promote the sense of coherence within the European Union. Is the subject of the increasing institutionalization of the European Union looked at from a similar perspective by the various member states? Do European citizens agree upon the place for a European identity with respect to the national or even regional identities that are already present in their country? Which sociological theories can be used to determine and explain the differences (if any) in opinion to such questions and what could be done in order to increase a sense of unity and support for a shared European identity? With these analyses we would like to develop an insight in the aspects that play a role in the fluctuating support for the European Union.

The most important aspect of this study is the operationalization of the concept of European identity, as there are multiple interpretations of what such an identity should comprise and what not: to what extent should a European identity replace national identity or should this not be the case at all and should it much rather be a supplement to the (pre-existing) national identity.

The image of the European Union

We will explore European identity mainly by investigating the image that the European Union conjures up with the citizens of the EU member states as mapped by the Eurobarometer. The Eurobarometer is a biannual survey which is conducted by TNS opinion & social at the request of the European Commission. First off it is good to examine in what way the opinion on the European Union has evolved among European citizens over the years. How people appreciate the

EU can provide useful information on how they feel about being a European as part of their identity and therefore can be used in order to investigate the support for the European Union. In order to be able to determine the impact of the financial and economic crisis and how its effect on national policy has influenced the public opinion, we will analyze the evolution of the image that the EU conjures up among its citizens. Starting in 2004, before the crises had hit to ensure a rather neutral benchmark of support of the EU, our next point of interest is 2006 and on to 2008, when it was quite clear that there was a financial and economic crisis but the biggest austerity measures and programs had not yet been implemented. Then 2010 when major austerity measures were taken across Europe (instigated either by European pressure or by the national governments own choice) then 2012, when the first positive economic results had been reported among the strongest/ economically healthiest European countries (in those countries therefore the end of the crises had been announced) and 2014. Based on the large role that in the available literature has been ascribed to the benefits-component, we expected that the support for the European Union has suffered greatly because of the economic and financial crisis. When cutbacks were needed and countries no longer had monetary sovereignty as a tool to battle harsh economic times and the collectively agreed budget rules of the European Union dictated sizeable measures to be taken in order to keep within the agreed budget deficits, suddenly the benefits of being a member of the European Union became less prominent and the downsides gained the upper hand. However we expect a delayed effect because when the crisis hit, after prosperous times, most countries initially had some reserves and alternative measures that they could take in order to support their economies. Later, when the crisis persisted, more radical austerity measures were needed and the effect that followed stretched further than the actual economical end of the crisis (since not only countries, but also individuals had to cut back their expenses and use up their reserves and it takes time to recover from that).

Figure 1; Standard Eurobarometer 82, p. 6

The results provided by the report on the Standard Eurobarometer 82 (2014), as shown in figure 1, which depicts the trend between spring of 2006 and the autumn of 2014 for the aggregated EU mean for the image of the EU. This image shows a bit more volatility than is shown by our data. Nevertheless, the main trend remains the same. In the first period, spring 2006 to spring 2009, there is a steady but downward sloping line of around 47% of the people expressing a positive image. The highest scores in the springs of both 2006 and 2007 (50% and 52% have a positive image) and the lowest scores in the spring of 2009 (only 43% positive). This downward trend is somewhat even with the first reports of the financial and economic crisis that was first reported in 2007 as the United States housing market collapsed and as an outflow the world banking system appeared to be affected via the sub-prime mortgage trading that seemed to be part of the rapid money-making strategy of all main banks. In some countries, the aforementioned austerity measures did not lead to a successful turnaround and those were forced to ask for third party help of the International Monetary Fund and the European Union. This help entailed various bailout programs that were accompanied by sanctions (in the form of rigid reforms of government, tax

16 systems and social security etcetera) which lead us to expect that the second half of 2010 marks a steep drop in appreciation for the European Union (as national politicians can easily blame the EU for the tough measures that need to be taken) in the countries that had to request such bailout. The Dodd–Frank regulatory reforms were instated in the U.S. in early 2010 with the aim to lower the chance of a recurrence of such a large scale crisis to hit (Wall Street Journal, July 16th 2010). These reforms form a landmark in regulating the banking system but they form the start of the strong decrease of the EU-image among its citizens. The results on the same question in the 2011 autumn questionnaire showed a 9 per cent drop; only 31% of the EU citizens had a positive image of the European Union. After this initial drop, over the next two years rates dropped even more: between the spring of 2009 and 2011 the percentage of people who expressed a positive image of the EU dropped from 48% to 31%. Also in this period the amount of people to whom the European Union conjures a negative image changes noticeably, where in the first years the percentages of this group hovered around 16% they have gone up as high as 28% in the autumn of 2011. For the next two and a half year, these ratings remained more or less stable. In this period, the aggregated reported negative image was on average 27.5% against 31.3% positive image. A slight recovery in the percentage of people to whom the EU conjured a positive image was found in the spring of 2014 when the figure for a positive image went up to 35% and this increase continued in the autumn of 2014 where 39% had a positive image. Overall the average neutral rating is 36.7% and it is quite clear in the graph that the lower values (32, 34) were reported in the earlier waves of the Eurobarometer and the highest values (40 and 41) were reported at the peak of the financial and economic crisis in 2010 and 2011. This can be the result of people who are seeing the effects of the crisis around them (and therefore lose their positive image) but are not directly or personally affected by the crisis as such (and therefore do not report a negative image). Of course these last remarks are highly speculative and we hope to shed some light on this with our study.

Next it is important to map the current situation in Europe: How do the European citizens currently feel about the European Union and are there specific groups we can identify within and across countries? As the economic crisis did not only influence national policies in a one dimensional way, but its implications are sizeable on a wide variety of everyday life. Hence there are many ways in which it affects people and for this study we are interested in the outflow of

17 how the crisis itself may be considered to be caused, invigorated or weakened by the European Union and furthermore how the European citizens regard the role of the European Union in handling the economic crisis through the various austerity measures that were forced upon countries, but also the temporary suspension of certain budgetary rules. "The impact of people's value predispositions always depends on whether citizens possess the contextual information needed to translate their values into support for particular policies [...] and the possession of such information can [...] never be taken for granted."(Zaller, 1992, p. 25). He argues here that the support for something which is quite abstract, such as the impact of the European Union on everyday life, is mostly determined by the contextual information that is available about the issue. When national governments are subjected to EU-imposed austerity measures which lead to increasing unemployment rates and cutbacks in social services such as pension plans, citizens who lose certainties (mainly concerning their income) are more likely to have (or develop) a negative attitude towards the European Union as the EU is identified as the governing body that causes them to lose those certainties. In this way political left wing or right wing preference can be seen as a reflection of such attitudes. Consequently, the positioning would be highly similar to the positioning on a political left-right scale, leading to the expectation that the support for more conservative and Eurosceptic political opinions is likely to increase - as the results of the 2014 elections for European Parliament, where Eurosceptic parties such as the Front National, UKIP, Five Star Movement and PVV have secured large victories, show.

H1a; The more one identifies as a left wing voter, the more positive one's image of the European Union.

As we predict in general that the respective left/right wing preference of voters to be connected with their view of the European Union, we also see merit in an analysis of how the left-right ratio has developed over the years. As we anticipate a connection between left or right political preference, it is useful to investigate how the left/right ratio has developed over the years. Is there a tendency towards more polarization along the left/right distinction? We expect that the number of people who identify themselves as left wing voters has decreased over the years, mainly as a result of the financial and economic crisis. As mentioned earlier we study data from 2004 and 2014 and we expect that the anticipated changes in voters’ preference have taken place

18 especially in the years 2008 - 2012, when the outflow of the financial and economic crisis was most prominent. We expect these to be mainly an outflow of the negative effects which the financial and economic crisis caused, as the impact of the crises on everyday life was most paramount in those years. Moreover, the financial sector and system are mainly viewed to be favored by the political right rather than the political left. In the light of what we saw in figure 1 (p. 16) concerning the image of the EU in the later years, we expect a stronger decrease in EU- image of people who identify as left voters compared to right wing voters, similar to what we see in the rise in negative opinions expressed on the image of the European Union between 2004 and 2014.

H1b; The mean EU-image of people who identify as left wing voters has decreased more between 2004 and 2014 as compared to the people who identify as right wing voters.

On the other hand there is Inglehart's interpretation (1977) of Maslow's hierarchy of needs: while scarcity prevails, materialistic goals such as a job to provide an income to be able to pay for food and housing will have priority over post-materialist goals like intellectual satisfaction, esteem, and belonging. Once the satisfaction of the survival needs can be taken for granted, the focus will gradually shift to higher needs, which only come into focus once all the needs that are lower down in the pyramid are mainly or entirely satisfied. This could be a key point in acceptance of a European identity through a mechanism described as ethnic competition theory. This theory states that this ethnic competition, especially at the individual level but also at a group level, actual or perceived, enhances negative sentiments against ethnic out-groups by provoking threats to personal and group interests (Tolsma, Lubbers & Coenders, 2008). With respect to the European Union, this theory can be used to predict how the "four freedoms", as described and anchored in European law, can be perceived on the individual level in the respective EU member states, depending on the extent to which a country is included in the European Economic Area. Those four freedoms are the free movement of goods, persons, services, and capital among the EEA countries. Generally, it are the people who live in lower socio-economical strata that are lower educated, less in touch with cultural transformations in societies and therefore are more likely to be competing for the same socio-economic status as immigrants from other European countries, that are in this position because they are not acquainted with the culture and customs

19 of the country they migrate to (Pardos-Prado, 2011). The recent mass influx of illegal immigrants, mostly fugitives from war and poverty in Africa and the Middle East, for example, causes tension on the social housing market. The people that need to rely on social housing are mostly of lower socio-economic status and therefore directly threatened by the increase in the number of immigrants that need to be taken in according to European agreements. Following these mechanism of competition with migrants from other EU-countries we expect that a lower GDP per capita would increase the relationship between socio-economic status and the image of the European Union.

H2; People in lower social classes will have a more negative image of the European Union.

Also on the individual level, previous research has shown that older people were more supportive of the European Union than younger people (Lubbers, 2008). The older generation was to a greater extent socialized with the original rationale for establishing the EU: to banish war from Europe forever. It would be interesting to see whether the results that Lubbers (2008) found still holds today. Are younger people, when controlled for all other demographic characteristics, less supportive of the European Union? Following the effects of widespread austerity measures, including changes in pensionable age and benefits, the expected effects of this traditionally more positive attitude of older people towards the European Union likely has decreased and therefore resulted in a lesser overall age effect on support for the EU. This expectation is also supported by the high levels unemployment among youth, a phenomenon that is seen across the whole European Union and shows peeks in particularly hard hit countries such as Spain, Italy and Portugal. We expect that over all the austerity measures have diminished the differences between generations and therefore expect that between 2004 and 2014 the effect of age cohorts on support for the European Union has decreased due to the austerity measures following the financial crisis.

H3a; We expect that in the older age groups the average image of the EU is more positive than in the younger groups.

H3b; We expect this difference between the various age groups to have decreased between 2004 and 2014.

The next step would be to address whether there is a rough distinction between rich and poor countries and between countries that have recently joined the European Union versus countries that have experienced a longer period of time as a member state (and therefore have experienced more various economic conditions as part of the European Union). Lubbers and Scheepers (2010) state that people in countries with higher GDP would have turned more skeptical towards the EU after the introduction of the euro, whereas those in countries with lower GDP would have turned less skeptical. Much popular debate on this focuses on the money each country contributes to the EU and whether their country is a net-contributor or a net-receiver. This entails a mainly financial focus on the EU and this also speaks from Lubbers and Scheepers (2010). They focus in their paper on the 2004 European Union enlargement and do so in terms of risk and opportunity and the effect that these terms had on public support for the enlargement. With respect to support of a European identity, their findings translates to a lower level of support for the European Union in countries with a higher GDP per capita. This was mainly because in these countries the EU is perceived as a risk of losing the established level of welfare (both materialistic and moral) rather than an opportunity for enrichment. It is important to note that this sentiment likely has become stronger after the grave measures taken to fight the Euro-crisis that sparked in 2008, which lead to several countries being placed under guardianship in order to implement the necessary reforms. Those reforms meant stern cut-backs and came down hard on the citizens (Greece and Cyprus as two of the most extreme examples) that in turn voiced their discontent in a strong anti-EU manner. Even though this might seem contradictory to Lubbers' and Scheepers' idea, it actually still is concerned with the balance of risk and opportunity, only now the opportunities seemed far away and the cut backs and financial reforms were felt immediately (and severely).

H4a; The lower the GDP in a country, the more positive the image of the European Union.

Alternatively there is the downside of EU-membership which is related to the austerity measures that have been formulated and imposed upon member states that did not have taken proper actions in addressing the financial and economic crisis in terms of reforming government expenditures and keeping a tighter budget towards recovery. In those countries, i.e. Portugal, Spain and especially Greece, we will probably see a steep decrease in the support for the EU

21 following the negative effects that their membership to the EU renders them in terms of i.e. the pensionable age and rising costs of daily goods. We have already observed these trends in the results that flow forth from the analyses on the trend of support for the European Union derived from the data provided by Eurobarometer 82.3 (as it was depicted in figure 1).

Lubbers (2008) found that the identity approach is better suitable to explain Euroscepticism than an economical approach. According to Lubbers (2008), a perceived cultural threat of the European Union on the Dutch national identity was strongly associated with a perceived cultural threat from ethnic minorities. This perception lead to a feeling that the Dutch national identity needed to be preserved, leading to a strong opposition of the treaty to establish a constitution for Europe. In countries that recently joined the European Union, the public perception that Europe provides a raise in welfare standards is largely promoted. This would mean that the European Union brings (or embodies) new opportunities, that could not have emerged in the country had it not became a part of the EU. When, on the other hand, a country has already been a member state of the (various previous forms of the present day) European Union for several decades, the current European identity could have drifted much further from what is considered to be important as the national identity, causing more friction and opposition to accept a European identity that comes with the increasing financial and political integration as the European Union progresses. This is in line with Immerfalls' model with respect to individual support for European integration (Immerfall and Therborn, 2008) in which he points out that perceived benefits provide a large contribution to support for European integration. Following this, countries that have recently joined the European Union would then have a more positive image of the European Union based on larger or more perceived benefits. This would explain away most, if not all, of the effect that we earlier expected to be caused by the standard of living (measured by GDP per capita) would seem to cause.

H4b; Countries that have recently joined the European Union have a more positive image of the European Union.

Next we examine whether (and if so also to what extent) the image of the European Union has been affected by the extra austerity measures that were being issued by the EU to specific member states that had the most severe problems following the effects of the crisis. These are the

22 countries that have received bailout plans to restructure their economy, for which in return the countries had to meet specific demands and targets of reform. Over the course of the years, the European Union has tried to increase support among the European citizens but it seems that these attempts by the EU have been rather unsuccessful. According to Blokker (2012) this lack of success in terms of enhanced social legitimacy has caused protests against austerity programs: "further exacerbated by the recent 'autocratic' tendencies in handling the financial and economic crisis, which have stimulated rather widespread, and sometimes cross-national, protests against imposed austerity programs" (Blokker, 2012, p.2) and therefore was undermining for a positive image of the EU. Naturally we expect that in these member states the feelings towards the European Union and, by extension, the image they have of the EU would have dropped more between 2004 and 2014 than it has in member states that had the control of the austerity measures without having to comply with specific demands from a third party.

H5a; In countries that were forced to issue (specific) extra austerity measures by the European Union, the image of the European Union has decreased more than in countries that were able to control their own programs.

As we will try to establish whether the image of the European Union in countries that were forced to issue extra austerity measures by the IMF and the European Union differs from countries that were able to set their own terms for financial reform and cut backs, it is also interesting to investigate if the personal opinion of people concerning the responsibility that the EU has in the austerity measures. Since the European Union has so many different faces in terms of governing bodies that potentially could be held accountable by citizens because of the sheer complexity of how the EU is structured (the European Parliament, the Euro-Group, the European Central Bank, and the European Council); citizens might not know which body is responsible for which actions. In this respect it is not the case of which body is actually in charge of imposing austerity measures, but who is portrayed to be the one.

H5b; People who consider the EU responsible for the austerity measures in their country will have a more negative image of the European Union. In countries that were forced to take extra austerity measures, this effect will be even stronger.

Following this we use a multi-level analysis where we check for some other cross-level interactions to assess if there are differences by means of context variables. Hereby we will combine the earlier mentioned individual level control variables and context characteristics to determine whether the difference in effect size of for example income on support for a European identity be explained by GDP per capita. Along the earlier introduced interpretation of Maslow by Inglehart (1977), it is likely that people who have a lower life satisfaction will experience a greater threat to their own standard of living as it is perceived to be threatening to their everyday life. In countries with a higher standard of living, this group perceives a bigger threat of their own position and as a result their support for the European Union is lower than in countries with a lower standard of living (where the EU might be even seen as an opportunity to escape poverty and therefore provide an opportunity to greater life satisfaction).

H6a; The lower one's life satisfaction, the more negative the image of the European Union.

H6b; We expect this effect to be stronger in countries with a higher GDP per capita.

Data

In order to determine the support for the EU, we investigate the data provided by two waves of the Eurobarometer. The Eurobarometer is a cross-national longitudinal study designed to identify and compare trends within Europe and is conducted by the Public Opinion Analysis sector of the European Commission. Since 1973, the European Commission has been monitoring the evolution of public opinion in the member states. In the surveys, among other, topics concerning European citizenship, enlargement, personal social situation, environment, the Euro, defense and many other. The units of analysis in this research are both the individual level (in the EU countries) and the aggregated national level. Both are derived from Eurobarometer, waves 62.0 (2004) and 82.3 (2014). Some details about the respective waves of the Eurobarometer that we analyzed can be found in table 1. The data derived from Eurobarometer waves 66.1 and 74.2 is only used for monitoring the development of mean EU-image over the years, which is shown in table 6. Table 1. Used waves of Eurobarometer Wave number Sample size (total) Period of data collection 62.0 29330 October & November 2004 66.1 29152 September & October 2006 74.2 30780 November & December 2010

82.3 33662 November 2014

Data derived from the Eurobarometer, own operations

All the data was retrieved through GESIS and the datasets contain information that has been gathered using structured questionnaires that were administered in over 30 countries or territories (in the respective languages); the 25 European Union Member States, Bulgaria, Romania, Croatia, the five countries - the Former Yugoslav Republic of Macedonia, Turkey, Iceland, Montenegro and Serbia - that are candidate member states (in 2014), and the Turkish Cypriot Community in the part of the country that is not controlled by the government of the Republic of Cyprus. For our studies we selected only the countries that belong to the European Union in 2014. As there had been an admission of eight countries at the first of May 2004, only three countries that are EU members in 2014 had not yet fully joined the EU at the starting point of our analyses. Since these three countries, Bulgaria, Romania and Croatia, were in the process of

25 becoming a member and actually were admitted into the EU before the end point of our analyses we also included these countries, making it a total of 28 EU countries examined both waves of the Eurobarometer that have been analyzed.

The questionnaires for the study were identical in the various member states, apart from minor but unavoidable differences generated by differences in party names and country-specific institutions. The sample size was 1,000 interviews in each EU member state (with a few exemptions, specifics on this are available on the European Commission website, where all public Eurobarometer data is published). In the surveys, the questions mostly contain statements for which the respondent was asked to pick his answer on Likert-scaled rankings. In this manner there where statements about the national government as well as the European Union, for example the next question: "In general, does the European Union conjure up for you a very positive, fairly positive, neutral, fairly negative or very negative image?". Some other questions that are interesting for this research are concerned with the respondent's political interest (not interested at all vs. very interested) and the political (self)-placement (left vs. right, on a ten point scale). Also various topics that could be used as control variables on the individual level, among other: age, gender, religion/religiosity, level of education, current work situation and respondent family’s standard of living. For controlling the context level we used GDP per capita, which we retrieved from EuroStat, duration EU membership (for which we created a dummy variable with three categories) and representation in the EU-parliament. These context variables have been derived from the public websites of the European Union.

Operationalization

In the following section we will address how we have operationalized the various variables for our analyses. First we adjust for over- or under-representation of certain characteristics in the respective samples by applying a weight variable. From the list of available weight variables we chose the weight result from target-variable which is a post stratification weight that allows to analyze the countries separately without the smaller countries being marginalized by countries with a substantially larger population.

Next we will address the assessment of how the support for the European Union has developed from 2004 to 2014. As we have mentioned before we will use the image that the European Union

26 conjures up to its citizens as a support base for our analyses. Respondents are invited to choose between six options very positive, fairly positive, neutral, fairly negative, very negative or don't know. We have recoded the variable in such a way that a higher score indicates a more positive image of the EU and the people who answer don't know or who refuse to answer are excluded from the analyses.

The political orientation of people is taken from self-placement on a 10 point Likert scale. Respondents were asked whether they considered themselves left or right, with respect to political matters. This was done by asking people how they would place themselves on a scale from 1 to 10 with 1 being completely leftist and 10 when they considered themselves completely rightist. For the analyses, people are regarded leftist if they rank themselves anywhere from 1 through 4 on the 10-point Likert-scale, rightist from 7 through 10 and respondents scoring 5, 6 are considered to be neutral. The people who spontaneously refused to answer or answered "don't know" were coded as 98 and 99 respectively and are coded thusly to create two categories that represent the people who refused and who did not know where to place themselves respectively.

Another self-placement was used to determine the socio-economic status of the respondents. Respondents were asked to place themselves and their household on a scale ranging from 1 to 5 with the following categories 1) working class of society, 2) lower middle class, 3) middle class, 4) upper middle class, 5) higher class of society. Four other possible scores were given to respondents who wanted to qualify themselves as: 6) other or in the categories 7) none, 8) refusing to answer the question or 9) those who did not know where to place themselves. We did not include the latter four categories in our analyses, rather we coded them as missing values as they do not contribute a substantial class to our data, as they add up to 916 cases. For the mixed model analyses we recoded the existing variable into dummies representing the five societal classes.

For our analyses we only used respondents of 18 years and older and for further generation analyses six clusters were made. The respondents were grouped into six different cohorts: 18 to 30 years old, 31 to 40, 41 to 50, 51 to 60, 61 to 70 and 70 years and older.

Our measure of life satisfaction is a recoded version of the life satisfaction variable that is present in the questionnaire, which asks the respondent to indicate "on the whole" how satisfied they are with their life on a five point scale. We have recoded it in such a way that a higher score means more life satisfaction. The variable ranges from: 1 (= not at all satisfied) to 4 (= very satisfied) and has 5 (= don't know) as a neutral category.

As control variables we used gender, education level and current occupation. For education level, respondents were divided into four groups: lower education level, middle education level, higher education level and still studying (those respondents are either on the middle or higher education level and had we not included this group we would have lost a large number of respondents). People's current occupation is divided into four categories: employed people, retired people, the unemployed and the last group is the group of 'not working people'. This last group consists of people who are studying and people who reported that housework was their main job (housewives and -men), similar to the socio-economic status we have created dummies in order to be able to add the occupational status to our mixed model analyses.

As both socio-economic status and occupational status are taken from self-placement on a limited scale, we have checked for correlation between the two variables. Cramer's V for the two variables was 0.102 (significant at 0.001), suggesting that there is a small to medium correlation between the two variables. Furthermore a test for correlation resulted in a Pearson correlation value of -0.102 (which is significant at 0.001). However, running several multilevel models where alternately socio-economic status and occupational status had been left out of the model, and one model including both, they did appear to interfere with each other by influencing the significance of the variables. Leaving out socio-economic status but including the occupation variable left us with a large number of statistically not significant variables, whereas the opposite model provided us with more significant results and thus a better view on which variables do affect the EU image. However, the occupation status variable is considered for the 2004 dataset because in 2004 there was no socio-economic status variable available for analyses.

On the aggregated level we have used GDP per capita figures which were derived from Eurostat, a directorate-general with as main responsibilities to provide statistical information to the institutions of the European Union. The figures are collected between 2004 and 2014 and are published on the website of Eurostat (http://ec.europa.eu/eurostat/web/main/home).

With respect to the length of their membership of the European Union, countries are divided into three groups. The 1st group contains the six founding countries (the Netherlands, Belgium, Luxembourg, (West) Germany, Italy and France). The 2nd group contains the nine countries that entered the union between 1973 and 1995 (United Kingdom, Sweden, Spain, Portugal, Austria, Ireland, Greece and Finland). The 3rd group includes the thirteen most recent entrants that joined from 2004 onward (Cyprus, Estonia, Latvia, Lithuania, Poland, Hungary, Bulgaria, Czech Republic, Slovakia, Slovenia, Malta, Romania and Croatia).

For our analysis on the impact of forced austerity measures we divide the countries into three groups: countries that have not been dictated certain (specific) austerity measures by the EU, countries that received third party aid but recovered faster than anticipated and did not need the full credit and lastly those that have been received third party aid for a financial bailout. Countries that were forced to issue extra austerity measures by the European Union are those that had to ask for financial aid by the EU, which entailed that the EU forced added austerity measures as a condition for the financial aid. There are three countries that requested third party help in overcoming the crisis but that recovered more quickly than anticipated (and therefore received only a small amount if any at all since third party help could also consist of a financial guarantee in case the economy would not recover). These countries are Hungary, Latvia and Romania. These countries have received backing by the IMF and additional funding from a EU body to restructure their government debts but recovered faster than anticipated and therefore did not need the full extent of the requested funding and in return did not have to carry out the drafted programs for reforms in full. The five countries that needed the full support (sometimes through subsequent or extended programs) are Cyprus, Greece, Ireland, Portugal and Spain. Countries that were able to set their own terms as to how they would obey rules of the growth and stability pact which dictates that the EU-countries are not allowed to have a budget deficit of over 3 percent in order to avoid sanctions by the EU. Therefore these countries have been categorized as not having any forced austerity measures imposed as the austerity measures they took were completely set by their own respective governments. Data on which countries received the aforementioned support programs is retrieved from the IMF press releases (www.imf.org).

Table 2. Descriptive Statistics wave 82.3 (2014)

N Minimum Maximum Mean Std. Deviation

Individual level

- Image of the European Union 26736 1 5 3.1939 0.92465

- Gender (0=male, 1 = female) 27058 0 1 0.5209 0.49957

- Political orientation 27058 1 4 2.3485 1.03485 (1=left, 2= central, 3=right, 4=refused, 5=d.k.)

- Age groups 27058 1 6 3.2543 1.67537 (1=18y-30y, 2=31y-40y, 3=41y-50y, 4=51y-60y, 5=61y-70y, 6=71and older)

- Education level 26363 1 4 2.3290 0.81268 (1=lower, 2=middle, 3=higher, 4=still studying)

- Occupation 27058 1 4 2.1460 1.29864 (1=working, 2=unemployed, 3=not working, 4=retired)

- Self placement of social class 26142 1 4 2.2561 0.99819 (1=working class, 2=lower-middle, 3=middle 4=upper-middle and higher class)

- Life satisfaction 26995 1 4 2.9476 0.78485

- "The EU is responsible for the 27058 1 5 3.1646 1.00857 austerity programs" (1=totally disagree, 4=totally agree, 5=do not know)

Valid N across variables 25216

Country level

- GDP per capita 28

- Mean EU-image in 2004 28

- EU membership 28 1 3 (1=founding, 2=entered1973-1995, 3=entered since 2004)

- Forced austerity measures 28 0 1 (0=no forced measure, 1=bailout plan)

Data derived from the Eurobarometer, own operations.

The last context variable that we check, is only applicable to our analyses of 2014. It is the average image of the European Union as resulted from our 2004 dataset and this mean score has been added to our 2014 dataset in a similar manner as we did with the GDP per capita variable. We will be able to use this score in order to see how the mean EU-image has changed for each country between 2004 and 2014 and we will be able to add the mean EU-image per country of 2004 in our 2014 mixed level model and we can then determine the degree to which the 2004 EU-image affects the 2014 EU-image.

Table 3. Descriptive Statistics wave 62.0 (2004)

N Minimum Maximum Mean Std. Deviation

Individual level

- Image of the European Union 26599 1 5 3.4293 0.935

- Gender (0=male, 1 = female) 26599 0 1 0.5219 0.500

- Political orientation 24735 1 5 2.31 1.206 (1=left, 2= central, 3=right, 4=refused, 5=d.k.)

- Age groups 26599 1 6 3.2543 1.663 (1=18y-30y, 2=31y-40y, 3=41y-50y, 4=51y-60y, 5=61y-70y, 6=71and older)

- Education level 26359 1 4 2.3290 0.854 (1=lower, 2=middle, 3=higher, 4=still studying)

- Occupation 26497 1 4 2.1460 1.287 (1=working, 2=unemployed, 3=not working, 4=retired)

- Life satisfaction 26475 1 4 2.9476 0.796

Valid N across variables 24309

Country level

- EU membership 28 1 3 (1=founding, 2=entered1973-1995, 3=entered since 2004)

- Forced austerity measures 28 0 1 (0=no forced measure, 1=bailout plan)

Data derived from the Eurobarometer, own operations.

In our analyses we will first look at the data of 2014 and then we will create similar analyses of 2004 and to conclude we will compare the two years with each other in order to determine the differences that may have occurred. Table 2 presents the characteristics of the variables that we used for our analyses on the effects of individual and context level variables in 2014. From this table follows that there are 25216 respondents who have provided sufficient answers for all of the variables in Eurobarometer wave 82.3 that are relevant in our analyses. Table 3 presents us the variables for the 2004 analyses, which are derived from Eurobarometer wave 62.0.

Results

As we have mentioned above, our main analyses are focused on 2014, so we will start with the results of those. After that we will turn our attention to how the situation was in 2004, in many respects a very different time but first and foremost this was prior to the financial and economic crisis, and we will compare the situation in 2004 and 2014 to see whether and to what extent changes have occurred.

2014 results

A quick glance at table 2 learns us that we have, due to the difference in cases that provide an adequate answer for the created variables, a total of 25216 valid cases. When we look at our null model, the random intercepts model, we can estimate the different intercepts per country and the mean differences between those countries. The variance partitioning indicates us how much of the variability on the individual level outcome can be explained by the clustering in countries. Table 3 contains the results from our null model indicating an intra class correlation of 5.85%, which indicates that roughly six per cent of the variability in EU-image can be explained by the

Table 3. Estimates of Covariance Parameters a 95% Confidence Interval Parameter Estimate Wald Z Sig. Low. Bound Up. Bound Residual 0.809524 118.972 .000 0.796296 0.822970 Intercept [subject = country] 0.050293 3.607 .000 0.030315 0.089867

a. Dependent Variable: Image of the European Union. Data derived from Eurobarometer 82.3, own operations. country clustering. This is a rather low intra class correlation, indicating that there is not much variation between the countries; the countries do not differ a great deal from each other with respect to what image their citizens have of the European Union. The intercept as reported in the estimates of fixed effects table of our null model indicates that the average value of EU-image across all countries in our analyses is 3.19, which is average given our five point scale in which 3 was the neutral category.

Including the individual level variables in the next step of our multilevel approach, the results of which are presented in the second column in table 5 (this table can be found on page 40), provides us with a model that can explain 9.10 % of variability in EU-image across countries. The reference mean intercept across all countries is 2.286 and most of the individual level variables have a significant effect. There are two variables that are not significant at the 0.05 level: firstly the influence of the EU austerity variable - which states that the EU is responsible for the austerity measures that the respondents' country's government had to impose - does not provide a statistically significant result in this regression. This means that we have to conclude at this point that the mechanism presumed in our hypothesis 5b is not being supported by the data.

The other not significant variable is one of the three dummy variables for political self- placement: the neutral category (who score an estimate of 0.007) does not significantly differ from the reference group; people who identify as left. Of the other three dummy that were taken into account for this variable, the people on the political right, have a higher estimate of 0.059 which is statistically significant. This means that hypothesis 1a, stating that the EU has a more positive image among left wing voters, is not true; our results indicate that the opposite seems to be the case. The other two groups we identified with respect to political preference consist of (1) the people that did not know and (2) those who did not want to disclose their preference and these groups both have, quite surprisingly, a lower score compared to the politically left group which is statistically significant at 0.05. The people that answered that they did not know where to place themselves on the 1 to 10 scale (1 being most left and 10 being most right in the political spectrum) have on average an EU-image that is 0.046 more negative than politically left people. Moreover, the people who refused to disclose their political preference between left and right, have an even more negative image of the EU with an estimate of -0.105 compared to the reference group. These results indicate that the people who refused to disclose their political preference have the most negative image of the EU among the five identified groups.

Looking at the rest of the variables we can see that most are significant at 0.001, with three exceptions: first there is our gender variable which has a 0.006 significance indicating that women have a less positive image of the EU then men (the difference is 0.029). The second variable that is not significant at the 0.001 level is our second age group which has a -0.056 compared to our youngest age group (which serves as our reference in this model) with a

34 significance of 0.004. The third variable with a slightly higher significance is the aforementioned dummy for people who did not know their political preference on a left-right scale. These three variables are still very significant and fall well within our acceptance (which we have set at 0.05, hence we will accept these results as statistical proof of the group differences that they indicate.

When we look at the education level variable, we see that each higher level holds a more positive image of the EU than the lower categories. The difference between the respondents who are still studying and those who have the lowest education level is with 0.186 the largest difference between the education level categories. However it does not differ too greatly from the group that has completed higher education (who have 0.160 higher than the reference group). This seems in line with recent reports on voting patterns in the British referendum on the EU, where students and higher educated people have predominantly voted for Britain to remain in the EU whereas the majority of the lower educated people has voted for Britain to leave the EU.

Our next variable of interest is the age variable. In contradiction with our hypothesis, our results indicate that each older age group has a less positive image of the EU than the younger cohorts. Apparently the ideas with respect to socialization in a pro-EU era as described by Lubbers (2008) promoting a more positive EU-image among older age groups do not outweigh the present day experience of the EU as a burden rather than a blessing for European citizens. The only exemption is the oldest group, which based on their estimate effect comes between the second and third age group. However, this is, like we have seen when looking at our education level variable, in line with what the voting patterns of the Brexit referendum have shown; young people have voted overwhelmingly pro-EU whereas older people, especially the age cohorts over 50 years old have in majority voted anti-EU.

Self reported social class shows a familiar pattern where the higher one's social class the more positive one's image of the European Union is. Interestingly, the results yield quite big differences between lower, middle and higher classes. The estimates indicate a 0.101, 0.157 and 0.276 difference respectively for the identified social classes compared to the lowest social class, the working class. What we can see is that the step between lower-middle class and middle class is relatively small but between middle class and upper-middle and higher class the estimate difference compared to the reference group (in this case the working class) nearly doubles. We have looked into the difference between upper-middle and higher class, but due to the low

35 number of respondents placing themselves in the higher class (only 185 out of 26142) the results of this analysis do not make any statistical sense. On the whole we can conclude that self reported social class has the biggest impact on EU-image of all the individual level variables we have tested in our analyses.

The last individual level variable we consider in our analysis, is the effect that life satisfaction has on one's image of the European Union. We expected that a the lower life satisfaction would lead to a more negative image of the European Union, caused by the feeling of competition for one's basic needs in life. This hypothesis is strongly supported by our data: with an estimated effect of 0.27 life satisfaction has one of the bigger impacts on EU-image of all our tested variables. This effect became even stronger in our cross level model, when we looked at the interaction between one's life satisfaction and the country GDP per capita. Even though this interaction was not statistically significant (we will come back to this later on), the estimate fixed effect for life satisfaction is 0.31.

Next we look at the proportion of explained variance, the R2, for the individual level: the proportional reduction of residual variance in comparison with the empty model. We can calculate that, compared to our null model, the residual variance has changed 9.10% ((0.8095- 0.7358)/0.8095), which means that after addition of the individual variables 9.10% more of the variation in EU-image can be explained as compared to the null model.

In our next model we introduce our country level variables in the regression: GDP per capita, the length of membership of the EU and whether or not a country had been forced by the EU to take specific austerity measures during the financial crisis provide mixed results in terms of significance. The estimates of the fixed effects of our third model are shown in table 5. Here we have added the country level variables that we have constructed. What immediately stands out is that most of our country level variables are not statistically significant. The variable that has the highest statistically significant impact, is the variable for mean EU-image in 2004: the estimate of 0.538 is significant at the 0.001 level which indicates that this variable has a very strong effect on the EU-image in 2014. The GDP per capita has a statistical significant impact on the EU- image (at p=0.05) but this impact is estimated less than 0.0001 and compared to the other fixed effects negligibly small. The other two country level variables are not significant (respectively

0.882 and 0.194), which means that the negative impact on image of the EU that both variables have, do not prove that the image of the EU actually is affected by either the forced austerity measures or the length of the EU membership.

In sum, we see that the intercept estimate drops from 2.286 in the individual level model to 0.758 in the context level model. The addition of country level variables has accounted for a further 1.528 of the average EU-image score (which is around half of the intercept in the null model) and the biggest impact seems to be resulting from the 'mean EU-image of 2004'-variable. The residual variance change compared to the null model, the R2 on the country level, explains an additional 33.01% residual variance. Again, we emphasize that this high R2 from our context level variables can be ascribed to the impact of the mean EU-image in 2004.

Our next aim in this multilevel analysis is to determine the effects as observed in the fourth and fifth models, when we include a random slopes model and our cross level model in which we examine the interactions between some of our individual and country level variables. The first cross level interaction we investigate is between the self-reported life satisfaction of the respondents and the respective countries' GDP per capita. The second interaction is between the opinion regarding the responsibility of the European Union for the austerity measures and whether or not a country has had to impose forced austerity measures. In order to determine this we added our variables for life satisfaction and EU responsibility for austerity measures in the random slopes model and we included two interaction variables in the cross level model. The last two columns in table 5 show the results of these regressions. The cross level interaction parameters, which are added in our last model, show a change in the significance of two country level variables: our 'Forced austerity'-variable becomes significant (at 0.01), meaning that the estimate fixed effect of 0.269 indicates an important contribution to one's EU-image of whether or not someone lives in a country that had to agree on a bailout deal. The GDP per capita becomes largely insignificant. This might be due to the interaction we investigate between our individual level variable on life satisfaction, this interaction is also not significant. However, since the estimates of both GDP per capita and the interaction variable 'GDP per capita * life satisfaction' are extremely small, it would not matter whether or not these variables are significant. We have to conclude from this that hypothesis 6b is not supported, there appears to

37 be no statistical evidence that a higher GDP per capita makes the effect from life satisfaction on the image of the EU stronger.

The result of the other cross level interaction that we investigate is also quite interesting; when we look at the interaction variable 'EU is responsible for austerity measures * Forced Austerity' we find a significant result, indicating that people who think that the EU is responsible for the austerity measures, view the EU negative as a governing body. This is not so much striking in the sense that it is a quite straight forward reasoning. The same goes that this would be stronger for citizens from countries who have been forced to take a specific set of austerity measures as a part of the third party bailout program, since these programs usually entail very direct cut backs in social services and welfare. Moreover, national politicians would emphasize that these austerity measures are forced upon them, that they do not employ them because they want to, rather because they have to in order to save the nation from going bankrupt (and losing all public and private savings in national banks in the process). But it stands out because these two variables did not provide statistically significant results when they were tested in the earlier individual level and country level models. This means that only the combination of both living in a country that had to accept forced austerity measures and believing that the EU is responsible for the imposed austerity measures leads to a more negative image of the European Union. From our random slopes model we can see that for life satisfaction the correlation between random intercept and the random slope is negative and significant, meaning that it is a so called 'finding in' effect; the effects will converge. For EU austerity on the other hand, the negative correlation between the random intercept and the random slope is not significant.

Table 4. Model fit comparison individual level country level random slopes cross level null model model model model interaction model -2LogLikelihood 74544.613 67732.767 67713.891 67343.171 67335.040 Δ-2LogLikelihood 6811.846 18.876 370.720 8.131

Data derived from Eurobarometer 82.3, own operations.

The random slope for both variables is statistically significant which shows that there is variation across countries in EU-image. When we look at the R2 of the slope for the cross level interaction

38 variable 'EU is responsible for austerity measures * Forced Austerity' - has an R2 of 24.05%, indicating that we can explain almost a quarter of the variance in the 'EU is responsible for austerity'-variable by the interaction with the country level variable on forced austerity measures. The R2 for the slope of our interaction between life satisfaction and GDP per capita is 4.76% meaning that 4,76% of life satisfaction can be explained by the interaction with GDP per capita. However we saw that this interaction is not statistically significant and therefore we cannot draw this conclusion.

Lastly we perform a check to determine which model provides the best fit for predicting the EU- image. To do this we look at the deviance of each model and compare them to each other, the results of this are available in table 4. The -2LogLikelihood tells us the deviance for each model and we can see that each next model has a lower -2Log Likelihood value. These values are used as Chi-square values and from the Chi-square distribution table of Wonnacott and Wonnacott (1982, p.352) we can derive that all these values are significant. We can thus conclude that each model provides a better fit for predicting the EU-image than the previous one: leading us to conclude that our cross level interaction model provides the best fit.

Table 5. Multi-level models on EU-image in 2014 EU-image Null model Individual level model Country level Random Slopes model Cross-level model Estimate S.E b S.E b S.E b S.E b S.E Intercept 3.188 *** 0.043 2.2862 *** 0.0606 0.7582 *** 0.4881 1.0539 *** 0.1988 0.8275 *** 0.4328 Gender (0= male 1=female) - 0.0290 *** 0.0106 - 0.0289 *** 0.0106 - 0.0296 *** 0.0105 - 0.0297 *** 0.0105 Political orientation (reference=left) Neutral 0.0067 *** 0.0139 0.0070 *** 0.0139 0.0028 *** 0.0138 0.0027 *** 0.0138 Right 0.0588 *** 0.0156 0.0590 *** 0.0156 0.0565 *** 0.0156 0.0566 *** 0.0155 Refused - 0.1050 *** 0.0228 - 0.1063 *** 0.0228 - 0.1173 *** 0.0226 - 0.1172 *** 0.0226 Don't know - 0.0461 *** 0.0210 - 0.0455 *** 0.0210 - 0.0558 *** 0.0209 - 0.0557 *** 0.0209 Level of education (reference=lower) Middle 0.0584 *** 0.0173 0.0588 *** 0.0173 0.0542 *** 0.0173 0.0539 *** 0.0173 Higher 0.1609 *** 0.0193 0.1617 *** 0.0193 0.1607 *** 0.0192 0.1605 *** 0.0192 Still Studying 0.1869 *** 0.0306 0.1875 *** 0.0306 0.1836 *** 0.0304 0.1834 *** 0.0304 Age group (reference=18y-30y) 31y - 40y - 0.0558 *** 0.0193 - 0.0558 *** 0.0193 - 0.0540 *** 0.0192 - 0.0539 *** 0.0192 41y - 50y - 0.0834 *** 0.0192 - 0.0835 *** 0.0192 - 0.0817 *** 0.0191 - 0.0814 *** 0.0191 51y - 60y - 0.0964 *** 0.0195 - 0.0964 *** 0.0195 - 0.0969 *** 0.0194 - 0.0964 *** 0.0194 61y - 70y - 0.1213 *** 0.0200 - 0.1211 *** 0.0200 - 0.1179 *** 0.0199 - 0.1174 *** 0.0199 71y and older - 0.0746 *** 0.0219 - 0.0744 *** 0.0219 - 0.0709 *** 0.0218 - 0.0703 *** 0.0218 Socio-economic status (reference=lower class) Lower-middle class 0.1015 *** 0.0162 0.1015 *** 0.0162 0.1005 *** 0.0161 0.1005 *** 0.0161 Middle class 0.1570 *** 0.0138 0.1569 *** 0.0138 0.1581 *** 0.0137 0.1581 *** 0.0137 Upper-middle & higher class 0.2760 *** 0.0235 0.2760 *** 0.0235 0.2739 *** 0.0234 0.2746 *** 0.0233 Life satisfaction 0.2724 *** 0.0078 0.2732 *** 0.0078 0.2507 *** 0.0202 0.3061 *** 0.0524 EU responsible for austerity - 0.0033 *** 0.0054 - 0.0034 *** 0.0054 - 0.0079 *** 0.0199 0.0235 *** 0.0204 Forced austerity measures - 0.1105 *** 0.0865 0.0766 *** 0.0923 0.2690 *** 0.0971 Length EU membership - 0.0138 *** 0.0379 - 0.0558 *** 0.0394 - 0.0563 *** 0.0347 GDP per capita - 0.0000 *** 0.0000 - 0.0000 *** 0.0000 - 0.0000 *** 0.0000 Mean EU image 2004 0.5378 *** 0.1321 0.4796 *** 0.1062 0.4795 *** 0.1063 Life satisfaction*GDP per capita - 0.0000 *** 0.0000 EU resp. for austerity*Forced Measures - 0.1094 *** 0.0382 Residual 0.809524 *** 0.735848 *** 0.735817 *** 0.722616 *** 0.722613 *** Intercept 0.050293 *** 0.064218 *** 0.033693 *** 0.138950 *** 0.116196 *** Slope Life satisfaction 0.009106 *** 0.008673 *** Slope EU austerity 0.009845 *** 0.007477 *** R2 individual level 9.10 % 9.10% 10.74 % 10.74 % R2 country level - 27.69% 33.01 % -176.28 % - 131.04 % R2 Slope Life satisfaction 4.76 % R2 Slope EU austerity 24.05 % Intra class correlation 5.85 % 8.03 % 4.38 % 16.13 % 13.85 % Data derived from Eurobarometer 82.3, own operations. N= 25216, * p< 0.05, ** p<0.01, ***p<0.001

Table 6. Mean EU-image comparison In table 6 we see the mean EU-

EU image Mean 2004 Mean 2006 Mean 2010 Mean 2014 image scores per country for the Austria 3.0645 2.8742 2.8805 2.8888 years 2004, 2006, 2010 and 2014. On Belgium 3.5951 3.5277 3.3238 3.1910 the whole we can see that these mean Bulgaria 3.8438 3.6848 3.6041 3.3749 scores have dropped between 2004 Croatia 3.1607 3.0432 3.0420 3.2875 and 2014, and the biggest drop has Cyprus 3.3261 3.5961 3.1812 2.7632 Czech Republic 3.2101 3.2831 3.0556 3.0914 occurred between 2006 and 2010. Denmark 3.1999 3.3415 3.1812 3.2452 There are some exceptions to this Estonia 3.2788 3.4072 3.2854 3.3657 trend: in Croatia, Denmark, Estonia, Finland 3.1490 3.1337 2.9981 3.1206 Lithuania, Malta, Poland and Sweden France 3.4164 3.3132 3.1540 3.1406 there is a slight increase in mean EU- Germany 3.3176 3.3277 3.0997 3.1510 Great Britain 3.0099 2.8973 2.6942 2.8829 image. Since none of these countries Greece 3.6153 3.3978 2.8970 2.6471 have had to impose forced austerity Hungary 3.3435 3.1722 3.2347 3.1899 measures as a result from receiving Ireland 4.0395 3.7302 3.3318 3.4167 third party bailout support we can Italy 3.7273 3.4051 3.4672 3.0131 assume that in these countries the Latvia 3.2490 3.1139 3.0823 3.2510 Lithuania 3.7564 3.4809 3.4245 3.4495 financial and economic crisis has not Luxembourg 3.6338 3.4404 3.3507 3.3976 had the same impact as for example Malta 3.3453 3.5891 3.3192 3.4659 in Cyprus, Ireland or Greece. If we Netherlands 3.2716 3.2747 3.1765 3.0447 look at Great Britain, the interesting Poland 3.4243 3.5797 3.5729 3.6164 thing in light of their recent Brexit Portugal 3.5415 3.4790 3.2115 3.0809 Romania 4.0746 3.7509 3.4085 3.6058 referendum result, resulting in a Slovakia 3.4440 3.4416 3.4384 3.1581 majority of the votes for Great Slovenia 3.7548 3.6056 3.2728 3.2137 Britain leaving the EU, is that Spain 3.7635 3.6200 3.1770 3.0786 between 2010 and 2014 the mean Sweden 3.0454 3.1545 3.0814 3.1814 EU-image has actually risen to the EU-mean 3.4210 3.3694 3.1985 3.1939 Data derived from Eurobarometer 62.0, 66.1, 74.2 and 82.3, own operations. 2006 level.

A thing worth noting is that for the 2004 and 2006 moments of measurement, Romania, Bulgaria and Croatia were not yet an EU member. The first two entered as a full member state in 2007 whereas Croatia only entered the EU as of July 1st 2013. This means that the difference in mean

EU-image between 2004 and 2014 for these countries cannot be compared in the same way as for the other countries.

Looking at the countries that have had to impose forced austerity measures (Ireland, Greece, Spain, Portugal, Cyprus, Hungary, Latvia and Romania) we can see that there is a steady drop in mean EU-image between 2004 and 2014 resulting in at least half a point, where the mean EU- image only Hungary and Latvia remains more or less on the same level, which can be explained by the fact that these countries did not have to use the full bailout program and as a result of this also had greater liberty to choose their own austerity programs. This drop in mean EU-image seems to occur somewhere between 2006 and 2010, varying across countries, most likely according to their economic situation; when the financial and economic crisis became noticeable in everyday life.

2004 results

In the next section we will lay our 2014 analysis models over the 2004 data, in order to get an image of how the situation was prior to the financial and economic crisis. We will discuss the results and where possible we will compare the 2004 and 2014 results to see if any interesting changes between 2004 and 2014 will emerge from the data. In table 6 we show the results for the multi level models for 2004. Note that we have not included the variable for self reported social class as this variable was not available prior to the 2014 waves of the Eurobarometer. We did include the occupational status variable in order to provide a better and more complete view of 2004. First we look at the -2 Log Likelihood in order to check which model provides the best fit, data for this is presented in table 6.

Table 7. Model fit comparison individual level country level random slopes cross level null model model model model interaction model -2LogLikelihood 73809.881 70753.697 70745.045 70706.111 70705.889 Δ-2LogLikelihood 3056.184 8.652 38.934 0.222

Data derived from Eurobarometer 82.3, own operations.

From the information criteria we learn that each model provides a better fit for predicting the EU-image than the previous one: leading us to conclude that our cross level interaction model provides the best fit. Next we turn to the fixed effects of our multi level analyses, which are reported in table 7. The intercept as reported in the our null model for 2004 for EU is 3.43, which is 0.24 higher on average compared to the 2014 results.

In our second model we include the individual level variables in our multi level analysis, which provides us with a model that can explain 6.19 % of variability in EU-image across countries. The reference mean intercept across all countries is 2.760 and again most of the individual level variables have a significant effect. In this model there are four variables that are not significant at the 0.05 level: firstly two of our dummy variables for age groups - the group 31 to 40 years old and the group 51 to 60 years old - do not provide a statistically significant result in this regression. The other two not significant results are from our occupational status variable, which we anticipated as this variable also did not provide significant results in our 2014 analyses (and it interfered with our social class variable).

All of the dummy variables for political self-placement are significant at 0.01 and we can see that compared to the left wing voters (who are the reference category) the neutral and right wing category have a more positive image of the EU and these results are significant. This means that also for 2004 hypothesis 1a, stating that the EU has a more positive image among left wing voters, is not true; our results indicate that the left wing voters have a less positive image of the EU. The difference between the effect of left and right voters in 2004 is 0.115 whereas this difference is only 0.059 in 2014, so we can conclude that between 2004 and 2014 the difference between left and right voters in terms of image of the EU has been cut in half. In other words between 2004 and 2014 people who identify as right wing voters, when compared to left voters, have had a larger decrease in image of the EU. This is contrary of what we hypothesized in hypothesis 1b, where we expected a higher decrease of EU-image among left wing voters as compared to right wing voters and therefore we must conclude that hypothesis 1b is not supported by the data.

The other two dummies that we have created that were taken into account for this variable, the people that did not know and those who did not want to disclose their preference and these groups both have a lower score compared to the politically left group, similar to what we have

43 seen in 2014 and these effects are statistically significant at 0.001. The people that answered that they did not know where to place themselves on the 1 to 10 scale (1 being most left and 10 being most right in the political spectrum) have on average an EU-image that is 0.117 more negative than politically left people. Moreover, the people who refused to disclose their political preference between left and right, have an even more negative image of the EU with an estimate of -0.131 compared to the reference group. These results indicate that the people who refused to disclose their political preference already had the most negative image of the EU among the five identified groups in 2004, but the effect of the 'I do not know'-group has decreased even stronger than the right wing voters. We conclude from this that the 'I do not know'-group has become much more diffuse between 2004 and 2014; more people are unsure of how they can identify politically and the image of the EU for this people is less affected by their political preference.

Looking at the level of education, we can see that the change in effect from the reference category (lowest level of education) to the middle category and the step from middle to higher education are both roughly 0.15, meaning that these steps are more or less equal. The effect of the last dummy, for respondents who are still studying, falls between the other two so we can assume that this group will be a mixture of people who are in middle and in higher education (the lower education level group is not relevant here since we only selected people who are 18 years or older, and these people are, if they are still studying, automatically in middle or higher education (the group we identify as lower education ends education before the age of 18).

For our next variable, the age groups provide us with less significant results and lower differences between the groups when we compare the 2004 results with the results for 2014. This means that we have to conclude that hypothesis 3b is not supported by the data, in fact the opposite of what we hypothesized is true: the differences between the age groups in 2014 are bigger than those in 2004. What does remain similar between 2004 and 2014 is that all the age groups have a negative fixed effect, leading us to conclude that when compared with the youngest group (18 to 30 years old) all older age groups have a less positive image of the EU. This is in contradiction with the cited literature and therefore provides an interesting contribution to the understanding of how age and the appreciation of the EU are correlated.

Table 8. Multi-level models on EU-image in 2004 EU-image Null model Individual level model Country level Random Slopes model Cross-level model Estimate S.E b S.E b S.E b S.E b S.E Intercept 3.434 *** 0.0511 2.7596 *** 0.0701 3.2051 *** 0.3799 3.2098 *** 0.1988 3.1662 *** 0.3866 Gender (0= male 1=female) - 0.0860 *** 0.0108 - 0.0861 *** 0.0108 - 0.0848 *** 0.0105 - 0.0847 *** 0.0108 Political orientation (reference=left) Neutral 0.0363 *** 0.0136 0.0363 *** 0.0136 0.0366 *** 0.0138 0.0366 *** 0.0136 Right 0.1148 *** 0.0158 0.1147 *** 0.0157 0.1145 *** 0.0156 0.1145 *** 0.0157 Refused - 0.1307 *** 0.0228 - 0.1309 *** 0.0228 - 0.1308 *** 0.0226 - 0.1309 *** 0.0228 Don't know - 0.1175 *** 0.0195 - 0.1173 *** 0.0195 - 0.1174 *** 0.0209 - 0.1175 *** 0.0195 Level of education (reference=lower) Middle 0.1533 *** 0.0152 0.1536 *** 0.0152 0.1529 *** 0.0173 0.1530 *** 0.0152 Higher 0.2754 *** 0.0168 0.2757 *** 0.0168 0.2732 *** 0.0192 0.2732 *** 0.0168 Still Studying 0.1948 *** 0.0286 0.1946 *** 0.0286 0.1950 *** 0.0304 0.1950 *** 0.0286 Age group (reference=18y-30y) 31y - 40y - 0.0318 *** 0.0174 - 0.0317 *** 0.0174 - 0.0304 *** 0.0192 - 0.0303 *** 0.0174 41y - 50y - 0.0468 *** 0.0179 - 0.0466 *** 0.0179 - 0.0460 *** 0.0191 - 0.0458 *** 0.0179 51y - 60y - 0.0363 *** 0.0190 - 0.0361 *** 0.0190 - 0.0368 *** 0.0194 - 0.0366 *** 0.0190 61y - 70y - 0.0556 *** 0.0239 - 0.0555 *** 0.0239 - 0.0547 *** 0.0199 - 0.0545 *** 0.0239 71y and older - 0.0534 *** 0.0263 - 0.0532 *** 0.0263 - 0.0517 *** 0.0218 - 0.0515 *** 0.0263 Occupational status

(reference = not working) Working - 0.0255 *** 0.0187 - 0.0258 *** 0.0187 - 0.0224 *** 0.0187 - 0.0224 *** 0.0187 Unemployed - 0.0611 *** 0.0257 - 0.0613 *** 0.0257 - 0.0563 *** 0.0257 - 0.0563 *** 0.0257 Retired - 0.0106 *** 0.0228 - 0.0108 *** 0.0228 - 0.0089 *** 0.0228 - 0.0089 *** 0.0228 Life satisfaction 0.2072 *** 0.0075 0.2076 *** 0.0075 0.2507 *** 0.0143 0.2226 *** 0.0347 Forced Austerity measures 0.2886 *** 0.1193 0.2820 *** 0.1170 0.2830 *** 0.0971 Length EU membership - 0.1035 *** 0.1014 - 0.1054 *** 0.0996 - 0.1049 *** 0.0347 GDP per capita - 0.0000 *** 0.0000 - 0.0000 *** 0.0000 - 0.0000 *** 0.0000 Life satisfaction*GDP per capita - 0.0000 *** 0.0000 Residual 0.795882 *** 0.746590 *** 0.746590 *** 0.744609 *** 0.744605 *** Intercept 0.072218 *** 0.104805 *** 0.076954 *** 0.101086 *** 0.101820 *** Slope (life satisfaction) 0.004047 *** 0.004009 *** R2 individual level 6.19 % 6.19 % 6.44 % 6.44 % R2 country level - 45.12 % - 6.56% - 39.97 % - 40.99% R2 Slope 0.94 % Intra class correlation 8.32 % Data derived from Eurobarometer 62.0, own operations. N= 24309, * p< 0.05, ** p<0.01, ***p<0.001

We will only briefly discuss the results of the effect of the occupational status on EU-image, since we only test this variable in 2004 and therefore we will not be able to compare the results with 2014. Moreover does only the dummy for the group of unemployed respondents a statistically significant result (at 0.05) indicating that the unemployed respondents on average have a 0.06 less positive image of the EU when compared to the group of not working respondents.

Next we turn to our country level variables: although in 2004 there have not yet been any countries who had to draw on third party (IMF and EU funded) bailouts, it is interesting to see that precisely this variable provides the only significant result. The group of eight countries that later on had to be bailed out had a more positive image of the EU than the other countries: a fixed effect of 0.29 in the country level model. We can assume that these countries were reaping the benefits of their economical positions in the EU, which only later on, when the financial and economic crisis arose, became problematic and leaving them sliding down the slippery slope of financial instability towards the worst case scenario: national bankruptcy. Of course this is speculation that we cannot substantiate; further in depth analysis should be done in order to investigate this.

The other context level variables (length of EU membership and GDP per capita) did not provide significant results. The residual variance change compared to the null model, the R2 on the country level, explains an additional -6.56% ((0.0722-0.0769)/0.0722) residual variance resulting from adding our context level variables. This indicates that there is a reversed composition effect: when we account for country level variables, the differences do not get smaller, they are getting larger.

The interaction component where we interact the individual level variable for life satisfaction with the country level variable for GDP per capita, does not provide a significant result. Leading us to conclude that, similar to our 2014 analysis, there is no connection between one's individual life satisfaction and the country's GDP per capita.

Conclusion and discussion

In this thesis we have investigated what kind of image the European Union conjures up among EU citizens along a five point Likert scale ranging from very positive to very negative and we have analyzed the impact that various individual level and country level variables have on this image using a multilevel model.

On the whole we can conclude that there is a reversed composition effect; when country level variables are added to the analysis, the differences in EU-image do not get smaller, they are getting bigger. This is a peculiar thing as it was expected that the differences between respondents from different countries would decrease when we corrected for country level differences (for example the differences between richer and poorer countries) based on the rather large country level differences between the various European Union member states.

Looking at the analyzed variables, we see that the 'mean EU-image in 2004'-variable has a huge impact on the 2014 EU-image result. It is reassuring to find this to be the case as this implies that the results in EU-image have a steady basis of what is intended to be measured actually is being measured. In the next section we will briefly go over what the results mean for each of our hypotheses.

When testing our first hypothesis, we saw that the left versus right antithesis does not strongly affect the image of the European Union. This is due to the fact that the Euro-skeptic parties across European countries often combine left- and rightwing positions. We see this mainly in the populist parties such as the Dutch PVV and the Italian Five Star Movement or the Spanish Podemos party. We should note that this issue became more widespread across European countries, making it increasingly harder to use the political left - right antithesis to compare countries. This entails that for future research the left - right political preference of respondents will have less connection with whether or not they regard the EU as positive, making it a less relevant variable for investigating the direct connection between political preference and EU attitudes. An interesting approach for future research on this is to investigate other dimensions of political affinity: positioning on a European integration scale or a dimension that connects national parties along their affiliation in the European Parliament. Unfortunately such data was

47 not available in the Eurobarometer data, which made it impossible to include alternative political interest variables in this study. Another aspect we can conclude from our political placement variable, is that the 'I do not know'-group has become much more diffuse between 2004 and 2014; more people are unsure on how they can identify themselves politically. The image of the EU for these people is less affected by their political preference. This is something that can point towards a less homogenous group, where the positive and negative images can cancel each other out resulting in the lower impact of this dummy variable on our EU-image variable in 2014. This is something that is also interesting for future research.

For hypothesis two, stating that the higher one's social class the more positive the image one has of the EU, the results of the analysis with respect to this variable clearly supports the hypothesis. With each step to a higher social class, the estimated effect of that new social class on EU-image has increased by a clear margin and each step is statistically significant. As we have mentioned the respondents' self reported social class has the biggest impact on EU-image of all the individual level variables that we have included in our analyses. In similar fashion, the pattern that emerged for our control variable on level of education effects corresponded with the results of the recent Brexit referendum voting analyses which showed a bigger support for staying in the EU among higher the educated.

Next we have investigated six age groups and we found evidence which contradicted our hypotheses. Our results indicate that, opposite to what we saw in earlier research, older age groups have less positive EU-image than the younger groups. These results are, again, in line with what we have seen in the Brexit referendum in Great Britain, where voting analyses indicated that the older age groups tended to vote for Great Britain to leave the EU while among the younger age groups the percentage of people who voted for staying in the EU was higher. Hypothesis 3b is also not supported by the data. The exact opposite of what we hypothesized appeared to be the case: the differences in EU-image between the age groups in 2014 are bigger than those in 2004. These results indicate that there is a basis for further research specifically with respect to age and educational level differences.

Our fourth hypothesis was concerned with the impact of two country level variables, a countries' GDP per capita and the duration of their membership to the European Union, on the EU-image. Even though the GDP per capita has a significant result in the country level model, this result is

48 not substantial support as in the model with cross level interactions the impact of GDP per capita it has disappeared. We therefore conclude that both hypotheses are not confirmed. It is likely that this small impact of GDP per capita is in line with the reversed composition effect that was concluded at the beginning of this chapter.

Next up are the results for the third country level variable, the division between countries that had to accept specific austerity measures as part of a financial bailout program and countries that were able to weather the storm of financial restructuring on their own. The data shows that there is no statistically significant effect when checking for individual and country level variables, however, in our cross level interaction model the country level variable for forced austerity measures does become significant and therefore we have to conclude that hypothesis 5a is only partly supported by the data.

Interesting was to see that this context level variable that divides the EU countries into two groups on the basis of whether or not they have received financial support in battling the financial and economic crisis (and as a result were forced to impose certain austerity measures) also provided significant results in the 2004 dataset. We can only assume that these countries were reaping the benefits of their economical positions in the EU. Something which only later on, when the financial and economic crisis arose, became problematic and leaving them sliding down the slippery slope of financial instability towards the worst case scenario: national bankruptcy. However, this is of course speculation that we cannot substantiate; further in depth analysis should be done in order to investigate this. The 2014 results show that in a similar country level model the group of countries that had to impose forced austerity measures, had a more negative EU-image than the countries who have not been bailed out. Of course, the reason for this to occur in our data is mere speculation that we cannot substantiate; further in depth analysis should be done in order to investigate this finding.

Our cross level interaction model resulted in both confirmation and rejection. Our first interaction variable, the interaction between life satisfaction and GDP per capita, resulted in a statistically not significant estimated effect. We therefore conclude that hypothesis 6b is not supported: there appears to be no influence of the country level variable GDP per capita on the effect of the individual level variable life satisfaction has on one's image of the EU. The second interaction variable, on the contrary, did provide a statistically significant result. This means that

49 the combination of both living in a country that had to accept forced austerity measures and believing that the EU is responsible for the imposed austerity measures leads to a more negative image of the European Union. This result supports our hypothesis 5b.

Finally we look at hypothesis 6a; the lower one's life satisfaction, the more negative the image of the European Union. This hypothesis is strongly supported by our data: life satisfaction is one of the variables with the biggest impact on EU-image of all tested variables.

Another aspect of our study we need to point out is concerned with the comparison between 2004 and 2014. Unfortunately not all variables that we investigated were available in both years, which gave us two different sets of variables for our analyses and this entails that we cannot simply compare the results of both multi-level analyses. We can look at the variables that are present in both datasets but we will have to keep in mind that the absence of certain variables and the presence of some other variables will impact the way in which our variables affect the image of the European Union. It is encouraging to see that over the pas years the Eurobarometer datasets are comprised of the same standard variables, including extra sections rather than changing existing variables. This makes it more suitable for examining evolution of a certain variable across various waves.

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