Download Economic voting and the Great Recession in Europe

Economic voting and the Great
Recession in Europe
A comparative study of twenty-five countries
Troy Alexander Cruickshank
August 2016
A thesis submitted for the degree of Doctor of Philosophy of
The Australian National University
© Copyright by Troy Alexander Cruickshank 2016
Declaration
This thesis is entirely my own work and has not previously been submitted for a degree or
diploma in any university. To the best of my knowledge and belief, the thesis contains no
material previously published or written by another person except where due reference is
made in the text.
Troy Cruickshank
i
Acknowledgements
I would like to thank first and foremost my supervisor Ian McAllister. Your advice to me
has always proven sound, whether relating to my thesis specifically or to academic life more
generally. I much appreciate your willingness to meet with me or read my work on short notice
throughout my candidature. I would also like to thank the other members of my advisory
panel, Juliet Pietsch, who frequently made herself available to discuss my work and whose
advice is also much appreciated, and Andrew Banfield, who stepped up at the last minute to
fill a formal vacancy.
I would also like to thank the other people who have taken the time to discuss my work with
me at various points throughout my thesis. These include, in no particular order, Hans-Dieter
Klingemann, Jeffrey Karp, Jill Sheppard, Richard Johnston, Shawn Treier, Timothy Hellwig
and Yusaku Horiuchi. Finally, I would like to thank my friends and family who have supported
me in countless ways throughout this process. You know who you are.
iii
Abstract
The Great Recession of 2007–09 was the worst global economic crisis since the Great Depression of the 1930s. The effects were felt across most of the developed world and Europe was
no exception. In many European countries, austerity programmes were implemented in response to the recession, which were often deeply unpopular. Many governments lost power
in the years following the recession, with sometimes strikingly harsh swings against them.
One notable example was the Irish election of 2011, in which the incumbent Fianna Fáil was
reduced from 71 to 20 seats, by far its worst result at any general election since independence
in 1922. This is congruent with the theory of economic voting, according to which voters will
remove from office governments that fail to deliver economic prosperity. Although there is an
enormous empirical literature supporting this theory, almost all of this evidence pertains to the
typical boom and bust cycle of individual countries and little is known about economic voting
during a severe global recession. The Irish result could have been indicative of the usual economic vote, a bolstered economic vote due to the unusual scale and severity of the crisis, or of
dissatisfaction with the government’s handling of the crisis. This thesis investigates whether
the usual economic vote in European countries was altered during the Great Recession.
This thesis uses survey data from the 2004, 2009 and 2014 waves of the European Election
Studies (EES) to compare the economic vote in 25 European countries before, during and after
the Great Recession. Multilevel methods are used to model voters’ support for the parties they
could vote for at general elections in their own countries. Using this method, the results show
that the economic vote was weaker during the crisis than it was either before or after. In order
to explain these results, I analyse which parties voters tended to prefer after the crisis and
how attitudes towards the European Union evolved over time. The results find that there was
a shift away from centrist and pro-European parties towards radical and Eurosceptic parties
following the crisis. In addition, support for the EU fell over the same time period and voters
were increasingly likely to hold the EU responsible for economic conditions. Given the timing
v
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of these shifts as well as the association between European institutions and austerity policies,
these findings suggest that the austerity programmes implemented in the wake of the crisis
may have been a stronger catalyst for economic voting in Europe than the Great Recession
itself.
Contents
Introduction
1
1 Theory of economic voting: how economic conditions shape the vote
9
1.1
What is economic voting? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
10
1.2
Perceptions or indicators: the link between the economy and the vote . . . . .
13
1.3
Sociotropic voting: do voters only care about themselves? . . . . . . . . . . . . .
16
1.4
Prospective and retrospective voting . . . . . . . . . . . . . . . . . . . . . . . . . .
18
1.5
The vote choice process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
21
1.6
Reward and punishment: the logic of sanction . . . . . . . . . . . . . . . . . . . .
24
1.7
Competent government: the logic of selection . . . . . . . . . . . . . . . . . . . .
26
1.8
Political context and the instability problem . . . . . . . . . . . . . . . . . . . . . .
29
1.9
The Great Recession . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
31
1.10 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
36
2 Measuring the economic vote
41
2.1
Data sources and timing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
42
2.2
Measurement and variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
48
2.3
Method of analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
57
2.4
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
62
3 Voting in a time of crisis: how the Great Recession affected the economic vote
63
3.1
Party support theory of economic voting . . . . . . . . . . . . . . . . . . . . . . . .
65
3.2
Hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
67
3.3
Measuring the economic vote . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
68
3.4
A spatial model of party support . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
70
3.5
The prime minister’s party and the economic vote . . . . . . . . . . . . . . . . . .
73
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CONTENTS
3.6
A multiparty model of the economic vote . . . . . . . . . . . . . . . . . . . . . . .
78
3.7
Influence of the economy on mean party support . . . . . . . . . . . . . . . . . .
84
3.8
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
86
4 Clarity of responsibility during a global recession
89
4.1
Clarity of responsibility: economic voting in different contexts . . . . . . . . . .
90
4.2
The dimensions of clarity of responsibility . . . . . . . . . . . . . . . . . . . . . . .
94
4.3
Hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
97
4.4
How clarity affects the prime minister’s party . . . . . . . . . . . . . . . . . . . . .
99
4.5
The effect of clarity on other parties . . . . . . . . . . . . . . . . . . . . . . . . . . 105
4.6
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
5 Extreme and Eurosceptic parties: the changing policy preferences of European
voters
113
5.1
Hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
5.2
Classifying parties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
5.3
How party position relates to voter support . . . . . . . . . . . . . . . . . . . . . . 124
5.4
The changing fortunes of pro-European integration parties . . . . . . . . . . . . 127
5.5
Left–right position: a shift to the extremes . . . . . . . . . . . . . . . . . . . . . . 131
5.6
Beyond left and right: social and economic dimensions . . . . . . . . . . . . . . 134
5.7
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140
6 Economic abstention: turnout intention in the face of economic pessimism
143
6.1
Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144
6.2
Hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148
6.3
Measuring turnout intention . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
6.4
An economic model of turnout . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153
6.5
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158
7 Attitudes towards European integration and institutions
161
7.1
Austerity and the European Union . . . . . . . . . . . . . . . . . . . . . . . . . . . 162
7.2
Measuring attitudes towards the European Union . . . . . . . . . . . . . . . . . . 164
7.3
Hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168
7.4
Attitudes towards further European integration . . . . . . . . . . . . . . . . . . . 170
7.5
Popular support for EU membership . . . . . . . . . . . . . . . . . . . . . . . . . . 177
CONTENTS
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7.6
Attribution of responsibility for the economy . . . . . . . . . . . . . . . . . . . . . 183
7.7
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187
8 Conclusion: revising theories of economic voting
191
A Countries and parties
199
B Coefficient tables
213
References
237
List of figures
1.1
Basic principle of economic voting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
10
1.2
Outline of economic voting theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
37
1.3
Vote choice process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
37
2.1
GDP growth in the European Union, 2000–2015 . . . . . . . . . . . . . . . . . . . . .
43
2.2
Distribution of raw and centred party support . . . . . . . . . . . . . . . . . . . . . .
50
2.3
Distribution of left–right distance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
52
2.4
Distribution of prospective and retrospective assessments . . . . . . . . . . . . . . .
54
2.5
Relationship between prospective and retrospective assessments . . . . . . . . . . .
55
2.6
Data stacking process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
58
3.1
Support for incumbent prime minister’s party by economic assessment . . . . . . .
69
3.2
Relationship between party support and left–distance . . . . . . . . . . . . . . . . . .
71
3.3
Predicted support for prime minister’s party by left–right distance . . . . . . . . . .
75
3.4
Predicted support for prime minister’s party by economic assessment . . . . . . . .
77
3.5
Data classification structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
79
3.6
Economic vote by year and incumbency status . . . . . . . . . . . . . . . . . . . . . .
81
3.7
Predicted support by year and incumbency status . . . . . . . . . . . . . . . . . . . .
82
4.1
Economic vote for dominant government party by time in office . . . . . . . . . . . 103
4.2
Economic vote for dominant government party by ideological cohesion . . . . . . . 104
4.3
Economic vote for dominant government party by institutional clarity . . . . . . . . 105
4.4
Government and opposition economic vote by time in office . . . . . . . . . . . . . . 107
4.5
Government and opposition economic vote by ideological cohesion . . . . . . . . . 108
4.6
Government and opposition economic vote by institutional clarity . . . . . . . . . . 109
5.1
Relationship between party support and position . . . . . . . . . . . . . . . . . . . . . 124
5.2
Relationship between integration position and salience . . . . . . . . . . . . . . . . . 126
x
List of figures
xi
5.3
Party support by position on European integration . . . . . . . . . . . . . . . . . . . . 129
5.4
Relative support for pro-integration parties . . . . . . . . . . . . . . . . . . . . . . . . 130
5.5
Party support by general left–right position . . . . . . . . . . . . . . . . . . . . . . . . 132
5.6
Party support by economic left–right position . . . . . . . . . . . . . . . . . . . . . . . 135
5.7
Party preference by social libertarian–authoritarian position . . . . . . . . . . . . . . 138
6.1
Average turnout at national elections in EU countries . . . . . . . . . . . . . . . . . . 151
6.2
Proportion intending to vote by year and economic assessment . . . . . . . . . . . . 152
6.3
Predicted probability of voting by past behaviour . . . . . . . . . . . . . . . . . . . . 155
6.4
Predicted probability of voting by economic assessment and party identification . 156
7.1
Support for European integration by prospective economic assessment . . . . . . . 173
7.2
Support for European integration by left–right position . . . . . . . . . . . . . . . . . 174
7.3
Evaluation of EU membership over time . . . . . . . . . . . . . . . . . . . . . . . . . . 179
7.4
Positive evaluation of EU membership by prospective economic assessment . . . . 180
7.5
Positive evaluation of EU membership by left–right position . . . . . . . . . . . . . . 181
7.6
Attribution of economic responsibility by prospective economic assessment . . . . 184
7.7
Attribution of economic responsibility by left–right position . . . . . . . . . . . . . . 185
List of tables
2.1
Sample size and interview mode of EES surveys . . . . . . . . . . . . . . . . . . . . .
44
2.2
Key variables used to measure the economic vote . . . . . . . . . . . . . . . . . . . .
48
3.1
Prime ministers’ parties model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
74
4.1
Components of institutional and government clarity . . . . . . . . . . . . . . . . . . .
95
4.2
Alternative government clarity models . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
5.1
Variation in party position over time . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
5.2
Regression model predicting general left–right position . . . . . . . . . . . . . . . . . 127
5.3
Left–right and extreme tendency by year and voter group . . . . . . . . . . . . . . . 133
5.4
Directional and extreme tendency along economic dimension . . . . . . . . . . . . . 136
5.5
Directional and extreme tendency along social dimension . . . . . . . . . . . . . . . 139
xii
Abbreviations
BNP
British National Party
CDU
Christian Democratic Union (Germany)
CHES
Chapel Hill Expert Survey
CSU
Christian Social Union (Germany)
ECB
European Central Bank
EES
European Election Studies
EU
European Union
GDP
Gross domestic product
IMF
International Monetary Fund
MSZP
Hungarian Socialist Party
PASOK
Panhellenic Socialist Movement (Greece)
ParlGov
Parliaments and Governments Database
PDY
Political Data Yearbook (European Journal of Political Science)
SGP
Stability and Growth Pact
Syriza
Coalition of the Radical Left (Greece)
UK
United Kingdom
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Introduction
The Great Recession of 2007–09 was a deep global recession that severely harmed the world
economy. It has been described as the worst economic crisis since the Great Depression of the
1930s, both in general (Reinhart and Rogoff 2009, 208) and in Europe specifically (European
Commission 2009, iii). In many European countries, the period following this recession has
been characterised by political upheaval and many incumbent governments have lost power
as a result (Kriesi 2014). This can seemingly be explained by the theory of economic voting,
which predicts that voters’ support for incumbent governments is linked to good economic
conditions (Key 1961; Fiorina 1981; van der Brug, van der Eijk and Franklin 2007; Duch
and Stevenson 2008). This simple idea is so well established that it has been described as
‘virtually a social science law’ (Duch 2007, 805). Nonetheless, there are still open questions
about economic voting. As the theory was developed after the Great Depression, it is not yet
known whether the economic voting effect operates in the same way during a severe global
recession than it does at more normal times. The main question motivating this thesis is: was
the economic vote during the Great Recession different from that at other times? Since many
of the elections following the crisis coincided with deeply unpopular austerity programmes
(Kriesi 2014), the thesis also examines whether this austerity played any role in influencing
voters’ attitudes and intentions.
The Great Recession began with the financial crisis in the United States sparked by the
subprime mortgage lending practices of US banks (European Commission 2009, 1). The
European Union was strongly affected, with the whole EU entering a fifteen-month recession
from early 2008. Although this period ended in mid-2009, it was succeeded by a further recession in late 2011 and yet another in 2012. The Eurozone remained in recession throughout
the entire period from late 2011 until early 2013.1 In several of these countries, government
funds were used to bail out banking sectors at risk of collapse (62). Austerity programmes were
1
These figures are based on OECD seasonally adjusted quarterly GDP growth rate data, using the common
definition of a recession as two or more consecutive quarters of negative growth.
1
2
INTRODUCTION
widely implemented. In the following years, a number of governments suffered memorable
defeats at the ballot box (Kriesi 2014). Decades of economic voting theory have established
that voters punish governments electorally when economic conditions are poor but the Great
Recession is of a much greater scale and severity than the typical national recessions that have
informed much of this theory. The last comparable event was the Great Depression of the
1930s, and although there have been studies of that time (e.g. Lindvall 2013, 2014), they
are fundamentally limited by the lack of any data beyond the electoral results and primitive
economic indicators of the time. The purpose of this thesis is to study electoral behaviour in
the European Union during the Great Recession and to determine whether voters responded
to economic dissatisfaction the same way then as they have at other times.
The Great Recession and its after-effects have played out differently in every country. It is
worth taking a closer look at some of the countries that were most deeply affected in order to
gain an understanding of the ways that governments and voters responded to the unfolding
situation. One of the most striking examples of an electoral backlash took place in Ireland.
Ireland was the first European country to enter recession, beginning in the second quarter of
2007 and, but for a short reprieve in late 2007, remaining in recession until the end of 2009.2
Unemployment more than doubled in the space of a few years, the rate rising from 6.4 percent
in 2008 to 14.7 percent in 2011.3 In 2009, 100 000 workers took to the streets of Dublin to
protest the government’s plan to levy the pensions of public sector employees to make up
for declining government revenue (BBC News 2009). Irish banks were heavily damaged by
the global financial crisis and later in 2009, the Irish government was forced to nationalise
the Anglo-Irish Bank to prevent its collapse, after other banks had already been bailed out
(Connor, Flavin and O’Kelly 2012). The first opportunity that Irish voters had to respond to
these events was the general election of 25 February 2011. This election proved catastrophic
for Fianna Fáil, the leading party of the coalition government at the time and the party which
had held the Irish premiership for all but eighteen of the 79 years between 1932 and 2011. At
the 2011 election, Fianna Fáil lost more than half of its vote compared to the previous election
and was reduced from 78 to a mere twenty seats in the Dáil Éireann (O’Malley 2012). Although
they have increased their support from that low at the 2016 election, they still received less
than one quarter of the vote (Irish Times 2016), showing that they have still not recovered
from most of the damage.
2
3
According to OECD quarterly GDP data.
According to OECD annual unemployment rate data.
3
One of the countries that has been most severely affected by the crisis is Greece. The Greek
crisis has been characterised by high levels of sovereign debt and the inability of the Greek
government to repay or service that debt. Greece suffered an exceptionally long period of
recession, with negative or zero quarterly GDP growth rates in all but two quarters of the six
years from mid-2007 to mid-2013, according to OECD figures. In light of increasing concern
that Greece would be unable to repay its sovereign debt, the so-called Troika of the European
Commission, European Central Bank (ECB) and International Monetary Fund (IMF) offered
Greece a series of bailout loans (European Commission 2010, 2012). These loans were conditional on the implementation of a package of austerity measures, including privatisation and
structural reforms. The measures were deeply unpopular with the Greek public, leading to
mass protests. One survey found that 29 percent of the Greek population had participated in
anti-austerity protests (Rüdig and Karyotis 2014, 488).
The economic instability in Greece has been matched by political instability, with five general elections having taken place in the years 2009–15. The first election took place on 4 October 2009, during the recession but before the debt crisis took hold. The centre-left Panhellenic
Socialist Movement (PASOK) was able to secure an absolute majority of votes and took government from the centre-right New Democracy party (Mavrogordatos and Marantzidis 2010).
Two elections were held in 2012, after the bailout loans had been accepted and the austerity
programme had begun to be implemented. The first election took place on 6 May but as no
government could be formed, fresh elections were held on 17 June. The result of this election
was that a grand coalition was formed in which New Democracy and PASOK shared power
along with a third party, Democratic Left. This coalition was was explicitly pro-European and
had the objective of keeping the anti-austerity Coalition of the Radical Left (Syriza) out of
power (Mylonas 2013). This effort was ultimately futile, as Syriza gained enough seats in the
election of 25 January 2015 to form a new government with the support of the right-wing
populist Independent Greeks. This new coalition was re-elected in the September elections of
the same year (Aslanidis and Kaltwasser 2016). In effect, Greek voters have rejected not just
the party in power at the time the crisis began but the entire political mainstream.
Not all countries that were deeply affected by the Great Recession experienced such turmoil. The United Kingdom was also hit very hard by the crisis but the political effects were less
severe than in Ireland and Greece. The UK was in recession for eighteen months between early
2008 and late 2009.4 The unemployment rate rose from 5.3 percent in 2007 to 8.0 percent in
4
According to OECD quarterly GDP data.
4
INTRODUCTION
2011, before slowly falling again to its pre-crisis level by 2015; youth unemployment peaked
at 21.3 percent in 2015.5 Two general elections have been held in the UK since the beginning of the crisis. The first took place on 6 May 2010. In this election, the incumbent Labour
Party lost a considerable portion of its support, with its vote share reduced to 29.0 percent, a
6.2 point decline since the preceding election (Whitaker 2011). Despite the use of a majoritarian electoral system, no party received a majority of seats but the Conservatives were able
to form a centre-right coalition with the centrist Liberal Democrats. At the second election,
which took place on 7 May 2015, the Conservatives were able to form a government in their
own right and the Labour Party recovered some of its vote, with a 1.5 point increase in support
(BBC News 2015). Thus although there was a change of government, this was nothing like
the severe collapse in support faced by the incumbent party in Ireland or the reconfiguration
of the party system in Greece.
In all of these cases it may seem obvious that the incumbent governments suffered defeats
because they were in power at a time when economic conditions worsened considerably. This
would be the classic economic voting response, in which voters sanction governments for
failing to prevent an economic crisis. This is a reasonable interpretation but there are other
interpretations that ought to be considered as well. In the case of the United Kingdom, the
Labour Party had already been in power for thirteen years by the time of the 2010 election,
an unusually long tenure for a Labour government as no Labour government had previously
served more than six years and a few months. Furthermore, this was the first election faced by
Gordon Brown, who had succeeded Tony Blair as prime minister and who was less charismatic
than his predecessor. In other words, had there been no recession, Labour may well have lost
the election anyway. The sheer magnitude of Fianna Fáil’s defeat makes it difficult to deny that
the Irish result was related to the crisis. Similarly, the fact that the grand coalition of Greek
mainstream parties has failed to hold on to office is strongly suggestive of a crisis response in
Greece. Even so, an economic vote would normally be expected to benefit the main opposition
party, which in this case was also clearly rejected. Furthermore, voters may well have been
reacting to the unpopular austerity measures as much as or more than the recession itself.
This thesis aims to shed new light on economic voting theory by testing these alternative
explanations. Although there is an enormous economic voting literature showing that voters
do tend to vote against incumbent parties when economic conditions are poor,6 these results
have not been thoroughly tested in the context of a global economic crisis. This means that
5
6
According to OECD annual unemployment rate and youth unemployment rate data.
This literature is discussed in Chapter 1.
5
it is not yet known whether the economic voting response is stronger in such a context or
whether there are spillover effects, such as a loss of support for mainstream non-government
parties. It has previously been hypothesised that the economic vote may operate differently
in different economic contexts (Stevenson 2002, 45) and the Great Recession offers an ideal
opportunity to test this hypothesis. Furthermore, economic voting theory has little to say
about the electoral response to the austerity measures that played such a large role in the
political response to both the Great Depression and the Great Recession. These are the key
questions motivating to this thesis. To what degree, if any, does the electoral response to the
Great Recession differ from the ordinary economic voting response and how much of this can
be attributed to austerity measures rather than the economic conditions themselves?
In order to examine these questions, this thesis uses data from several sources. The most
important of these is the European Election Studies (EES) voter surveys. These are surveys of
approximately one thousand respondents in each European Union member state. A wave of
the EES surveys has been collected shortly after each set of elections to the European Parliament since 1979. Three of these waves are used in this thesis. The first, the 2004 wave, took
place a few years before the beginning of the Great Recession. The 2009 wave was collected
at a time when most of the countries had entered recession. The final wave, collected in 2014,
took place in the aftermath of the crisis, when many countries were still in recession and once
the effects of the austerity programmes had been widely felt. In other words, these three time
points represent the before, during and after periods of the Great Recession. The EES data
includes a number of questions that make it suitable for measuring the level of economic voting propensity. The EES survey data is also supplemented with aggregate data, most notably
from the Parliaments and Governments Database (ParlGov). Multi-level modelling is used to
estimate economic voting effects across the 25 countries that had joined the EU in 2004 or
earlier.
This thesis consists of eight chapters. Chapter 1 reviews the economic voting literature
and explains the theoretical model informing this study. This chapter argues for a sociotropic
model of economic voting and for an individual-level study using party support rather than
vote choice as the dependent variable. It argues that both the prospective and retrospective
theories of economic voting have merit but that a prospective approach is appropriate for this
thesis because asking people in 2009 how they felt the economy had changed over he past
twelve months is not very informative, since there is broad agreement in most of the studied
countries that the economy had become worse in that period. This chapter also outlines the
6
INTRODUCTION
two-stage model of vote choice according to which voters form an assessment about the economic conditions in their country, which in turn influences their level of support for each of the
parties in their country, this preference finally determining their vote choice. This theoretical
model informs the empirical model developed in later chapters to measure the economic vote.
Chapter 2 explains in depth the data and methods used throughout the rest of the thesis.
The chapter discusses how the economic vote is measured using multilevel analysis to predict
an individual’s level of support for the parties in his or her country. The term ‘economic vote’
is used throughout this thesis to mean the degree to which an individual’s current support for
government parties relative to opposition parties is affected by his or her economic perceptions.
This means that there need not be an actual election in order to speak of the economic vote
in a given year. The chapter also discusses the choice of datasets in more depth as well as the
details of how the key variables have been measured.
The remaining chapters discuss the results of the analysis. Chapter 3 compares the economic vote in 2004, 2009 and 2014. In this chapter, a multilevel model is developed to measure the effect of a change in prospective economic assessment on a voter’s levels of support
for each of the parties in their country. This model is used to show that support for incumbent
parties is positively associated and support for opposition parties negatively associated with
an individual’s prospective economic assessment, as economic voting theory predicts. It goes
on to show that this effect was stronger in 2004, before the Great Recession, than it was in
2009, when the recession was at its peak. It further shows that the economic voting effect was
stronger in 2014 in the aftermath of the recession than in 2009, although not as strong as in
2004. These results are contrary to expectations and some of the implications are discussed.
Potential explanations for this result are explored throughout the remainder of the thesis.
Chapter 4 is concerned with explaining why some countries experienced a stronger economic voting effect than others. There is a body of evidence that certain political institutions
afford greater clarity of responsibility than others, making it easier for voters to apportion
blame and so strengthening the economic voting effect in those countries. For example, in the
United Kingdom, the majoritarian electoral system, the weak upper house and the procedural
norms together have the effect that prime ministers are almost always in a position to implement their preferred legislation without having to negotiate with a number of other actors.
In Germany, by contrast, federal chancellors usually have to contend with coalition partners,
a powerful upper house and a parliamentary committee system not necessarily controlled by
their party. As a result, it is not surprising that British voters are more willing than German
7
voters to hold their governments to account for poor economic conditions. This chapter investigates whether clarity of responsibility can also explain the variation between countries in
their response to the Great Recession. The only clarity effect that is found is that the economic
vote is stronger in countries where the government is more ideologically cohesive. Even this
clarity effect was depressed during the recession.
Chapter 5 examines which parties benefited from the Great Recession. Earlier chapters
have established the presence of economic voting during that time, meaning that incumbent
parties suffered a loss of support when voters believed the economy would worsen. This raises
the question of which parties those voters turned to and it is this question that this chapter
addresses. It is variously claimed that extreme parties, left-wing parties and Eurosceptic parties
ought to be the beneficiaries of any post-crisis change in party support. This chapter puts
these claims to the test by classifying each of the parties in the study and examining whether
parties in any particular group are more likely than other parties to gain support from voters
pessimistic about the economy. It is found that the biggest beneficiaries of the recession were
Eurosceptic parties and parties further from the centre than the mainstream.
As well as the economic voting behaviours discussed up to this point, there is also the
possibility that voters have chosen to respond to the dire economic situation by withdrawing from voting altogether. Chapter 6 investigates the possibility of an economic abstention
effect. There is an existing body of evidence suggesting that poor economic conditions do
depress turnout but, as with economic voting, there have been very few studies of this effect
in the context of a severe transnational crisis. This chapter tests the hypotheses that there was
an economic abstention effect in all three years of the study and that the size of this effect
was greater during the crisis than it was at other times. Although the economic abstention
effect was found to exist, it was also found that this effect was weaker during the crisis before
becoming stronger than even previously in the years following.
The preceding chapters found that the strongest changes in attitudes and intentions mostly
took place too late to be attributed to the crisis alone, suggesting that the austerity introduced
in response to the crisis may also have played an important role. Chapter 7 tests this hypothesis
by examining attitudes towards the EU and European integration as well as how responsible
voters believe the EU is for the economy. Given the highly visible role of European institutions
in negotiating austerity programmes with several countries as a condition of bailout loans, it
is expected that any anti-austerity sentiment would influence this attitude. It is shown that
8
INTRODUCTION
support for European institutions and further integration fell in the years following the crisis
and voters became increasingly likely to believe that the EU is responsible for the economy.
Finally, Chapter 8 draws these various threads together to offer a coherent explanation of
European electoral behaviour before, during and after the Great Recession. Several possible
explanations for these results are discussed and the merits of each examined. It is argued that
the best explanation for these results is that the electoral response to the austerity measures
imposed in many countries was stronger than the response to the recession itself. It seems
that voters were able to be relatively forgiving of a crisis that spread to Europe from overseas
but they were less forgiving of their governments’ management of the crisis and unwilling
to accept the austerity programmes that many of those governments implemented. This has
implications for economic voting theories, which usually assume that specific policy measures
are of little import to voters as it is only the outcomes that interest them.
Chapter 1
Theory of economic voting: how economic
conditions shape the vote
Few theories in political science have been as successful as the theory of economic voting.
Economic voting effects have been observed time and time again, at numerous times and in
numerous countries. As Raymond Duch puts it, ‘it has now become virtually a social science
law that the economy is one of the most important influences on how individuals vote’ (Duch
2007, 805). Despite, or perhaps because of, this success, there are numerous variant theories
of economic voting. Some argue that economic voters are trying to select the best possible
party to govern them; others that they are simply trying to reward or punish the incumbent
government for its performance. Some theories posit that voters look to the future condition of
the economy; others that they simply react to its past performance. It has been theorised that
economic voters are only concerned with their own situation but it has also been theorised that
they also take into account the conditions of others. Moreover, economic voting suffers from
an instability problem (Lewis-Beck and Paldam 2000). What this means is that measurements
of the strength of the economic vote have a tendency to be very sensitive to both the country
and the time of the measurement, although there is no obvious reason why this should be
the case. Attempts to solve this problem have led to even more formulations of economic
voting theory, such as the idea that certain institutional arrangements are more conducive to
economic voting than others. This chapter reviews these competing theories in the light of
the existing empirical evidence in order to construct the specific economic voting theory that
informs this thesis.
This chapter begins with an overview of economic voting, explaining what it is and briefly
discussing the history of the theory. The next sections examine some of the key disputes in the
literature. The first of these is whether the economic vote should be modelled using objective
economic indicators or voters’ perceptions of the current economic conditions. The second
9
10
CHAPTER 1. THEORY OF ECONOMIC VOTING
Figure 1.1: Basic principle of economic voting
The central idea of economic voting theory is that a strong economy benefits the incumbent
electorally and a weak economy benefits the opposition. This implies that a person’s vote
choice is affected differently by the prevailing economic conditions depending on which parties
are currently in power.
is whether economic voters are egocentric, that is to say entirely concerned with their own
situation, or sociotropic, which means at least partially concerned with the well-being of others. The next debate is whether economic voting is prospective or retrospective in orientation,
in other words, whether voters are more concerned with the likely future trajectory of the
economy or with its history. Following that, there is a discussion of the process of vote choice
formation, where it is argued that economic voting should be studied in terms of party support
rather than final vote choice. Then there is a discussion of the two basic logics of economic
voting that have been proposed. These are the sanctioning models and the selection models,
which offer different explanations as to what individual economic voters intend to achieve
through their actions. Following this, there is a review of the literature about economic voting during the Great Recession. It will be shown that there is a need for a cross-national
individual-level study that explicitly compares economic voting during the Great Recession to
that at other times. Finally, it will be shown how these threads fit together to form the cohesive
theory that underpins this work.
1.1
What is economic voting?
The fundamental claim of economic voting theory is that voters are more likely to support
the incumbent government when the economy is performing well and more likely to support
opposition parties when the economy is performing badly. This idea is illustrated in Figure 1.1,
which is intended to be general enough to describe any variant of the theory suitable for a
multiparty system. As the diagram shows, a person’s vote choice is influenced by the current
1.1. WHAT IS ECONOMIC VOTING?
11
economic conditions and, crucially, the precise nature of this influence depends on which
party or parties are currently incumbent. Without this interaction, a strong economy would
always benefit the same party but the theory predicts that a strong economy benefits whichever
party happens to be in power. The term ‘economic vote’ refers to this interaction throughout
this thesis. In a strictly two-party context, such as the United States, this model can be, and
frequently is, simplified further so that the interaction can be eliminated entirely and the model
simply becomes support for the government relative to support for the opposition depends on
the prevailing economic conditions. It will be shown later in this chapter that this is not an
appropriate model for the multiparty systems analysed in this thesis.
Before proceeding to discuss specific aspects of this theory, it is worth briefly discussing
the historical development of the idea of economic voting. Economic voting has a long history
of being studied. Some of the earliest studies were published during the Great Depression,
although the term ‘economic voting’ was not used until much later. Tibbitts (1931), building
upon an even earlier study (Rice 1928, cited in Tibbitts 1931), found evidence that changes
in incumbent vote share at US congressional elections can be partly explained by fluctuations
in economic conditions. This study is unusual for such an early paper in that the ideas it
presents are very similar to economic voting as it understood today. Another early study of
economic voting was conducted by Pearson and Myers (1948), who found that price indices
compared favourably to opinion polls as a predictor of US presidential elections, with high
inflation aiding the challenger and low inflation the incumbent. On the other hand, Wilkinson
and Hart (1950) found almost no correlation between incumbent vote share at presidential
elections and an index of economic activity.
There was little agreement at this stage not only about results but also about which parties
should benefit from good economic conditions. Many studies of the relationship between
electoral behaviour and economic conditions were looking for party-specific effects rather
than the incumbent effects that characterise economic voting and which have come to be
expected today. For example, during Franklin D. Roosevelt’s presidency and motivated by his
depression-era social and economic reforms, there were studies seeking to determine which
groups had the greatest support for Roosevelt, finding that poorer counties (Ogburn and
Coombs 1940) and counties that had been most severely affected by the depression (Gosnell and Colman 1940) were most likely to support the president. Other studies anticipated
that voters would turn to the left when economic conditions are poor and to the right when
conditions are good, finding some support for this hypothesis in US election results at US pres-
12
CHAPTER 1. THEORY OF ECONOMIC VOTING
idential (Kerr 1944) and congressional (Rees et al. 1962) elections. There is little discussion
from this period of economic voting outside the United States and that is not well-developed.
The idea that the fate of the contemporary Conservative government in the United Kingdom
might depend on economic conditions is explored by Durant (1965), the then-director of the
UK Gallup Poll, who quotes newspapers as the source of this idea, but it is not clear whether
this is expected because of that party’s political orientation or because of its incumbency. Even
though these ideas have largely been supplanted by the incumbent-focused theory of economic voting, studies of this kind still appear occasionally. Carlsen (2000), for example, analysed the effect of unemployment and inflation rates on quarterly opinion polls in the US, the
UK, Canada and Australia, arguing that left- and right-wing parties are affected differently by
different kinds of economic adversity.
This debate was reignited when Kramer (1971) published a study introducing multivariate
statistical analysis to this question. This marked a shift away from the basic cross-tabulation
and correlation analyses of earlier studies but Kramer’s work continued in the tradition of analysing aggregate data sources, namely incumbent vote share and various economic measures
such as unemployment, income and price indices. He found a positive relationship between
real income and incumbent vote share in elections to the US House of Representatives. Shortly
afterwards, Stigler (1973) published a refutation, using similar methods to argue that this relationship actually does not exist and theorising that it ought not exist in any case, as in his
view the two major parties in the United States are equally driven to manage unemployment
and inflation. Arcelus and Meltzer (1975) also found only weak evidence of any link between
aggregate economic conditions and the vote shares of the major US parties. Tufte (1975) on
the other hand found results consistent with Kramer’s, in that US midterm election results
were influenced by economic conditions. In an effort to explain these inconsistent findings,
Bloom and Price (1975) hypothesised that economic conditions affect voter behaviour much
more during times of deprivation than during times of prosperity. Their findings seemed to
confirm this hypothesis and they argued that voters were inclined to punish governments for
poor conditions at US House elections but not to reward them for better conditions. Even this
did not settle the debate, and further research found no important relationship between US
presidential vote share and economic conditions (Fair 1978).
Economic voting soon became a major focus of study within the discipline. Not only did
the literature expand considerably but various competing theories of economic voting arose.
Kinder and Kiewiet (1981) proposed the sociotropic voting theory, which rejects the older
1.2. PERCEPTIONS OR INDICATORS
13
egocentric voting theory and argues that voters are concerned, to at least some degree, with
the conditions of others, not just themselves. Fiorina (1981) brought new attention to Downs’
(1957) idea that economic voting could be explained by a rational model of selection, which
offered an alternative to the then-dominant idea that economic voting was an act of punishment or reward (Key 1961, 1964, 1966). Since the theory developed in so many different
directions, rather than continuing chronologically, this chapter will proceed thematically. Each
of the key debates is discussed in turn, along with any empirical evidence that sheds light on
the competing approaches and a discussion of how these developments are relevant for this
thesis.
1.2
Perceptions or indicators: the link between the economy and
the vote
Economic voting theory naturally raises questions about the nature of the link between the
prevailing economic conditions and the individual’s vote choice. For example, what sorts
of economic privations are voters sensitive too? Are these all equal? Do voters see these as
separate issues or facets of a cohesive economy issue? The answers to these questions typically
fall into one of two categories. The first category posits that voters respond to material changes
in well-being, which can be measured by standard economic indicators, such as inflation or
unemployment rates. The second category proposes that voters form an overall impression of
the condition of the economy and it is this impression that influences their vote choice. For
example, even if all of the economic indicators for a given year are positive, a particular voter
might be under the impression that the economy has worsened over the past year, leading
that voter to turn against the government. This might be the case if that voter’s personal
experience of the economy was bad or if the sources of information relied upon by that voter
gave a distorted impression of the true situation. There are further related questions, such
as whether voters are exclusively concerned with their personal situation or whether they are
concerned with the economy more broadly and also whether voters are primarily influenced
by their assessment of past economic performance or their expectations of future economic
performance. These related questions are discussed in the following sections. This section
examines the relative merits of the economic indicators and individual perceptions theories of
economic voting and explains why the perceptions approach is preferred in this thesis.
The different approaches offer contrasting benefits. While it is naturally true that voters
can only respond to their own perceptions of the economic situation, if these perceptions are
14
CHAPTER 1. THEORY OF ECONOMIC VOTING
highly accurate, then an unnecessary link can be eliminated from the theoretical model of
economic voting behaviour. Indicator-based theories also lend themselves well to macro-level
empirical studies, where individual evaluations are not available in any case. Furthermore,
since most countries collect data pertaining to multiple different aspects of the economy, an
indicator-based theory can make empirically testable claims about how voters react to different
kinds of economic privation. For example, it is sometimes argued that voters are more sensitive
to unemployment when the incumbent government is left-leaning and inflation when rightleaning (Powell and Whitten 1993, 404–405; Whitten and Palmer 1999; van der Brug, van der
Eijk and Franklin 2007, 59). Empirical efforts to pin down these sorts of differences have not,
however, been overwhelmingly successful. For example, Chappell and Veiga (2000) examined
136 elections in thirteen European countries between 1960 and 1997 and were able to find
significant economic voting effects for inflation but not for other kinds of economic problems.
Another study examined quarterly opinion polls from Australia, Canada, the United Kingdom
and the United States, finding that voters were more tolerant of inflation than unemployment
when right-wing governments were in power, but there were no conclusive results for leftwing governments (Carlsen 2000). A recent study of OECD elections between 1975 and 2013
has also found a difference between patterns of economic voting for left-wing and right-wing
governments but in this case it was found that voters were more tolerant of inflation when leftwing governments were in power (Bouvet and King 2016, 76). These findings are obviously
contradictory and it may well be that there is no systematic pattern of differences in economic
voting according to the ideology of the governing parties.
On the other hand, if voters’ perceptions of the economy are substantially distorted, then
omitting the perception formation process from the theory will cause problems, which may
explain the inconsistent findings discussed above. Furthermore, given the notorious instability
problem of economic voting studies, it is worth reconsidering some traditional strong assumptions, as they may provide some helpful clues. If it is in fact the case that voters’ perceptions
are distorted, then both the link between objective conditions and voters’ perceptions of those
conditions and the link between perceptions and vote choice ought to be studied carefully
in order to understand economic voting. Much therefore hinges on whether voters accurately
perceive economic conditions. This was studied by Paldam and Nannestad (2000), who found
that Danish respondents mostly knew little about the state of the economy, in terms of indicators like unemployment, inflation and foreign date rates (Paldam and Nannestad 2000). This
is problematic for an indicator-based theory, since if voters do not know how those indicators
1.2. PERCEPTIONS OR INDICATORS
15
are moving, then it is difficult to see how they could condition their votes. In response to
these findings, Sanders (2000) looked at voters’ assessments of the overall condition of the
economy in the United Kingdom between 1974 and 1997. He found that, although voters typically have little awareness of specific economic facts, their assessments of the general state of
the economy tend to be remarkably accurate.
Taken together, these findings suggest that a perception-based theory of economic voting
is more promising than an indicator-based theory. On the other hand, it has also been found,
in an aggregate study of German electoral behaviour, that hidden unemployment as well as
official unemployment harms support for the governing parties (Feld and Kirchgässner 2000).
It is thus possible that the evident lack of knowledge of official statistics does not represent
mass ignorance of the true state of the economy. Nonetheless, even if it is true that voter
perceptions are in some respects a better reflection of the economic reality than the official
statistics, this just strengthens the argument that published economic indicators are a flawed
tool for understanding the economic vote. It has also been argued that, even though the
average voter may have no incentive to learn about the condition of the economy, there is a
subset of informed voters that does have such an incentive and this subset could be sufficient
to explain the economic voting effect (Aidt 2000). Only a theory that includes perceptions can
properly account for this possibility.
One difficulty with a perception-based theory is that it has been theorised that voters have
a tendency to filter the information that they pay attention to based on their prior beliefs about
the incumbent government. Government supporters are more likely to be receptive to news
that supports the belief that the economy is being managed well than to news challenging that
belief, while the opposite is true for opposition supporters. If this is the case then this produces
a potential endogeneity problem since, according to this model, a citizen’s vote choice both
influences and is influenced by his or her perceptions of the economy. This point has produced
considerable concern in the literature (e.g. Wlezien, Franklin and Twiggs 1997; Evans and
Andersen 2006; Evans and Pickup 2010) and has been particularly forcefully expressed by
van der Brug, van der Eijk and Franklin (2007, 26). The empirical evidence, however, suggests
that this is much less of a problem in practice than it might appear. As part of their detailed
study of the economic vote, Duch and Stevenson (2008, 123–126) compared their key models
to variants specifically designed to account for any endogeneity effects in voters’ economic
perceptions and found no evidence of systematic bias in the results of the naive models. Other
attempts to exogenise economic perceptions have similarly confirmed the existence of the
16
CHAPTER 1. THEORY OF ECONOMIC VOTING
economic vote (Lewis-Beck, Nadeau and Elias 2008; Lewis-Beck, Stubager and Nadeau 2013;
Hansford and Gomez 2015; Stevenson and Duch 2013, 318).1
1.3
Sociotropic voting: do voters only care about themselves?
A related question is whether voters are concerned with the broader economy or merely their
own personal well-being when they vote economically. As discussed earlier in this chapter,
many of the early studies of economic voting used aggregate data to demonstrate a link
between economic conditions and voter support for the incumbent government. These early
studies typically gave no account of the individual behaviour that gave rise to these largescale patterns and it fell to later scholars to produce theories to explain these trends. A common assumption was that individuals respond to changes in their own personal conditions.
Thus, if many people become worse off, both the government’s vote share and the national
measures of economic well-being decline simultaneously. Conversely, if many people become
better off, these indicators improve together. This is what is meant by egocentric (or pocketbook) voting—voters consider only their own personal circumstances, or perhaps those of
their friends and family. Although these early studies rarely made explicit an assumption of
egocentric voting, it tends to be apparent in their models. For example, Tufte (1978, 127–134)
breaks down the vote at the US presidential elections of 1968, 1972 and 1976 by income and
family financial situation. Similarly, Bloom and Price (1975, 1243) produced a model in which
the short-term variations in voting behaviour are primarily explained by short-term changes in
real income. In their influential book The American Voter, Campbell et al. (1960) also make the
claim that political behaviour, at least in some spheres, can be explained in terms of ‘primitive
self-interest’ (205). Again, their analysis focuses on the way that voting behaviour changes in
response to changes in personal economic conditions (381–401).
Although egocentric voting was widely assumed, the theory was rarely put to the test.
When Fiorina (1978) did test the egocentric voting hypothesis using individual-level survey
data, he failed to find support for it. Similarly, in a study of voting in US congressional elections between 1956 and 1976, Kinder and Kiewiet (1979, 521) found little support for the
idea that an individual’s vote choice was materially affected by any economic troubles that
individual had personally experienced. In order to explain this, and in light of the evidence
1
It should also be noted that it is not the purpose of this thesis to establish the absolute size of the economic
vote but rather to determine whether economic voting was different during the Great Recession. The substantive
results of this thesis should thus be unaffected by any inflation in the estimates of the size of the economic vote
owing to an endogeneity effect, unless that effect was itself affected by the recession.
1.3. SOCIOTROPIC VOTING: DO VOTERS ONLY CARE ABOUT THEMSELVES?
17
that a government’s fortunes are tied to the economic conditions it presides over, they theorises that voters respond more to national economic conditions than to their own personal
conditions (524). That is, voters do hold governments to account for the state of the economy
but not necessarily for the individual economic events in their own lives. A later study found
further evidence for what they refer to as sociotropic voting (Kinder and Kiewiet 1981). Using
a panel study of voters at the 1972, 1974 and 1976 US elections (congressional and, other than
in 1974, presidential), they once again tested the egocentric voting hypothesis. Again, they
found that sociotropic voting better explains their observations than egocentric voting. Although they note that presidential elections have often provided more support for egocentric
voting than congressional elections (147), they conclude that ‘for presidential voting even
more completely than for congressional voting, citizens’ assessments of national economic
conditions—their sociotropic judgements—overwhelm economic grievances encountered in
private life’ (148).
Although it is used here in contrast with the term egocentric, the term sociotropic does not
simply mean altruistic. That would suggest that sociotropic voters act purely in the interests
of others and pay no heed to their own interests. Rather, the term was coined to mean ‘taking
some account—we needn’t say exactly how much—of other persons’ interests or, if you like,
of the collective’s interest’ (Meehl 1977, 14). So, both theories accept that voters heed their
own interests to some degree. Sociotropic voters do not necessarily ignore their own interests
but they do give some consideration to the interests of others. The key difference between
egocentric and sociotropic theories of voting is how the individual voter behaves. According
to the egocentric theories, a voter will react to changes in his or her personal conditions. So a
voter who loses his or job, for example, will be more likely to vote against the government than
one who keeps his or her job, irrespective of the unemployment rate. The sociotropic theories,
on the other hand, predict that the voter who just lost his or her job is not much more likely
than anyone else to vote against the government but if the unemployment rate goes up then
this will mobilise voters, employed or otherwise, to vote against the government.
The sociotropic theory of economic voting was criticised by Kramer (1983), who argued
that the evidence offered in support of this theory was insufficient to reject the null hypothesis
of egocentric voting. In his view, this evidence is merely a ‘statistical artifact’ (93). Despite
these early objections, sociotropic voting has become the consensus position in the literature
as the evidence has mounted (Nannestad and Paldam 1994). This consensus appeared to
be under threat when Nannestad and Paldam (1995, 1997a) published two studies finding a
18
CHAPTER 1. THEORY OF ECONOMIC VOTING
stronger egocentric than sociotropic economic voting effect in Denmark. This was so unexpected that this result has been described as ‘virtually unique in the literature’ (Lewis-Beck,
Stubager and Nadeau 2013, 501). Even these findings have been disputed on methodological
grounds (Hibbs 1993, 62–63) and several later attempts to replicate this results have reached
the contrary conclusion (Borre 1997; Lewis-Beck, Stubager and Nadeau 2013; Stubager et al.
2014). In light of this, it now appears that Denmark is no exception to the usual pattern of
sociotropic voting.
Finally, there is also some evidence that the centre of focus for the economic vote may
lie somewhere between the individual and the entire nation (Rogers 2014). This idea that
voters are ‘communotropic’—mostly concerned with the economic conditions of their own
communities—is interesting but has not yet been thoroughly tested in its own right, unlike
the sociotropic theory. Furthermore, this approach would pose measurement difficulties, since
communotropic economic assessment questions are not currently common in survey instruments. Given the considerable body of evidence that has been accumulated in support of the
sociotropic hypothesis, this thesis uses a sociotropic theory of economic voting.
1.4
Prospective and retrospective voting
Retrospective voting and prospective voting are the ideas that voters base their vote choice
on an assessment of the incumbent government’s past performance or an expectation of the
government’s likely future performance respectively. In many respects, the prospective voting
hypothesis is a more natural one than the retrospective voting hypothesis. A rational voter,
after all, ought to be more concerned with the future, which can still be influenced, than the
past, which cannot. Most retrospective studies do not give an explicit rationale for the decision
to use that approach but among those that do, there appear to be two main justifications.
The first is the idea that voters are not trying to rationally select the party that will produce the most favourable outcomes but rather to simply reward or punish the incumbent for
its successes or failures. The relative merits of the selection and sanctioning models of economic voting are discussed later in this chapter. For now, it suffices to say that the sanctioning
model is not accepted by this thesis. The second justification for a retrospective theory is that,
while the rational voter is indeed trying to select the party most likely to deliver the desired
outcomes, past performance is simply a cheaper and more accessible means of assessing this
than the alternatives (Downs 1957, 38–40). It is argued that, while past performance is only
a rough measure of a party’s likely future performance, it is not worth the time and effort
1.4. PROSPECTIVE AND RETROSPECTIVE VOTING
19
for most voters to find and synthesise other sources of information to produce a better estimate. While this argument has merit, it is not actually necessary to enquire into the process
of prospective assessment formation in order to understand economic voting. Why use the
retrospective measure as a proxy for the prospective measure, when the latter could simply be
measured directly? It should also not be ignored that citizens’ retrospective and prospective
assessments of the economy are often not particularly strongly correlated (Kuklinski and West
1981; Conover, Feldman and Knight 1987).
Efforts have been made to compare the prospective and retrospective theories empirically.
MacKuen, Erikson and Stimson (1992) made a distinction between ‘peasants’—retrospective
voters whose expectations of the trajectory of the economy are simple extrapolations of its
recent performance—and ‘bankers’—prospective voters whose expectations are formed by a
more sophisticated analysis. Using survey data, they attempted to determine which archetype more accurately described US voters, finding that the banker model was more accurate
than the peasant model. In other words, their findings supported by the prospective theory of
economic voting. They confirmed these findings in a follow up study, further noting that retrospective assessments do seem to influence future expectations to a degree but it is ultimately
the future expectations that predict vote choice (Erikson, MacKuen and Stimson 2000). Recent
studies have continued to find empirical support for prospective economic voting. Michelitch
et al. (2012) found prospective economic assessment to be an important predictor of vote
choice at both the US and Ghanaian presidential elections of 2008. The fact that they were
able to make the same findings in both the US and Ghana, two very different countries, is
promising, as it makes it unlikely that prospective voting is merely a quirk of American politics.
A further problem for retrospective voting is that there is evidence that voters’ retrospective assessments lack some of the qualities that would be expected from rational retrospective
voters. Using survey data collected from 40 countries between 1996 and 2005, Stanig (2013)
has shown that several psychological biases are in play when voters form retrospective assessments of the economy. For example, voters are more likely to change their assessments
in response to worsening conditions than to improving conditions (736–737). Furthermore,
these assessments are also affected by partisanship and ideology, with supporters of government parties typically less likely to be critical than opponents (737–739). Strangely, however,
this difference between supporters and opponents of the incumbent government is considerably weaker during a recession than at other times (739–740). It has to be acknowledged that
20
CHAPTER 1. THEORY OF ECONOMIC VOTING
prospective assessments may also suffer from some of these problems as well. Unfortunately,
there is little evidence either way, as most published research has focused on the retrospective
assessment.
A related question that has attracted recent scholarly attention is: how far back do retrospective voters look? Hellwig and Marinova (2015) surveyed voters during the 2012 US
presidential election, finding that short-term and long-term retrospective assessments of the
economy were approximately equally accurate, which is to say, not particularly accurate at
all. They also found that neither the short-term nor the long-term assessment was a clearly
superior predictor of vote choice over the other. Based on this finding, they argue that the
almost universal choice of a twelve-month time window in retrospective voting studies is no
worse than any other choice (884). Wlezien (2015), on the other hand, found that voters at
US presidential elections between 1952 and 2012 appeared to look back approximately two
years when forming the retrospective assessment that would influence their vote choice. This
may well be a quirk of US elections specifically, given that partial legislative elections take
place two years before every presidential election. These findings may indicate that voters are
simply reflecting back on the period since the last time a federal election of some kind was
held. Finally, Taniguchi (2016) studied retrospective voting during the 2013 Japanese upper
house election. Based on a survey of Japanese voters, he argues that voters actually look to
the long-term, slowly adjusting their perceptions over time. Unfortunately, no consensus has
emerged from these studies, nor do these results shed any light on the time frames relevant
to prospective voters. Nevertheless, this thesis is limited to the questions actually asked in
the surveys used for its analysis. As will be seen in the next chapter, this means that the time
window has to be either the year just past or the coming year, at the time of the survey.
This thesis is based on a prospective theory of economic voting. Given that retrospective
voting tends to be the default choice for economic voting studies, this may be a slightly surprising choice. The theoretical motivations discussed here are genuine—even if voters do use
retrospective evaluations as a shortcut for forming prospective assessments, as Downs (1957)
claims, it is not at all obvious that this means that those retrospective evaluations should be
used as a proxy for the prospective assessments when the means are available to measure
the latter directly. Nonetheless, it must also be acknowledged that there are more practical
motivations for pursuing a prospective rather than a retrospective study. As will be shown
in Chapter 2, there was something of a consensus among European voters in 2009 that the
economy had become worse over the previous year. This lack of variance in a key independ-
1.5. THE VOTE CHOICE PROCESS
21
ent variable would have posed serious problems for statistical analysis, so the retrospective
approach was not viable. Voters did, however, have a variety of opinions about the future
course of the economy, so a prospective analysis was possible. In any event, the fact that
the retrospective approach broke down during the Great Recession, at least for the data used
by this thesis, while the prospective approach remained viable, offers empirical support for
the argument that economic voting is fundamentally a prospective phenomenon, regardless
of how convenient retrospective evaluations may often be as a means of quickly forming a
prospective assessment.
1.5
The vote choice process
The preceding sections have discussed the nature of the link between the economy and an
individual’s vote choice but the mechanism by which this vote choice is made was left unspecified. Most early studies of economic voting tended to use aggregate data, so there was
no need for a specific theory of individual vote choice and no means of testing such a theory
anyway. These studies simply looked for relationships between the incumbent government’s
vote share2 and various economic indicators. A good example of this genre is Pearson and
Myers (1948), showed that the incumbent party tended to win US presidential elections if
and only if the economy was strong. Aggregate studies still take place today—Carlsen (2000),
for example, who showed that government vote share in four countries was linked to the
unemployment rate—but they cannot shed light on the vote choice mechanism.
The simplest plausible vote choice mechanism is one whereby a voter simply weighs up the
strengths and weaknesses of the incumbent government and votes to return the government
should its overall performance exceed some (possibly idiosyncratic) threshold and votes to
reject the government otherwise. This naive picture of the vote choice process seems perfectly
logical in the context of a strict two-party system, such as the United States. This approach
tends to be assumed by individual-level economic voting studies. Given that the vast bulk of
such studies have investigated US politics specifically, this may well explain the near universality of this approach. This approach is typically modelled using logistic or probit regression
on an individual’s reported vote choice or intended vote choice. This is not only methodologically straightforward but also a natural operationalisation of this theoretical model of vote
2
Some early US studies looked at the vote share for the Republicans or the Democrats rather than the incumbent party as such. This is because the link between economic conditions and support for the incumbent
government had not yet been decisively established, so these papers were testing theories about certain economic
outcomes favouring specific parties. For example, Kerr (1944) found a correlation between the conservative vote
and several economic indicators in the United States.
22
CHAPTER 1. THEORY OF ECONOMIC VOTING
choice, which may further explain the enduring popularity of this approach. An example of
this approach is Nadeau and Lewis-Beck (2001), who predicted the likelihood of US voters
supporting the incumbent party’s presidential candidate under various scenarios. This theory
of vote choice works well in the US context.
Many countries are not two-party systems, however, and certainly most European countries have more than two parties that typically receive a non-trivial proportion of the vote. This
extra complexity can be handled in several ways. The simplest way to generalise the naive vote
choice model to a multiparty system is to ignore parties altogether and recast the vote choice
task as voters simply making a decision to support or reject the government. Thus a vote for
any coalition party is treated the same and a vote for any opposition party is treated the same.
One example of this approach is Lewis-Beck (1986), who used various economic variables to
predict the likelihood of voters supporting any of the incumbent government parties as opposed to any of the opposition parties at elections in France, Germany, Italy and the United
Kingdom. Another example is Nadeau, Niemi and Yoshinaka (2002), who modelled this dichotomous vote choice in eight European countries. Interestingly, they found different results
in the majoritarian and consensual systems that they studied (419). This could be an artefact
of the forced dichotomisation of the vote choice, since majoritarian systems tend to have fewer
relevant parties than consensual systems. This method heavily simplifies a complex decision.
If voters really are primarily concerned with the choice between supporting and rejecting the
government, then it is not clear why such complex party systems are sustainable. In that case,
it would be expected that voters would simply support the largest government or opposition
party and the remaining parties would be starved of votes. Yet this is not the case.
An explicit theory of vote choice formation was proposed by Downs (1957, 36–50) and
elaborated by van der Brug, van der Eijk and Franklin (2007, 31–53). According to this theory,
vote choice is a two-stage process. The first stage is one of assessing party utility. For each
viable party, voters estimate by some method the utility they would derive from the success
of that party. This estimated party utility will be referred to as party support throughout this
thesis.3 The second stage of this process is where voters actually select which party will receive
their votes. This is assumed to be done by the obvious method of selecting the party that has
the greatest estimated utility to the individual voter, or in other words, each voter selects the
party that he or she supports most. The interesting stage is thus the first stage, in which
these levels of support are established in the first place. It is important to note that a voter’s
3
This follows the usage of van der Brug, van der Eijk and Franklin (2007).
1.5. THE VOTE CHOICE PROCESS
23
levels of support for the various parties do not merely form an ordering between the parties
but also indicate the degree to which one party is preferred over another. This is important
because one of the key implications of this theory is that a particular event might cause two
different voters to adjust their levels of support for a particular parties in the same direction
and by precisely the same amount and yet lead to the two voters changing their vote choices
in different ways or to only one voter changing his or her vote choice while the other does not.
The final outcome depends not just on the immediate effect of the event on party support but
also on each individual’s levels of support for the other parties in the system. It is argued that,
by failing to capture this complexity, the dichotomous approaches discussed above invite or at
least exacerbate the instability that has long plagued the study of economic voting (14).
A further method that is sometimes used to predict vote choice in empirical studies is to
generalise the two-party logistic regression model to multiple parties using techniques such
as pairwise or ordered logistic regression. For example, Duch and Stevenson (2008, 42–52)
measure what they call the ‘general economic vote’—the total variation in vote choice that
can be explained by the economy—by using a multinomial logistic regression model to predict
which of several parties would be the respondent’s vote choice. Although this is done without
giving a detailed theory of vote choice, it is compatible with the party support theory outlined
above. This method also has the advantage of taking into account the full complexity of the
party system, unlike the dichotomous method. Nonetheless, the full party support model
offers two key advantages over this approach. The first advantage is that, by modelling party
support directly, it is possible to use much richer data, since a full vector of party support
levels encodes much more information that a single categorical vote choice item. The second
advantage is that the party support model allows important information about the party, such
as its incumbency status, to be treated as independent variables, which is not possible using
the ordered logistic regression method. This is particularly important for a comparative study
such as this. These methodological details are the subject of the following chapter but they are
worth mention here because they highlight the importance of an explicit vote choice theory.
There is some empirical evidence that economic voters in multiparty systems do operate
according to a more complex logic than a simple government–opposition dichotomy could
describe. For example, it has been found that unionist voters in Northern Ireland in 2011
tended to base their party choice not just on the performance of the government but also on
the perceived influence of each party within the governing coalition (Garry 2014). This latter
aspect can be explained by the party support theory but not by the dichotomous vote choice
24
CHAPTER 1. THEORY OF ECONOMIC VOTING
theory. It has also been shown that partisans of particular coalition parties tend to believe that
the political position of the entire coalition is closer to the position of their preferred party
than other voters do (Meyer and Strobl 2016). Furthermore, it has been found that economic
voting effects are stronger for the head of government’s party than for other coalition partners
(Fisher and Hobolt 2010; Debus, Stegmaier and Tosun 2014). It has also been established that
the vote choice decision becomes more difficult for voters to make as the number of parties
increases (Orriols and Martínez 2014). These results all suggest that voters think about parties
rather than blocs and that the party rather than the coalition is thus the natural unit of analysis.
Consequently, the party support theory of vote choice forms an important piece of the overall
economic voting theory tested by this thesis.
1.6
Reward and punishment: the logic of sanction
So far this chapter has discussed various individual aspects of economic voting but there has
not yet been a discussion of the underlying logic of the phenomenon. What motivates people
to vote economically and what do they hope to achieve in doing so? This section introduces
the sanctioning model of economic voting, which was the first serious effort to answer these
questions. It is not, however, the only possibility and the competing selection model is discussed in the following section. The sanctioning, or reward–punishment, model posits that
voters seek to reward governments for good performance and punish them for bad performance. This would explain why voters are more likely to support governments when economic
conditions are good than when they are bad, which is the central observation of economic
voting. This theory is most strongly associated with V. O. Key, who described it thus:
The patterns of flow of the major streams of shifting voters graphically reflect the
electorate in its great, and perhaps principal, role as an appraiser of past events,
past performance, and past actions. It judges retrospectively; it commands prospectively only insofar as it expresses either approval or disapproval of that which
has happened before. Voters may reject what they have known; or they may approve what they have known. They are not likely to be attracted in great numbers
by promises of the novel or unknown. Once innovation has occurred they may
embrace it, even though they would have, earlier, hesitated to venture forth to
welcome it. (Key 1966, 61)
1.6. REWARD AND PUNISHMENT: THE LOGIC OF SANCTION
25
In effect, he argues that election results must not be read as an endorsement of the policies
of the winning party, but as a judgement of the competence of the previous government (Key
1961, 473–474; 1964, 543; 1966, 51–52).
The most striking characteristic of this model is that it asserts that voters are outcomedriven rather than policy-driven. What this means is that citizens do not compare the policy
packages put forward by the various parties in order to ascertain which is the best offering. It is
assumed that they not have the resources to do this effectively (Fiorina 1981, 45). Accurately
weighing the different claims as to which course of action will most benefit the economy4 is
likely to be a demanding task for even highly educated voters. Even reading all of the party
manifestos consumes more time than many people are likely to be willing to spend. On the
other hand, most citizens are likely to have some idea of how well the economy has been
functioning. Recessions do not tend to go unnoticed after all. In other words, comparative
policy assessments are difficult to come by but assessments of government performance—
assuming a link between economic outcomes and government policy—are readily accessible
(9). The sanctioning voter, according to this line of thinking, thus makes the best use of the
available information.
A second key characteristic of the sanctioning model is that it is explicitly retrospective
in orientation. According to this theory, voters are not merely looking at past performance
in order to inform their future expectations. A vote is actually an expression of the voter’s
assessment of the government’s past performance. Key (1961, 473) describes the public as
‘speak[ing] in disapprobation of the past policy or performance of an administration’. Elsewhere he claims that ‘[r]etrospective judgments by the electorate seem far more explicit than
do its instructions for future action’ (Key 1964, 643). Thus according to this model of economic voting, citizens use their vote to signal which outcomes they deem acceptable and which
they do not. For example, even if a voter expected that the economy would improve from a
recent crisis under the incumbent government, the sanctioning model predicts that this voter
would vote against that incumbent anyway. Doing otherwise would send the wrong message.
As Duch and Stevenson (2008, 11) put it, politicians ‘anticipate that voters will sanction them
if they underperform. And, to maintain the credibility of this threat, voters punish incumbents
at the polls when retrospective economic performance is substandard.’
This illustrates the vote choice logic at the heart of the sanctioning model and it is a rational
one. Although unremarkable today, this was an unexpected position to take in the wake of
4
Voter utility is certainly not limited to economic issues and there is no reason why a sanctioning voter would
not treat failures in other areas equally harshly but non-economic issues are beyond the scope of this thesis, so this
analysis will assume that the issues in question are economic ones.
26
CHAPTER 1. THEORY OF ECONOMIC VOTING
highly influential work such as Campbell et al. (1960), which had argued that vote choice was
driven primarily by party identification and not rationality. Key (1966, 7) admitted as much
when he described his own argument as ‘perverse and unorthodox’ before adding: ‘To be sure,
many individual voters act in odd ways indeed; yet in the large the electorate behaves about as
rationally and responsibly as we should expect, given the clarity of the alternatives presented
to it and the character of the information available to it.’ Others have produced formal models
of rational individual behaviour which would produce an aggregate retrospective voting effect
of the sanctioning kind. The basic idea is that voters must punish unsatisfactory outcomes in
order to incentivise incumbents to act in the public interest (Barro 1973; Ferejohn 1986). It
is worth noting that, for this to be effective, voters must even punish governments for poor
economic outcomes even if they are personally unaffected. This has led Ferejohn (1986, 23)
to argue that sociotropic voting is an essential feature of retrospective voting (23).
The sanctioning model is appealing for its simplicity. It is also important to understand
because it underpins so much of the early economic voting research. Nonetheless, it is not
the model that will be used by this thesis. For one thing, as has already been discussed,
this thesis is testing a prospective theory of economic voting, while the sanctioning model
assumes a thoroughly retrospective orientation on the part of the voter. The empirical evidence
that prospective voting exists (for example Erikson, MacKuen and Stimson 2000; Michelitch
et al. 2012) cannot be explained by the sanctioning model. The next section introduces an
alternative model, the selection model.
1.7
Competent government: the logic of selection
The selection model offers an alternative explanation for the phenomenon of economic voting. The differences between the two models stem from differing assumptions about what
voters intend when casting their votes. Whereas the sanctioning model asserts that voters are
primarily casting a judgement upon the incumbent government, the selection model posits
that voters are actually trying to select the party most likely to maximise their utility. For
economic voters in particular, this means selecting the party which can most reasonably be
expected to deliver positive economic outcomes. This model stems from Downs’ An Economic
Theory of Democracy, in which he explains the basic challenge of voting as one of selection:
When a man votes, he is helping to select the government which will govern him
during the coming election period (i.e., period t + 1). Therefore as we have just
1.7. COMPETENT GOVERNMENT: THE LOGIC OF SELECTION
27
shown, he makes his decision by comparing future performances he expects from
the competing parties. But if he is rational, he knows that no party will be able to
do everything that it says it will do. Hence he cannot merely compare platforms;
instead he must estimate in his own mind what the parties would actually do were
they in power. (Downs 1957, 39)
The point being made here is that if voters are assumed to behave rationally and to be motivated by selection, rather than sanction, then voter behaviour can only be understood by
knowing how voters measure the expected utility of each political party. Furthermore, this is
not simply a question of comparing party manifestos because they are not reliable indicators of
behaviour in office. This differs greatly from the sanctioning model, which argues that voters
do not think about a party’s future actions but only about its past performance.
According to the selection model, voters determine the expected utility associated with
each party by looking at their past performance. It is argued that this is precisely what a
rational voter would do:
Since one of the competing parties is already in power, its performance in period
t gives him the best possible idea of what it will do in the future, assuming its
policies have some continuity. But it would be irrational to compare the current
performance of one party with the expected future performance of another. For a
valid comparison, both performances must take place under the same conditions,
i.e., in the same time period. Therefore the voter must weigh the performance
that the opposition party would have produced in period t if it had been in power.
(39–40)
In other words, voters compare the incumbent party’s current performance—using their retrospective knowledge—to the hypothetical performance of an opposition party. It is important
to note that these assessments are not based purely upon retrospective knowledge. Downs
(1957, 41–45) explains that these assessments are modified by what he calls the ‘trend factor’
and ‘performance ratings’. The trend factor refers to the consideration that voters give to
current events and is the device used to project retrospective knowledge into the future. For
example, if it is clear that the economy is on the verge of a recession, voters are not going
to ignore this simply because the economy had been otherwise well-managed up until this
point. Performance ratings are only used to discriminate between two parties proposing indistinguishable platforms. In this case, voters can only contrast the past effectiveness of the
parties.
28
CHAPTER 1. THEORY OF ECONOMIC VOTING
The model just described was not introduced as a freestanding theory but rather formed
part of Downs’ comprehensive theory of political behaviour. It was Fiorina (1981) who developed these ideas further and presented them as a coherent alternative to the sanctioning
model. His description of the model is succinct: ‘In deciding how to vote, the rational citizen
would compare the performance of the incumbent administration to the platform promises
of the challenger rather than compare both sets of platform promises’ (46). One key difference between the sanctioning and selection models is that the latter explicitly assumes that
a party’s past behaviour is a reliable predictor of its future behaviour (Downs 1957, 96–113;
Fiorina 1981, 45), whereas the logic of sanctioning implies that voters expect to be able to
condition the parties to behave as intended. If the parties did not respond to such cues, then
there would be no reason to punish or reward them electorally. Another difference between
the models is that the sanctioning model assumes that voters typically cannot compare policy
platforms, whereas that selection model assumes that this can be done in principle but that
it is too expensive, so retrospective information is a cheap and effective substitute (Fiorina
1981, 45–47).
The selection model has been a popular choice for empirical research into economic voting
beginning with Kramer (1971, 134), who argued that the ‘past performance of the incumbent
party in particular gives some indication of what it would do if returned to office, and of the
effectiveness of its policies and personnel’. Even his critic Stigler (1973, 165) agreed that a
rational voter should predict future performance based on past performance. The selection
model continues to inform recent work, such as Duch and Stevenson (2008). One of the
strengths of this theory is that selection effects have proven capable of explaining why incumbent legislators can be so difficult to unseat (Ashworth 2005; Ashworth and de Mesquita
2008; Gowrisankaran, Mitchell and Moro 2008). The argument is that voters tend to return
high quality legislators and replace low quality ones, so the median incumbent tends to be
of a higher quality than the median candidate. This effect cannot be explained by the sanctioning model. It should be mentioned that there are some recent experiment-based studies
finding support for a sanctioning effect (Huber, Hill and Lenz 2012; Woon 2012). Nonetheless, the evidence for selection effects is strong. It is also possible that the two effects exist
simultaneously (Fearon 1999, 56–57).
This thesis is informed by the selection model. This may be surprising, since the selection
model is a retrospective theory and it is has been stated that the thesis is operating from a prospective perspective. These approaches are not contradictory, however. The selection model
1.8. POLITICAL CONTEXT AND THE INSTABILITY PROBLEM
29
assumes that voters have a prospective orientation, in that they seek to select the party most
likely to produce the best outcomes. What makes it a retrospective theory is that it posits that
voters use retrospective knowledge as a shortcut for forming their view as to which party this
is. As mentioned earlier, Downs expects voters to augment their retrospective evaluations with
a trend factor, reflecting their assumptions about the future course of present events. Given
this framework, it is not unreasonable to interpret a voter’s reported prospective economic assessment as a projection of his or her retrospective economic assessment using a trend factor.
The prospective interpretation of the selection model used in this thesis thus predicts that the
retrospective and prospective economic evaluations are typically moderately correlated but
that the prospective evaluations are the stronger predictor of party support.
1.8
Political context and the instability problem
An enduring problem for economic voting research has been the instability of its findings.
It has frequently been the case that promising efforts to measure the economic voting effect
have produced wildly different estimates in different countries and sometimes even the same
countries at different times. Explaining this instability has increasingly become a focus for
research in this area (Lewis-Beck and Paldam 2000, 113–114; Dorussen and Palmer 2002, 1–
5). This instability is particularly salient for a comparative study such as this one because it is
clearly not reasonable to assume that the economic vote is the same for all of the different
countries under study or even that it is stable across time. An understanding of political
context is thus important and this section reviews what is already known about the impact
of context on the economic vote. Non-contextual explanations for the instability problem
have also been proposed. For example, van der Brug, van der Eijk and Franklin (2007, 16)
argue that model misspecification is the principle cause of this instability5 but even if true,
this does not mean that context does not also play a role. Voter heterogeneity has also been
proposed as a possible cause of the instability problem (Dorussen and Palmer 2002, 7) but
these possibilities are beyond the scope of this thesis.6 One way to take into account contextual
differences is simply to model them without trying to explain them. For example, if the model
allows for the possibility that, say, Poland and the Czech Republic have different levels of
5
In particular, they argue that the instability results from the failure to take into account party competition.
Their recommendation is to model party support explicitly rather than just vote choice and this is the approach
taken in this thesis, as was discussed earlier in this chapter.
6
As well as adding considerable extra complexity and consuming degrees of freedom, the required data is not
readily available: ‘Information about relevant sources of voter heterogeneity is much more sparse, basically only
available for a few countries and measured at irregular intervals’ (Dorussen 2002, 309).
30
CHAPTER 1. THEORY OF ECONOMIC VOTING
economic voting then this can be accounted for. This is the method used by Chapter 3. Of
course this is merely an empirical fix and does not solve the theoretical problem, as it sheds
no extra light on these differences.
A more sophisticated approach requires an understanding of the causes of the variation
between political contexts. The most influential theory of contextual variation in the economic
vote is the theory of clarity of responsibility, which was introduced to the literature by Powell
and Whitten (1993) and further developed by Whitten and Palmer (1999). According to this
theory, economic voting is more likely to take place in contexts where voters can reasonably
hold a particular incumbent responsible for the condition of the economy. While this can be
reasonably done in a country like the United Kingdom, where there is very little to hinder
prime ministers from taking whatever course of action they feel is required, this is not necessarily the case everywhere. For example, the German chancellor frequently has to negotiate
policies with coalition partners as well as gain the support of the upper house if certain forms
of legislation are required. Furthermore, certain policies may be beyond the jurisdiction of the
federal government and require the support of the states to be implemented. This implies that
it is less clear in Germany which government and who within the government, if anyone at all,
ought to be held responsible for any unwelcome economic events. Accordingly, the argument
runs, German voters ought to be less inclined to vote economically than their British counterparts. Much of the work in this area has focused on precisely how clarity of responsibility
should be measured. In its original conception, the key variables were the voting cohesion of
the incumbent parties, the strength and inclusiveness of the parliamentary committee system,
opposition in any constitutionally significant upper house, and the presence of a minority or
coalition government (Powell and Whitten 1993). Alternative measures have been proposed
but these are discussed in Chapter 4, where a clarity of responsibility model is constructed.
An alternative contextual theory of economic voting has been developed by Duch and
Stevenson (2008). Basing their model on the rational expectations literature, and particularly
Alesina and Rosenthal (1995), they distinguish between two types of economic growth shocks.
These are competency shocks, which derive from the actions of the incumbent government,
and exogenous or nonpolitical shocks, which do not (Duch and Stevenson 2008, 132–133).
The strength of the economic vote depends on the proportion of shocks which are competency shocks (138). Duch and Stevenson (2008, 147) argue that this proportion is related to
the mix of ‘electorally dependent’ and ‘nonelectorally dependent’ economic decision makers.
Electorally dependent decision makers include both elected officers themselves and also the
1.9. THE GREAT RECESSION
31
government agencies that answer to them. Nonelectorally dependent decision makers are
‘everyone else whose decisions might impact the economy, including individuals, firms, interest groups, nonelectorally dependent (entrenched) bureaucrats, foreign leaders, the WTO,
and many more’ (139–140). The argument is that in contexts where economic decisions are
largely driven by elected officials or bureaucrats responsible to them, a high proportion of
economic shocks will be competency shocks and the economic vote will be correspondingly
high. By contrast, in contexts where many economic decisions are made by statutory bodies or
international agencies or are otherwise outside of the control of the incumbent government,
the economic vote will be considerably lower. There are some similarities with the clarity of
responsibility theory, in that both theories relate the strength of the economic vote to political control but there are importance differences. Clarity of responsibility theory is concerned
with the degree to which the dominant government party has to negotiate with other parties
to implement government policy, whereas this theory is concerned with the degree to which
economic policy is influenced by non-political actors. Although this is a promising theory, it is
not used by this thesis, mainly because it is based on some of the opposite assumptions from
those made here. For example, theirs is a retrospective vote choice theory, whereas this thesis
is based on a prospective party support theory.
1.9
The Great Recession
The central question motivating this thesis is whether the Great Recession affected the economic vote in the European Union. Of course, classic economic voting theory already predicts
a strong electoral response, since the recession was both widespread and severe. The question is whether the strength of the economic vote exceeded this expectation. There are two
threads in the literature that suggest that this might be the case. The first thread is the idea of
grievance asymmetry, which claims that the response to a good event is not necessarily of the
same magnitude as the response to a bad event. It is typically argued that the response to a
negative stimulus is stronger than the response to a positive one. This argument appeared as
early as Campbell et al. (1960, 555), who wrote:
Compare the change wrought in party fortunes in either of these cases [the panic
of 1893 and the Great Depression] with the change occurring when a party already
in office witnesses a surge of economic prosperity. This prosperity clearly benefits
the administration party, but it has nothing like the magnitude of the effect that
32
CHAPTER 1. THEORY OF ECONOMIC VOTING
would result from economic distress. A party already in power is rewarded much
less for good times than it is punished for bad times.
This idea was tested by Bloom and Price (1975), who extended previous economic voting
models to account for this possibility. In an aggregate study of US House of Representatives elections, they found that recessions were associated with reduced incumbent support
but that the opposite was not true for economic recoveries. Similar results have occasionally
been found elsewhere. For example, a grievance asymmetry was also found at Danish elections between 1985–92 using an individual-level model (Nannestad and Paldam 1997b) and
a study of Hungarian voters in 1997 found a similar effect (Duch 2001). Looking beyond
studies of economic voting specifically, it has also been found that the media and public opinion response to negative economic news is stronger than the response to positive economic
developments (Soroka 2006). What these findings suggest is that economic voting is weaker
than expected during times of prosperity. It is not therefore too much of a stretch to hypothesise that economic voting might be stronger than expected during a recession of exceptional
severity.
The second relevant thread in the literature pertains to the salience of economic issues.
The argument is that voters are more likely to vote economically when the economy is a salient issue for some reason and that the salience of economic issues is linked to economic
performance. Consequently, economic voting is expected to recede when the economy is performing satisfactorily. Duch and Stevenson (2008, 171) summarise the argument thus: ‘when
the economy is in equilibrium, or possibly even out of equilibrium but in a positive direction, economic evaluations are likely to play a much less important role in the vote decision.’
This idea has some empirical support, with survey data from the 2008 US presidential election (Singer 2011a) and cross-national survey data from 38 countries in the years 2001–2006
(Singer 2011b) showing that the salience of the economy is greater in contexts where economic conditions are poor and among individuals who are personally affected by economic
privations. A follow-up study using aggregated survey data from elections in 43 countries
in the years 2001–2011 found further evidence that economic issues tend to be most salient
when the economy is less healthy (Singer 2013). It is interesting that the implications of the
asymmetry and salience arguments are so similar despite the differences in logic. Whether it
results from qualitative differences in the way people respond to different kinds of information or from the fluctuating salience of economic issues, these literatures predict a stronger
economic vote during bad times than good times. This leaves open the possibility that a severe
1.9. THE GREAT RECESSION
33
global recession ought to produce a still stronger economic vote, beyond that which would be
predicted by classical economic voting theory.
The handful of comparative studies that have been published are largely based on aggregate data, which is probably because comparable aggregate data is more quickly available than
cross-national survey data. Several studies investigated the relationship between government
vote share and economic indicators. One study of EU countries in 2008–2011, which also
included aggregated perceptions data, found evidence of an economic voting effect during
the crisis, noting that most incumbents lost support, but their study design does not include a
baseline for comparison so it is not clear whether this economic vote is stronger or weaker than
expected (LeDuc and Pammett 2013). Similarly, a study of elections in 28 OECD countries in
the years 2007–2011 found clear evidence of an economic voting effect during the crisis, but
once again there is no baseline to compare this to (Bartels 2014). Other studies did compare
Great Recession voting behaviour to an earlier baseline. Hernández and Kriesi (2015) looked
at the relationship between economic indicators and the vote share of the dominant governing
party at post-crisis elections in 30 European countries. They found different patterns in different parts of the continent, with an economic vote typically exceeding baseline expectations
in Western European countries but not in Central and Eastern European countries, where the
response appeared to be more moderate. Likewise, Bouvet and King (2016) investigated the
relationship between incumbent vote share and economic indicators in 32 OECD countries at
elections between 1975 and 2013, with particular attention paid to the Great Recession. They
found evidence that voters were more likely to punish right-wing parties than left-wing parties
during the recession. A further study looked at economic opinion in eleven Western European
countries in the period 2007–2011, finding that economic opinion typically reflected the economic reality and that the perceptions of voters during the Great Recession were by and large
not an overreaction (Anderson and Hecht 2014).
In the few years since the Great Recession, a literature has emerged on economic voting
during that time. The bulk of these have been studies of individual countries. Many of these
studies seek to establish whether there was an economic voting effect in some country during
the Great Recession. For example, a crisis economic voting effect was found in the United Kingdom, as well as qualified support for austerity policies, with evidence that this support is likely
to wane unless the economy starts to improve (Borges et al. 2013). A study of British voters’
attributions of responsibility for the crisis found evidence that Conservative partisans were
more likely to blame the government for the crisis compared to Labour partisans, who tended
34
CHAPTER 1. THEORY OF ECONOMIC VOTING
to blame financial institutions (Hellwig and Coffey 2011). There is also evidence that Labour
Party support in the years 2004–2009 was more related to unemployment among low income
earners and to inflation among high income earners (Palmer and Whitten 2011; Palmer, Whitten and Williams 2013). On the other hand, Duch and Sagarzazu (2014) used panel studies
from the UK and Germany to study perceptions of the Great Recession and economic voting,
finding little difference in the economic vote of rich and poor voters, despite poorer voters
feeling the effects of the recession more strongly. There is thus agreement that economic voting occurred in the UK during the crisis but not as to whether rich and poor voters behaved
differently.
Countries that were particularly heavily affected by the recession have tended to attract
scholarly attention. Unsurprisingly, economic voting appears to have been a feature of elections in these countries as well. For example, a Greek study used a combination of individual
and aggregate data to examine the economic vote in Greece both before and during the Great
Recession (Nezi 2012), finding evidence of economic voting throughout the crisis, although a
large shift in economic perceptions is necessary to produce a change in government. Turning
to Spain, Fraile and Lewis-Beck (2012) use survey data from 1982 to 2008 to show that economic voting does exist in that country, despite some contrary claims in the literature. In a later
study, they then look at the 2008 election specifically, finding a clear economic voting effect
during the economic crisis, although they unfortunately do not compare 2008 directly with
previous years (Fraile and Lewis-Beck 2013). A study of post-election surveys from Italian
elections between 1990 and 2008 found that instability is not an inherent characteristic of
Italian politics but rather the result of valence political behaviour such as economic voting
(Bellucci 2012). And Marsh and Mikhaylov (2012) examined the 2011 Irish election, which is
particularly notable for the extreme loss of support of the previously highly successful incumbent party Fianna Fáil. While they naturally place importance on the specific circumstances of
the crisis and its aftermath, they unsurprisingly find support for a link between the economic
and political events of that time.
Economic voting is certainly not limited to those countries that suffered exceptionally from
the recession. Germany is a particularly interesting case because it was governed by a grand
coalition of the major centre-left and centre-right parties at the time of the recession, so economic voters potentially had a much more difficult to choice to make. In other words, despite
the severe economic crisis, there was no viable alternative to the incumbent government. Anderson and Hecht (2012) used panel survey data from before and after the 2009 German
1.9. THE GREAT RECESSION
35
elections to examine the economic vote in this context, finding that voters personally affected
by the recession used several strategies to shift their support away from the parties of the
grand coalition but that sociotropic voters tended not to continue supporting the coalition.
Clarke and Whitten (2013) used survey data of Germans in 2009 to compare valence models, which are a generalisation of economic voting models, to spatial models, finding that the
valence model was best equipped to explain vote choice. These results suggest that, despite
being governed by a grand coalition, economic voting still occurred in Germany during the recession. Economic voting effects during the Great Recession have also been found in Sweden
(Martinsson 2013; Lindvall, Martinsson and Oscarsson 2013), Portugal (Freire and SantanaPereira 2012), Turkey (Çarkoğlu 2012) and Hungary (Stegmaier and Lewis-Beck 2011).
Although this literature clearly establishes that economic voting during the Great Recession took place across many and varied European countries, there are two things that are
missing. The first of these is a comparative, individual-level study of economic voting behaviour during the crisis. This is important because there is only so much that can be learnt from
single-country and aggregate studies. Single-country studies are limited because they cannot
possibly test or control for contextual effects, such as clarity of responsibility. Moreover, when
looking at only one country, there is always the possibility, however remote, that any effect
that is found is not a typical behaviour but one characteristic to the particular institutions
of that country. Even though economic voting effects have been found in so many individual
European countries, it is not yet clear whether these effects are similar in size, since each study
uses different data and different methods. Aggregate studies are also limited because there
are so many different individual behaviours that can explain any observable aggregate effect.
Therefore, in order to gain a deeper understanding of the economic vote during this crisis,
a comparative individual-level study is needed. The published study that comes closest to
meeting these criteria is Whiteley (2016), who used European Social Surveys data to compare
voters from 21 countries in 2006 and 2012, in other words shortly before and shortly after the
crisis. He modelled incumbent support according to spatial, valence and cleavages theories of
voter behaviour, finding more similarities than differences before and after the crisis in each
case. This was not, however, a study of economic voting specifically and leaves open questions
about the nature and level of economic voting during the crisis.
The second thing missing from this literature is a study specifically designed to contrast
the economic vote before, during and after the recession. This is important because such a
study would make it possible to test the hypothesis that the economic vote was stronger than
36
CHAPTER 1. THEORY OF ECONOMIC VOTING
expected during the crisis. Without a time comparison, the possibility cannot be excluded that
the economic vote seen during the crisis was simply a continuation of the normal economic
voting response that is seen at other times. There have been a small number of single-country
studies that have specifically made this comparison but no comparative studies. For example,
Martinsson (2013) found that the economic vote in Sweden was unusually strong at the postcrisis 2010 election and Lindvall, Martinsson and Oscarsson (2013) compared the economic
vote in Sweden during the Great Recession to that during an earlier crisis in 1991–1993,
finding that economic status was a stronger predictor of economic voting behaviour during
the Great Recession than it had been during the earlier crisis. On the other hand, Freire and
Santana-Pereira (2012) found the economic vote in Portugal to be slightly depressed at the
2009 elections compared to previous years and Çarkoğlu (2012) found a stronger economic
vote in Turkey in 2007 than in 2011. Others still have argued that the immediate economic
voting response to the recession was muted and that it was not until the implementation of
unpopular austerity programmes that voters started to turn against their governments (Bermeo
and Bartels 2014, 3–4). In the face of such contradictory results, there is a need for a systematic
cross-national individual-level study designed to compare the economic vote before, during
and after the Great Recession, a need which this thesis fills.
1.10
Conclusion
The basic idea behind economic voting theory is that voters are more likely to support incumbents when the economy is performing satisfactorily than they are when it is performing
poorly. This basic idea is simple but there are many different theoretical behaviour models that
could support this idea. This chapter has reviewed the key theoretical debates in the economic
voting literature and discussed the empirical evidence in support of the various positions. Although some of these questions are more settled than others, it is not possible to proceed
without making some assumptions about the nature of the economic vote. This theoretical
model is a selection model, which means that it is assumed that voters use their votes in order
to select the best possible government rather than to send a reward or punishment signal to
the incumbent. The assumption that voters are not entirely egocentric but rather sociotropic
economic voters is probably the least controversial, in light of the large body of evidence that
sociotropic economic voting exists. This thesis is based on a prospective theory of economic
voting. This is partly for data reasons, as the next chapter will show. Although retrospective
1.10. CONCLUSION
37
Figure 1.2: Outline of economic voting theory
This illustrates the specific theory of economic voting used in this thesis. A voter’s level of
support for a particular party is influenced by his or her perceptions of the economy and the
direction of this influence depends on whether that party is incumbent or not. The strength of
this economic vote is in turn influenced by contextual factors such as clarity of responsibility
and the Great Recession.
Figure 1.3: Vote choice process
Vote choice is not modelled as being directly influenced by the economic vote. Rather, the
economic vote independently affects a voter’s level of support for each party and these levels
of support determine the final vote choice.
38
CHAPTER 1. THEORY OF ECONOMIC VOTING
studies tend to predominate, the empirical evidence does not strongly favour either alternative. This thesis prefers an economic perceptions model over an objective indicators model
because, despite the criticisms of perceptions models, individual voters are not necessarily objective and do filter information in idiosyncratic ways. In light of the fact that almost all of the
countries under study have multiparty systems, a party support model is preferred to a vote
choice model. Finally, it is theorised that the strength of the economic vote may be affected
by contextual features such as clarity of responsibility or the Great Recession, as an example
of a deep international economic crisis.
Taken together, these features describe a cohesive theory of the economic vote, which is
outlined in Figure 1.2. As the diagram shows, the level of support that a particular voter has
for a particular party is influenced by both the prospective economic assessment of that voter
and the incumbency status of the party—which may have more than two levels if importance
within the governing coalition is taken into account. These two influences interact with each
other, as the effect of a particular economic assessment on support for a government party
is expected to be different from the same effect on support for an opposition party. It is this
interaction of effects that is referred to as the economic vote. It is worth noting that, although
the basic principle of economic voting appears straightforward, even obvious, it necessarily
describes an interaction effect.7 This explains some of the complexity of economic voting
models, including those introduced later in this thesis. This economic voting effect is itself
affected by contextual factors, such as the clarity of responsibility in a particular country and
events such as the Great Recession. It is of course the latter that is the focus of this thesis,
the primary objective of which is to establish whether that recession did indeed have an effect
on the economic vote. Figure 1.3 illustrates that a citizen’s final vote choice is determined by
his or her level of support for each of the parties that could potentially receive the vote. It is
assumed that voters simply select the party that they currently support the most.
This chapter has also discussed the existing literature on economic voting in the Great
Recession. Although there have been a number of studies of economic voting in individual
countries during that time frame as well as a handful of aggregate-data cross-national studies,
there are limits to what can be learned from these. Even among these studies, few explicitly
contrast what was observed during the crisis to what was observed at other times. In order
to gain a fuller understanding of how the economic vote was affected by the Great Recession,
7
Except in the special case of a two-party system, where support for the government and support for the opposition can be reduced to a single difference in support variable and the incumbency effect can be eliminated entirely.
This special case describes the United States but is otherwise rare, especially among the European countries studied
in this thesis.
1.10. CONCLUSION
39
there is a need for an individual-level cross-national study comparing the economic vote before, during and after the crisis. This thesis fills this need. The next chapter discusses the data
used to undertake this study as well as the specific methods used to analyse that data.
Chapter 2
Measuring the economic vote
The previous chapter introduced the theory of economic voting and explained the specific
theoretical framework that informs this thesis. The following chapters use this framework to
develop statistical models describing the economic vote, which are then used to test hypotheses
that shed light on the research questions driving this thesis. The purpose of this chapter is to
prepare the ground for these statistical models by describing the data sources that are used,
the key variables that are analysed and the methods that are used to perform this analysis.
This chapter focuses on the details that are relevant for the entire thesis. Other details that
are more specific to individual analyses are discussed as needed in the chapters where they
are relevant.
As the purpose of this thesis is to examine whether and how economic voting behaviour
was affected by the Great Recession, it is important to be able to measure the economic vote
both during the crisis and at other times. As will be shown, it happens that the European
Election Studies have conducted multinational voter surveys at ideal times for making such
a comparison. These surveys include questions that indicate respondents’ levels of support
for various parties as well as their economic assessments. When combined with incumbency
data from other sources, this makes it possible to model the economic vote according to the
framework introduced in the previous chapter. It will also be shown that there is a structure
to this data, which has to be taken into account by the statistical methods used. This structure
is accounted for by using multilevel analysis.
This chapter begins by introducing the data sources that have been used, and shows how
the timing of the three survey waves used coincides with the course of the Great Recession.
Following this, there is a discussion of the particular measurements used. This includes a
description of the key variables and the precise question wording where appropriate, along
with some comments on the distribution and level of measurement. Finally, the methods used
to analyse all of this data are introduced and the reasons behind these choices are given.
41
42
CHAPTER 2. MEASURING THE ECONOMIC VOTE
2.1
Data sources and timing
As the previous chapter explained, this study is intended to be an individual-level crossnational study comparing the economic vote before, during and after the Great Recession.
In order to achieve these goals, there is a need for survey data satisfying three key criteria:
it must include the necessary questions to measure the economic vote, it must be comparable across nations and it must consist of appropriately timed survey waves. Fortunately, the
European Election Studies satisfy all three of the requirements.1 The EES surveys are a series of
post-election surveys, which are collected in each European Union member state shortly after
every European Parliament election. As will be seen later in this chapter, these surveys include the required questions. These surveys are specifically designed to be comparable across
countries. They are based on a single questionnaire which is translated into the appropriate
local language or languages. It should be noted that this is not a study of European Parliament
elections but rather of hypothetical national elections. The advantage of this approach is that
it makes it possible to measure the economic voting tendency across all of the countries at
the same point in time, whereas the actual national elections are invariably spread out across
many years, which makes it very difficult to compare countries. Although the EES surveys correspond to European Parliament elections, they also include questions about national politics
and these are the questions that are used.
The EES surveys are also suitably timed. This thesis uses the 2004, 2009, and 2014 waves
(EES 2004, 2009, 2014; Schmitt et al. 2009; van Egmond et al. 2010; Popa et al. 2015), which
were all conducted shortly after the European Parliament elections in those years. Figure 2.1
shows the quarterly GDP growth rate across the current 28 member states of the EU. The
Great Recession began in the aftermath of the global financial crisis of 2007–2008 and by the
end of 2008 the EU was in recession, following the common definition of a recession as two
consecutive quarters of negative growth. The dashed vertical lines show when the European
Parliament elections occurred and correspondingly when the three EES surveys were collected.
As the figure shows, the 2004 election took place well before there was any sign of crisis,
while the 2009 election took place shortly after its peak. Although there was a brief apparent
recovery, conditions worsened again in 2011, with a further recovery in 2013. By the time
of the 2014 European Parliament elections, the EU had been out of recession for eighteen
1
The survey data used in this thesis was originally collected by the 2004, 2009 and 2014 EES research groups.
Those studies have been made possible by various grants. Neither the original collectors of the data nor their
sponsors bear any responsibility for the analyses or interpretations made here. The data is available from the
home page of the European Election Study (http://eeshomepage.net/) and from the Archive Department of GESIS–
Leibniz Institute for the Social Sciences (http://www.gesis.org/).
2.1. DATA SOURCES AND TIMING
43
Figure 2.1: GDP growth in the European Union, 2000–2015
1.0
GDP growth (%)
0.5
0.0
-0.5
-1.0
-1.5
-2.0
-2.5
2001
2003
2005
2007
2009
2011
2013
2015
time
This shows the seasonally adjusted GDP growth rate across the entire 28-country European
Union by quarter, each compared to the previous quarter. The dashed vertical lines show when
the European Parliament elections took place in 2004, 2009 and 2015. Source: OECD
months. These three time points thus offer a before, during and after snapshot of the Great
Recession. It should be noted that this is the overall picture, as the recession took a slightly
different course in each country. Nonetheless, the only two EU member states to avoid the
recession altogether were Poland and Slovakia.
The EES surveys typically encompass each member state of the EU. The EU was enlarged
during the time frame of this study, with Bulgaria and Romania joining in 2009 and Croatia
joining in 2014. Since it would be difficult to separate any Great Recession effect from that
of joining the EU, these countries have been excluded from this study. This leaves the 25
countries that have been member states since 2004.2 The political parties included in the study
are those for which there is a measure of the dependent variable, party support. These are the
parties that were chosen by the EES coordinators in each country as being the most important
parties in that country. These typically include all of the parties that could reasonably expect
2
Unless otherwise specified, the Swedish data from 2004 has been excluded from the analysis. This is because
some variables were measured on a different scale from that used elsewhere. Most problematically, this includes
the economic assessment questions, which were measured on a three-point scale instead of a five-point scale. Since
these responses could not be reconciled satisfactorily with those from other countries, the decision was made to
ignore the Swedish survey in that year. Sweden is however still included in the other years. Also, whenever party
support is the dependent variable, Belgium, Lithuania and Luxembourg in 2004 also had to be excluded because
the party-specific questions were not asked in those countries.
44
CHAPTER 2. MEASURING THE ECONOMIC VOTE
Table 2.1: Sample size and interview mode of EES surveys
Country
Austria
Belgium
Cyprus
Czech Republic
Denmark
Estonia
Finland
France
Germany
Greece
Hungary
Ireland
Italy
Latvia
Lithuania
Luxembourg
Malta
Netherlands
Poland
Portugal
Slovakia
Slovenia
Spain
Sweden
United Kingdom
Sample
1010
889
500
889
1317
1606
900
1406
596
500
1200
1154
1553
1000
1005
1335
—
1586
960
1000
1063
1002
1208
2100
1500
2004
Mode
phone
mail
face
face
phone
face
phone
unknown
phone
phone
face
mail
mail
face
face
phone
—
mail
face
phone
face
phone
face
face
phone
Sample
1000
1002
1000
301/720
1000
300/707
1000
1000
1004
1000
300/705
1001
1000
300/701
300/705
1001
1000
1005
302/700
1000
301/715
1000
1000
1002
1000
2009
Mode
phone
phone
phone
phone/face
phone
phone/face
phone
phone
phone
phone
phone/face
phone
phone
phone/face
phone/face
phone
phone
phone
phone/face
phone
phone/face
phone
phone
phone
phone
2014
Sample Mode
1114
1084
530
1177
1085
1087
1096
1074
1648
1085
1104
1081
1091
1055
1096
538
544
1101
1223
1033
1095
1143
1106
1144
1421
face
face
face
face
face
face
face
face
face
face
face
face
face
face
face
face
face
face
face
face
face
face
face
face
face
The 2004 wave used telephone, mailback and face-to-face interviews, depending on the country. The 2009 wave used telephone interviews in every country, which were supplemented by
face-to-face interviews in certain countries. All interviews in the 2014 wave were conducted
face-to-face. Source: EES
to win a seat at a general election. Table 2.1 shows the sample size and interview mode of
each survey in each of the three waves. At least five hundred responses have been collected
in each country and one thousand responses is typical, except for some particularly small
countries. Malta was not surveyed at all in 2004. Various interview modes have been used,
with telephone and face-to-face modes the most common. A mix of modes was used in 2004,
including mailback surveys in four countries. It is not known which method was used in France
in that year.3 Interviews were predominately conducted by telephone in 2009 but these were
supplemented with face-to-face interviews in several countries where a representative sample
could not be otherwise obtained (van Egmond et al. 2010, 5). All interviews were face-toface in the 2014 wave. Although there are a mix of modes in this data, mode effects are not
3
The codebook simply states that ‘France did not report the technical implementation of its study’ (Schmitt
et al. 2009).
2.1. DATA SOURCES AND TIMING
45
expected to influence the findings, since economic voting is not something that respondents
might be embarrassed to admit. In fact, it cannot be determined if any individual respondent
is an economic voter since these patterns are only observable in the aggregate.
Although the EES survey data forms the primary dataset for this thesis, some contextual
data has been drawn from other sources. The main contextual information required is the
incumbency status of each party at the time the surveys were collected. This information
was taken from the Parliaments and Governments Database (Döring and Manow 2015), also
known as ParlGov, which aggregates information about election results and parliament and
cabinet composition from EU and other countries. Since multiple governments may be in
power in a country during a single calendar year, it was necessary to decide precisely which
dates to use to measure incumbency. In most cases, the measurement date chosen was the first
day of the survey fieldwork, namely 14 June 2004, 8 June 2009 and 22 May 2014. Whichever
party held the post of head of government on this date is designated the prime minister’s party4
in that year. Parties holding other positions in the government are designated cabinet parties.5
All other parties are coded as opposition parties. This approach makes sense because survey
respondents were asked to reflect on the government currently in power at the time.
In two cases, however, slight adjustments were made in order to accommodate the peculiar
circumstances. On 7 May 2009, the Czech government was replaced by a nonpartisan caretaker government, in advance of elections in October of that year (Linek and Lacina 2010).
Since this caretaker government was not officially associated with any particular parties, it is
treated in this thesis as an extension of the preceding coalition government led by the Civic
Democratic Party. Similarly, the Prime Minister of Hungary Ferenc Gyurcsány resigned from
the minority Hungarian Socialist Party (MSZP) government on 21 March 2009, being replaced
by Gordon Bajnai on 14 April (Várnagy 2010). Although he was an MSZP appointment and a
government minister, Bajnai was a compromise candidate proposed to appease other parties,
and he is recorded as a nonpartisan prime minister in the ParlGov database. In light of these
circumstances, this thesis treats the MSZP as the party holding the office of prime minister
at the time of the 2009 surveys. Other cases worth special mention are Luxembourg in 2004
and 2009 and Belgium in 2014. In these instances, the national elections coincided with the
4
This also includes the party holding the presidency of Cyprus, which is the only EU country with a full presidential system. Since the phrase ‘party of the head of government’ is rather unwieldy, the term ‘prime minister’s
party’ is used throughout this thesis instead and should be understood to include the party of the President of
Cyprus.
5
Note that in some countries, the official cabinet does not include all government ministers. The term is not
used here to refer to such an inner cabinet but rather to all government ministers, irrespective of seniority. This
usage applies throughout this thesis.
46
CHAPTER 2. MEASURING THE ECONOMIC VOTE
European Parliament elections, so there was the potential for a change of government to have
occurred during the survey fieldwork, which would have been problematic. In practice this
was not an issue, as both Luxembourg elections led to only minor changes to the cabinet,
which in any case did not take place until after the fieldwork period (Dumont and Poirier
2005; Dumont, Kies and Poirier 2010), and the Belgian election did not result in any change
to the party composition of the cabinet (Rihoux et al. 2015). The full list of parties analysed
in their thesis along with their incumbency status in each year can be found in Appendix A.
The most important sources of data are those discussed already, the EES surveys and the
ParlGov database. These are used throughout the thesis. There are also some supplementary
data sources that have been used to supply additional variables that are only required by
certain chapters. Chapter 4 extends the basic economic voting model to test the clarity of
responsibility theory developed by Powell and Whitten (1993). This requires country-level
variables measuring various aspects of the political system. Some of these, such as whether
the country has a presidential or a parliamentary system, were generated from the literature
or common knowledge. Other variables, such as the composition of the parliament, were
complex enough to require a systematic approach to measurement and these were mainly
taken from the European Journal of Political Research Political Data Yearbook (PDY). Chapter 5
extends the basic model to examine how party support is influenced by the party’s spatial
position. The spatial position data was collected from the Chapel Hill Expert Survey (CHES).
Some of the remaining chapters also use extra variables from the EES surveys in addition to
what is described in this chapter. In all of these cases, the measurements are discussed in the
relevant chapters before they are used.
There are two further complexities that had to be considered when coding political parties.
The first of these is the phenomenon of regional and regionalist parties. Regional parties are
those that are only active in parts of a country, such as the Christian Social Union in Bavaria (CSU), which does not operate outside the German federal state of Bavaria. The CSU
is a special case, since it works very closely with the Christian Democratic Union of Germany (CDU), which operates everywhere else in Germany. In practice, the CDU and CSU
tend to be seen as a unit at the national level—the CDU/CSU faction—and this is how they
are treated in the EES surveys as well. A more difficult example of regional parties are the
parties in Belgium, which are typically organised along linguistic lines, with French-speaking
parties and Flemish-speaking parties, rather than at the national level. These are not normally
part of a de facto greater unit, like the CDU/CSU faction. Regionalist parties are those that
2.1. DATA SOURCES AND TIMING
47
seek independence or at least greater autonomy for their region, such as the Scottish National
Party in Scotland and Plaid Cymru in Wales. There are further examples in Flanders and many
regions of Spain, namely the Basque Country, Navarre, Galicia and Catalonia. The problem
posed by these parties is that the analysis takes place at the level of the nation-state. In the case
of Belgium, this is sometimes dealt with by treating Flanders and Wallonia as separate states
but this is not the approach used here, since they do share a national government, despite
their different party systems. Moreover, this approach does not scale to the other countries
mentioned. Instead, a record has been made of the regions where each party is active. For
most parties, these comprise the entire country. The EES surveys also include a variable noting where each respondent was interviewed. When the stacked dataset was produced, any
rows matching voters to parties not active in their region were discarded. Thus the analysis is
affected by all British voters’ opinions of the Labour Party but only Welsh voters’ opinions of
Plaid Cymru, for example.
The second complexity encountered when coding political parties was that the party systems of the various countries in the EU feature varying degrees of fluidity. Northern European
countries, such as Germany, Ireland, Sweden and Denmark, tend to have stable party systems
whereas the party systems of Mediterranean countries, such as France, Spain and Italy, are
characterised by frequent mergers and splits, which still occur today despite some increased
stability in recent decades (Krouwel 2012, 49–78). This means that, in many cases, it is far
from straightforward to identify political parties in 2004 with the same parties in 2009 and
2014. The Irish party named Fianna Fáil in 2004 is obviously the same party as the Fianna
Fáil of 2014 but can the now defunct Democratic Movement of France be identified with the
Union for French Democracy, which succeeded it in 2007? The Italian party Forza Italia eventually merged with several other parties in 2009 to form The People of Freedom, which later
dissolved in 2013, reviving Forza Italia. Should these all be treated as the same party? This
question is even more difficult to answer in the case of a party split, in which case a party may
have multiple successors. To make matters worse, political parties frequently form short-lived
electoral alliances for the European Parliament elections and often the EES surveys ask voters
about these alliances, rather than the constituent parties. In order to avoid making arbitrary
decisions, each party is treated as a discrete unit in each year. This means, for example, that
responses to questions about the Fianna Fáil of 2004 are not pooled with those about the
Fianna Fáil of 2014. This only applies to the pooling of responses. Measurements of a party’s
time in office are unaffected by this decision.
48
CHAPTER 2. MEASURING THE ECONOMIC VOTE
Table 2.2: Key variables used to measure the economic vote
Variable
Scale
Level
Source
Party support
Year 2009
Year 2014
Left–right distance
Party ID
Cabinet party
Prime minister’s party
Prospective assessment
Female
Age
High education
Low education
Urban area
Rural area
Unemployed
Not in workforce
0 (low) to 10 (high)
1 = yes, 0 = no
1 = yes, 0 = no
0 (close) to 10 (far)
1 = yes, 0 = no
1 = yes, 0 = no
1 = yes, 0 = no
−2 (worse) to +2 (better)
1 = yes, 0 = no
18 to 101 years
1 = yes, 0 = no
1 = yes, 0 = no
1 = yes, 0 = no
1 = yes, 0 = no
1 = yes, 0 = no
1 = yes, 0 = no
measurement
measurement
measurement
measurement
measurement
party
party
individual
individual
individual
individual
individual
individual
individual
individual
individual
EES
EES
EES
EES
EES
ParlGov
ParlGov
EES
EES
EES
EES
EES
EES
EES
EES
EES
These are the variables used in every chapter. Most chapters include further variables, which
are introduced as used. The dependent variable is party support, unless otherwise specified.
The measurement level is shown for each variable. This indicates whether the variable is
specific to the party or the individual. Variables that depend on both are said to be at the
measurement level. Although none are shown here, some chapters also include variables
measured at the country level. The source is either the European Election Studies or the
ParlGov database.
2.2
Measurement and variables
There are a number of key variables that are fundamental to this thesis and appear in every
chapter. This section introduces the main dependent variable, party support, and several independent variables, including party identification, ideological distance, incumbency, economic
assessment and some control variables. For each variable there is a discussion of why it is important and how it is measured. These variables are listed in Table 2.2. There is an inherent
structure to the data used, which should not be ignored. Each EES survey wave consists of
a number of national surveys. Random sampling is used for each national survey but some
countries are more heavily sampled relative to their population sizes than others. Individual
responses are thus nested within countries. Furthermore, individuals are asked about each
of the important parties in their country, so these responses are nested within both individuals and parties because they relate a particular individual to a particular party. This will be
referred to as the measurement level. Thus measurements are nested within individuals and
parties, each of which is nested within with countries. The problem of structured data is ad-
2.2. MEASUREMENT AND VARIABLES
49
dressed later in this chapter. For now, it will simply be noted at which level a particular variable
is measured.
The dependent variable used for the economic voting models is party support. Party support relates a particular individual to a particular party and is thus measured at the measurement level. This variable is intended to measure the voter’s current preference level for the
party. It is derived from EES questions asking voters the likelihood that they would vote for
that party at a future national election.6 The precise wording of the question in 2009 is:
We have a number of parties in [country] each of which would like to get your
vote. How probable is it that you will ever vote for the following parties? Please
specify your views on a scale where 0 means ‘not at all probable’ and 10 means
‘very probable’. If you think of [party]: what mark out of ten best describes how
probable it is that you will ever vote for [party]?
The wording is almost identical in the other years, except for that fact that the 2004 wave
used a scale starting at one instead of zero. These were transformed to a zero to ten scale
for comparability with the 2009 and 2014 data. In order to ensure that this did not bias
the results, several potential transformations were considered. These included variations on
scaling the data and recoding particular points. The transformation that was ultimately used
was to recode one on the ten-point scale as zero and to leave the other levels unchanged. This
approach was chosen because it produced a distribution very similar to the distribution of the
2009 variable. It seems that most scores correspond directly on the two scales except of course
that parties that are strongly disliked were given the score zero where that was available or
one where it was not. Using the alternative transformations yields similar results in any case.
The decision to use party support rather than vote choice as the dependent variable was
explained in the previous chapter. It is based on the theory that vote choice is a two-stage
process, the first stage determining party support levels based on economic and other considerations and the second stage determining vote choice based on those party support levels
(van der Brug, van der Eijk and Franklin 2007, 31–53). In this model, it is assumed that the
second stage is deterministic, with voters always selecting the party that they have the highest
level of support for. In order to test whether this assumption is reasonable, the pattern of
support levels was examined among those voters who answered the question asking which
party they would vote for at a national election held the following day. Among these voters, a
6
Q12 in 2004, Q39 in 2009 and QPP8 in 2014.
50
CHAPTER 2. MEASURING THE ECONOMIC VOTE
Figure 2.2: Distribution of raw and centred party support
raw
centred
normalised count
1.00
0.75
0.50
0.25
0.00
0
2
4
6
8
10
-8 -6 -4 -2
0
2
4
6
8 10
party support
The left panel is a histogram of raw party support and the right panel is a histogram of party
support centred around the individual-level mean, from the three surveys combined. The bin
size is 0.5 in both instances. Source: EES
consistently high proportion nominated the party they had the highest reported support for.7
The proportion of voters matching this criterion was 92.7 percent [92.3%, 93.2%]8 in 2004,
93.7 percent [93.3%, 94.1%] in 2009 and 94.4 percent [94.0%, 94.8%] in 2014. Although a
small number of respondents have not responded as expected, these results confirm that this
vote choice model is a reasonable approximation of actual behaviour.
The party support variable has been centred around each individual’s mean reported party
support. The distributions of the raw and centred variables are shown in Figure 2.2. As the
figure shows, the distribution of the raw variable is both multimodal and highly skewed. A
disproportionate number of reported party support levels were zero, corresponding to 40.9
percent of the total measurements. These are the instances in which a voter reported that
they would never vote for a particular party. There are secondary modes at five and ten.
These properties are problematic for regression analysis, which assumes that residuals are
normally distributed (Gelman and Hill 2007, 46).9 In fact, the raw level of support reported
by a particular individual depends on that individual’s interpretation of the eleven-point scale.
7
In many cases, respondents assigned the same support level to multiple parties and in some of these instances
multiple parties shared the distinction of being a particular respondent’s most preferred party. As long as the
response to the party choice question was one of those parties, the statistics reported here treat these cases as
matches.
8
Square brackets indicate 95% confidence intervals.
9
Just because the dependent variable is not normally distributed does not necessarily mean that the residuals
will not be. For this to be the case, however, the independent variables would need to be distributed in a very
particular way, so as to compensate for the non-normality of the dependent variable, which they are not.
2.2. MEASUREMENT AND VARIABLES
51
By centring the scores around the individual-level mean, the differences between the scores
are preserved and it is these differences that are of substantive interest, not the absolute levels.
This is because the theory predicts a change in vote choice whenever the voter’s support for a
new party exceeds their support for their previous choice, not when it exceeds some absolute
bar. As the figure shows, the distribution of the centred variable is somewhat positively skewed
and it appears to be bimodal, although the mode at zero is an artefact resulting from the fact
that some individuals give all the parties an equal score. Nonetheless, the distribution of the
centred variable is far more appropriate for regression analysis than that of the raw variable.
The timing of the survey is indicated by two dummy variables. The base case is the 2004
survey wave and there is a dummy variable indicating the 2009 wave and one indicating the
2014 wave. As discussed earlier, the 2009 survey wave took place when the initial crisis was
at its peak, so this dummy variable indicates the mid-crisis time point. Similarly, the 2014
wave took place after the end of the recession, so its dummy variable indicates the post-crisis
time point. The reference case is naturally the pre-crisis time point.
As well as predictors related to economic voting specifically, the economic voting model
developed in the next chapter also includes other variables known to be predictive of vote
choice. The first of these, left–right distance, is derived from the spatial theory of voting
(Downs 1957; Hotelling 1929; Davis, Hinich and Ordeshook 1970). According to this theory,
individual voters prefer the parties that are closest to themselves in some political space, which
is most frequently conceived of as a single dimension stretching from left to right. The left–
right distance variable measures this closeness between a voter and a party. This is known
to be a strong influence on vote choice (van der Eijk, Schmitt and Binder 2005; Kroh 2009).
Two survey questions were used to produce this measure. The first measures the individual’s
spatial position.10 The precise wording of this question in 2009 was:
In political matters people talk of ‘the left’ and ‘the right’. What is your position?
Please indicate your views using any number on a scale from 0 to 10, where 0
means ‘left’ and 10 means ‘right’. Which number best describes your position?
The second question measures the individual’s opinion of the spatial position of each party.11
This question immediately follows the self-location question and is worded: ‘And about where
would you place the following parties on this scale?’ The wording of these questions was very
similar in the other years, except for the fact that the 2004 survey used a 1–10 scale instead
10
11
Q14 in 2004, Q46 in 2009 and QPP13 in 2014.
Q14_i in 2004, Q47 in 2009 and QPP14 in 2014.
52
CHAPTER 2. MEASURING THE ECONOMIC VOTE
Figure 2.3: Distribution of left–right distance
count
60000
40000
20000
0
0
1
2
3
4
5
6
7
8
9
10
left–right distance
This is a histogram of the left–right distance between each individual and party, from the
three surveys combined. A distance of zero indicates that the individual believes the party
to be perfectly aligned with them ideologically. A distance of ten indicates the belief that the
individual and party occupy the opposite extremes of the spectrum. Source: EES
of a 0–10 scale, as was the case for party support. These were adjusted in the same way as
party support, described above, placing all values on the 0–10 scale.
The left–right distance between a particular voter and a particular party is the absolute
difference between that voter’s reported left–right position and his or her assessment of that
party’s left–right position. This effectively measures the distance an individual perceives between him- or herself and the relevant party. This can be expected to be a reliable measure of
spatial distance, as there is evidence that most voters are able to place themselves on a left–
right spectrum (Mair 2007) and that in most countries there is a strong correlation between
left–right self-identification and views about the desirable level of government intervention in
the economy as well as certain social issues (Dalton, Farrell and McAllister 2011, 81–108).
Additionally, the relative positions of parties according to the aggregated placement of survey
respondents tends to coincide well with other measures of party position (109–141). The
measure of left–right distance described here ranges in value from zero, in the case of perfect
alignment, to ten, which means the voter is on the far left and the party the far right or vice
versa. The distribution of this variable is shown in Figure 2.3. The mean value is 3.13 and
the standard deviation is 2.66. Frequency falls off quickly as the distance increases past five,
2.2. MEASUREMENT AND VARIABLES
53
which is to be expected because most voters place themselves near the centre and extreme
distances are only possible for voters placing themselves near the endpoints.
The other key predictor derived from non-economic voting theories of voter behaviour is
party identification. The theory of party identification originated with Campbell et al. (1960),
who argued that a key determinant of vote choice was an individual’s sense of identification
with a particular party, this identification being largely inherited from parents or social milieu.
Green, Palmquist and Schickler (2002, 24–51) have investigated some of the characteristics of
party identification. Identifying with a party is not the same as supporting it or voting for it,
but rather a deeper and longstanding attachment to a particular party. People may vote against
their identification on occasion but it changes only rarely. Not everyone identifies with a party
but those who do are more engaged with politics. The original analysis of party identification
in the United States has been replicated in recent years (Lewis-Beck et al. 2008). The theory of
party identification has its origin in studies of the United States and its application to European
politics has not been uncontroversial but there is evidence that party identification models do
have some applicability to European countries (Berglund et al. 2005).
Party identification is, like party support and left–right distance, measured at the measurement level, that is it relates a particular individual to a particular party. It is a dummy variable,
taking the value one if the individual identifies with the party and zero otherwise. No individual identifies with more than one party12 but some individuals identify with no party. The
question used to measure this variable13 was:
Do you consider yourself to be close to any particular [political]14 party? If so,
which party do you feel close to?
Overall, 55.4 percent of respondents reported identifying with a particular party. As a result,
the party identification variable takes the value one for 7.7 percent of the party–individual
pairings.
Further independent variables are needed to measure the economic vote. In the previous
chapter, a theoretical model of economic voting was developed. According to that model, party
support is influenced by both the party’s incumbency status and the individual’s prospective
economic assessment. Incumbency status is measured at the party level of course and consists
of two dummy variables, one indicating that the party held the office of prime minister (or
12
There is some evidence that people can identify with multiple parties (Schmitt 2009) but the EES surveys did
not ask about secondary identifications.
13
Q29a in 2004, Q87 in 2009 and QPP21 in 2004
14
In the 2014 survey only.
54
CHAPTER 2. MEASURING THE ECONOMIC VOTE
Figure 2.4: Distribution of prospective and retrospective assessments
2004
2009
2014
9000
prospective
6000
count
3000
0
9000
retrospective
6000
3000
0
-2
-1
0
1
2
-2
-1
0
1
2
-2
-1
0
1
2
economic assessment
This shows the distribution of prospective and retrospective economic assessments in each
year. Both range from −2 (much worse) to +2 (much better). Source: EES
president in the case of Cyprus) and the other indicating that the party held at least one cabinet
post at the relevant time. These are not mutually exclusive categories and in particular the
prime minister’s party is always a cabinet party, since the prime ministership is itself a cabinet
post. The method used to determine a party’s incumbency status has already been explained
earlier in this chapter. Of the 504 parties in the pooled dataset, 69 (14%) were prime ministers’
parties, a further 96 (19%) were other cabinet parties and 339 (67%) were opposition parties
at the time the surveys were conducted.
Prospective economic assessment is measured at the individual level using the following
survey question:15
And over the next 12 months, how do you think the general economic situation
in this country will be? Will it get a lot better, a little better, stay the same, a little
worse or get a lot worse.
15
Q21 in 2004, Q49 in 2009 and QPP16 in 2014.
2.2. MEASUREMENT AND VARIABLES
55
Figure 2.5: Relationship between prospective and retrospective assessments
2004
2009
2014
prospective
1.0
0.5
0.0
-0.5
-1.0
-2
-1
0
1
2 -2
-1
0
1
2 -2
-1
0
1
2
retrospective
Each panel shows the mean prospective economic assessment for a particular retrospective
assessment in the given year. These are shown with their 99% confidence intervals, although
owing to the high sample size many of these are not visible. Source: EES
The responses to these questions have been recoded so that they range from −2 to +2, with
negative numbers indicating a pessimistic assessment and positive numbers an optimistic one.
Zero means the economy is expected to stay the same. The surveys also include an equivalent
retrospective question asking the respondents to assess whether the economy has improved
or worsened over the past twelve months. It was explained in the previous chapter that this
thesis uses a prospective voting model for both theoretical and data-driven reasons. The latter can be seen from Figure 2.4, which shows the distribution of both the prospective and
retrospective variables. In both 2004 and 2014, the distributions of both variables resemble
the binomial distribution, but for the fact that extremely positive assessments are remarkably
rare. In 2009, however, this is not the case. Voters’ retrospective assessments are very skewed
in that year, with 79.2 percent of respondents reporting a negative assessment, 13.7 percent
a neutral assessment and only 7.1 percent a positive assessment. While such a negative retrospective assessment of the economy was certainly justified at that time, this sort of consensus
among respondents is at odds with the variance desired in a key independent variable. The
prospective assessment in 2009 is also a little more skewed than in other years but the skewness is much more tolerable, with 39.9 percent reporting a negative assessment compared to
26.4 percent a neutral and 33.7 percent a positive assessment.
It is also interesting to look at the relationship between prospective and retrospective economic assessments in each year. These are shown in Figure 2.5. It can be seen that the relation-
56
CHAPTER 2. MEASURING THE ECONOMIC VOTE
ship between these variables is approximately linear in 2004 and 2014 but once again 2009
is different. In that year, not only does the relationship appear to be somewhat less linear but
retrospective assessment is also a weaker predictor of prospective assessment. Another way
of seeing this is to look at the correlations between the two variables in each year. This is 0.56
in 2004, 0.28 in 2009 and 0.63 in 2014. In other words, there is a strong correlation between
the two before and after the crisis but only a weak correlation during the crisis. These observations about the pattern of responses to the economic perceptions questions, combined with
the theoretical reasons outlined in the previous chapter, have led to the decision to base the
economic voting analysis in the following chapters on prospective, rather than retrospective,
economic assessment.
In addition to these key independent variables, demographic variables are used as controls. These are gender, age, education, urban density and workforce participation. Gender
is a dummy variable taking the value one for women and zero for men. Women make up
54.4 percent of the sample. Age is measured in years but this is sometimes expressed as decades in the models used in the coming chapters so that all of the estimated coefficients are
on similar scales, since the effect size of age in years is typically very small. Unfortunately,
the other demographic questions were often measured on very different scales in the different
survey years and sometimes even countries within a survey year. This makes it difficult to
find granular correspondences between the different years, so responses have been grouped
into coarse categories instead, aiming to keep the size of the categories roughly equal. Education has been divided into low, medium and high groups. Low education respondents are
those who did not complete high school. Medium education respondents are those who have
completed high school but do not have a university degree. Current students are included in
this category. High education respondents are those who have a degree or higher. Medium
education is the most common category and also the reference case, covering 39.9 percent of
respondents, followed by high education, covering 36.9 percent, then low education, covering
23.3 percent.
Urban density indicates the density of the area where the respondent lives. This is also divided into three groups. These groups are necessarily rather subjective, owing to the phrasing
of the questions. The reference case is a small or medium-sized town. The other groups are
rural, which indicates a village or rural area, and city, which also includes large towns. Town
was the most frequent response (35.1%) followed by city (34.0%) and finally rural (30.9%).
The final control variable is workforce participation. Once again this is divided into three
2.3. METHOD OF ANALYSIS
57
groups. The reference case is those who are employed, with the other groups being the unemployed and those who are not in the workforce for any other reason. The latter group is
mostly retired people but also includes stay-at-home parents and disabled people, among others. Unsurprisingly, a majority of respondents (50.2%) indicated that they were employed,
with only a small number (8.0%) claiming to be unemployed. The remaining 41.8 percent are
not in the workforce. All of these variables are coded as dummy variables indicating each of
the non-reference cases.
2.3
Method of analysis
This thesis uses a stacked dataset design, which was strongly influenced by van der Brug, van
der Eijk and Franklin (2007, 40-46), although the methods used are different. The motivation
for this design is that the key variables of interest exist at the measurement level, which relates
particular individuals to particular parties. For instance, the dependent variable, party support
is such a variable. The degree to which individual i supports party j cannot be said to belong
completely to either the individual level or the party level—it belongs to both. Since this
party × individual, level, which is referred to in this thesis as the measurement level, is where
the most important measurements occur, it is appropriate for the unit of observation to be a
party–individual pair. This objective was achieved by transforming the EES survey dataset,
which was measured at the individual level. Parties are represented by repeated questions—
that is, each party-related question is asked once for each party. The dataset was transformed
such that each individual appears in the dataset once for each party they were asked about,
with only the questions about the relevant party included in each new row. Figure 2.6 illustrates this process.
The key analytical method used in this thesis is multilevel modelling. Multi-level modelling is an extension of linear regression and generalised linear modelling that explicitly models
variation between groups. This is achieved by giving the model coefficients a probability model
of their own (Gelman and Hill 2007, 1). The decision to use multilevel modelling was motivated by the clustered nature of the data. One of the consequences of stacking the dataset
as described above is that individual observations have been repeated in the stacked dataset
many times. If linear regression were used this would artificially deflate the estimated standard errors but multilevel modelling can account for this sort of structure. In any event, the
data used in this thesis is inherently structured. For one thing, there are twenty-five countries
in the analysis and to ignore this would be to assume implicitly that there is minimal variance
58
CHAPTER 2. MEASURING THE ECONOMIC VOTE
Figure 2.6: Data stacking process
country
resp_id
age
support_p1
support_p2
support_p3
UK
UK
UK
1
2
3
88
21
40
0
7
3
8
3
8
5
8
0
⇓
country
party_id
resp_id
age
support
UK
UK
UK
UK
UK
UK
UK
UK
UK
UK-Lab
UK-Lab
UK-Lab
UK-Con
UK-Con
UK-Con
UK-LD
UK-LD
UK-LD
1
2
3
1
2
3
1
2
3
88
21
40
88
21
40
88
21
40
0
7
3
8
3
8
5
8
0
In the original dataset, each observation corresponds to an individual and variables relating
to particular parties are repeated for each party. In the stacked dataset, each observation
corresponds to a party–individual pair, eliminating the need to repeat variables.
between these countries, which is a strong assumption to make. Furthermore, many of the
variables that are of interest, including party support, the dependent variable, involve opinion
about specific parties. Since there may be variation between a country’s parties as well as
variation between countries, there are clusters within clusters.
While ignoring this clustering would lead to deflated standard errors, it must be acknowledged that there are other ways of addressing this particular problem. It has been shown
that robust standard errors and aggregation can produce the same results as multilevel models under the appropriate conditions (Arceneaux and Nickerson 2009). Nonetheless, each of
these other methods is limiting in some specific way. Aggregation cannot be used to estimate
individual-level effects and robust standard errors are not recommended for clusters of fewer
than about twenty observations (188). The number of parties in each country in this study
varies from five to fifteen. Furthermore, multilevel modelling is sufficiently flexible that is
possible to include predictors at multiple levels within the same model. This is particularly
important for this study, since economic voting theory relates variables that necessarily relate
to multiple levels. Incumbency is only meaningful at the party level. Economic performance is
measured at the national level. And it is individuals who make vote choices. A key advantage
of multilevel modelling is that these variables and their interactions can be included in a single
2.3. METHOD OF ANALYSIS
59
model (Gelman 2007a, 7-8). Of course, it would be possible to aggregate all of these together
and estimate everything at the national level. It would also be possible to model separate
equations for every political party of interest. Both approaches are problematic however. The
former approach, complete-pooling analysis, ignores all variation between groups and the latter, no-pooling analysis, is statistically inefficient. A key strength of multilevel modelling is
that it offers a compromise between these extremes (Gelman and Hill 2007, 256). Multilevel
models produce both fixed effect and random effect estimates. The fixed effects are those of
primary substantive interest given the research questions behind this thesis. The decision to
use multilevel modelling is motivated by the need to account for the structure of the data,
rather than interest in the random effects specifically. As a result, the discussion of random
effects is limited to discussions of variance and sometimes covariance. Individual countries
are not normally discussed, as the purpose of this thesis is to gain an understanding of large
cross-national trends.
The decision to use multilevel linear regression models distinguishes this study from previous survey-based research into economic voting behaviour. As discussed in the previous
section, the stacked dataset design of this thesis was influenced by van der Brug, van der
Eijk and Franklin (2007), but they chose not to use multilevel modelling in their study. They
reason that the chief advantage of multilevel models is that they avoid the biased standard
errors that would result from naive regression modelling and they observe that the this problem can be avoided equally well by using robust standard errors, which they do (47). They
also argue against multilevel modelling on the grounds that it is not well equipped to handle
the cross-classified data structure that results from the stacked dataset design (48). While
there is merit to these arguments, there is still much to be said for multilevel modelling in
this context. Although it is true that the use of robust standard errors can avoid the problem
of deflated standard error estimates, there are other advantages to using multilevel modelling, particularly in that they permit greater flexibility in the models. As for the problem of
cross-classification, it must be acknowledged that this does add complexity to the models but
modern computing power combined with Bayesian estimation techniques makes it possible to
estimate these complex models. Duch and Stevenson (2008), on the other hand, do use some
multilevel modelling. They adopt two different methods for their analysis, a one-stage and a
two-stage strategy. Their one-stage strategy uses multilevel models to estimate all of the parameters together, whereas their two-stage strategy involves estimating the level of economic
voting in national surveys and then using those estimates in cross-national models (94-100).
60
CHAPTER 2. MEASURING THE ECONOMIC VOTE
One of the key differences between their methods and the methods used in this thesis is that
they use multinomial logistic regression to predict vote choice, whereas this thesis uses linear
regression to predict party support, for the reasons given in the previous chapter.
Most of the independent variables in the models presented in the following chapters have
been centred around the grand mean. By contrast to the centring of the dependent variable,
the reasons for which have been given earlier, this centring of the independent variables has
been done for technical reasons. Specifically, centring in this way markedly increases the convergence speed. It is also helpful for variables which are involved in quadratic and interaction
terms. One consequence of this centring is that some caution is required when interpreting
intercepts and interactions. Because of this and also owing to the complexity of some of the
models, model coefficients are typically not interpreted directly in this thesis. Instead, postestimation simulation is used to derive quantities of more direct interest. The method used is
that described in Gelman and Hill (2007, 140–143). Because the predictions resulting from
this method take into account the uncertainty of many predictors, the error bands around them
can be deceptively wide. In particular, it is very often the case that two predictive intervals
overlap even though there is a significant difference between the actual predictions. In order
to minimise confusion, predictive intervals are not normally shown in any plots but instead
the question of significant difference is tested directly and discussed in the text. Standard
errors are of course quoted in the text for most predictions as well. The full model coefficient
tables can be found in Appendix B. There are multiple methods for computing p-values for
multilevel models. The p-values shown in the coefficient tables are based on Satterthwaite
estimates of the degrees of freedom, although these values play no part in the analysis in this
thesis. The Pseudo R2 reported for these models is based on Xu (2003).
One challenge that arises with any use of survey data is that of missing data. This has
been dealt with using listwise deletion. Owing to the stacked dataset design, a respondent
does not have to be excluded using this method simply because he or she has not answered
a question about a particular party. Only the row corresponding to that party–individual pair
is removed, while rows are included for each party that the individual did answer questions
about. This means that the only individuals that had to be excluded completely were those who
refused to answer any party related question—and those voters leave very little basis to impute
missing values—and those who did not answer questions at the individual level. As people
typically did not refuse to answer the demographic questions posed, the only problematic
question was prospective economic assessment. The proportion of respondents who declined
2.3. METHOD OF ANALYSIS
61
to answer this question was 13.3 percent in 2004, 4.1 percent in 2009 and 5.4 percent in 2014.
Other techniques for managing missing values were also considered. Multiple imputation in
particular has much to be said for it (Rubin 1978; King et al. 2001, 50) but that approach did
not prove viable here because multiple imputation techniques and software have not yet been
developed for all of the multilevel models that are used in this thesis. Ultimately, the structure
of the data was considered a more important issue than missing data and that is what led to
the decision to use listwise deletion and multilevel modelling.
The survey response rate has also been given some consideration. Response rates in the
EES surveys typically ranged from 60–80 percent for the face-to-face mode but were lower for
the telephone mode, sometimes below 20 percent. One approach to the issue of potential bias
resulting from low response rates is post-stratification survey weighting. Survey weights have
been included in the EES survey data to correct for non-response bias. These weights were
computed using a raking procedure on the variables of age, sex, region, education and, in
some countries, whether or not the household has a fixed phone line. In principle, weighting
in this way allows for the more accurate estimation of population parameters from the survey
data by reducing the effect of non-response bias. These weights are not however used in
the multilevel models in this thesis. Instead, non-response bias is minimised by including all
relevant demographic variables in the models as controls. Since the weights are conditioned
on variables that are already being controlled for in the models, they add no extra information.
There has long been a controversy in the literature about the relative merits of weighted and
unweighted least squares estimators for linear regression (DuMouchel and Duncan 1983, 535)
and the relative merits of model-based and sampling-based approaches to the problem of unit
non-response continue to be debated today (Gelman 2007a, 2007b; Bell and Cohen 2007).
The decision to use a model-based approach is motivated by the fact that the parameters of
most interest are regression coefficients rather than simple means or proportions. There is
also the problem that weighted estimators simply have not been developed for certain crossclassified multilevel models, so the sampling-based approach would severely limit the types of
analysis that could be undertaken using these methods.
The statistical analysis described in this thesis has been performed using the statistical programming language R (R Core Team 2016). In addition to the core language and its libraries,
the analysis was supported by the lme4 (Bates et al. 2015), lmerTest (Kuznetsova, Brockhoff
and Christensen 2016) and ordinal (Christensen 2015) packages. Most of the plots in this
thesis were produced using the ggplot2 package (Wickham 2009). Finally, the arm (Gelman
62
CHAPTER 2. MEASURING THE ECONOMIC VOTE
and Su 2015), dplyr (Wickham and Francois 2015) and RSQLite (Wickham, James and Falcon
2014) packages were all used heavily for utility purposes.
2.4
Conclusion
This chapter has introduced the dataset and the methods that will be used throughout the rest
of the thesis, as well as discussing how the key variables have been measured. This study will
undertake a multilevel analysis of survey responses collected in twenty-five European Union
member states. The primary data source is the pooled responses from the 2004, 2009 and
2014 waves of the European Election Studies surveys. These waves correspond to time points
before, during and after the Great Recession. This survey data is supplemented with contextual
data from other sources. The key dependent variable is party support, the degree to which
a voter states that he or she is likely to vote for a particular party in the future. Important
independent variables are the survey year, spatial distance between party and voter, party
identification, incumbency and prospective economic assessment.
The next chapter will introduce the basic models used with various extensions throughout
this thesis. Owing to the complexity of the complete model, several simpler models are presented first, examining the first hypothesis from various angles. These simpler models are easier
to interpret but only the complete model takes advantage of all of the available data, which
provides a clearer overall picture. The intention behind this approach is to use the simpler
models to illustrate various aspects of economic voting behaviour and then use the complete
model to obtain definitive estimates. Later chapters of the thesis extend or alter these models
in various ways, so as to test the remaining hypotheses.
Chapter 3
Voting in a time of crisis: how the Great Recession
affected the economic vote
After the global financial crisis of 2007–08, most developed countries slipped into recession, in
an event that has become known as the Great Recession. This was the worst event of its kind
since the Great Depression of the 1930s and a number of governments suffered catastrophic
electoral defeats in the following years. The example of Ireland was introduced earlier, where
the formerly dominant party Fianna Fáil was reduced to approximately half of its previous
vote in 2012 (Marsh and Mikhaylov 2012, 478). Such results accord with the established
theory of economic voting, which predicts that poor economic conditions will lead to voters
turning against their governments at the ballot box. On the other hand, this theory was almost
entirely developed using evidence relating to less turbulent economic conditions, what might
be described as the ordinary boom and bust cycle of the economy. Whether or not voters
respond to a severe transnational crisis in the same way as a typical recession is not yet clear.
This chapter explores this question by comparing voters’ party suport levels in 2004, well
before the crisis, to those in 2009, at the height of the first wave of the Great Recession, and
in 2014, after the initial shock had subsided.
In the immediate aftermath of the Great Recession, some political commentators argued
that the situation would benefit the Left, since they are traditionally critics of the economic
system which produced the crisis (Bartels 2012, 44) but these expectations have not been
borne out. There is little evidence of an ideological shift in OECD countries as a result of the
crisis (Bartels 2014). Commenting on the United States, Bartels (2013, 70), observes that:
The truth of the matter is that ideological mandates are exceedingly rare in American politics, even in times of economic crisis. Indeed, what may be most striking
about the politics of the Great Recession is how ordinary they look. In bad times,
as in good times, ordinary citizens have a stubborn tendency to judge politicians
63
64
CHAPTER 3. VOTING IN A TIME OF CRISIS
and policies not on the basis of ideology or economic doctrine, but of perceived
success or failure.
Economic voting theory offers a more plausible account of electoral behaviour during the crisis.
In an analysis of aggregate data from twenty-eight OECD countries, Bartels (2014, 188–194)
shows that governments generally received an increased vote when GDP growth was positive
and a decreased vote when negative. Kriesi (2014, 305–315) similarly finds a relationship in
European countries between incumbent vote share and the economic indicators, particularly
inflation in Central and Eastern Europe and unemployment in Western Europe. Kenworthy
and Owens (2011, 212–216) examined US voter attitudes in survey data since the 1970s and
found that voters do tend to lose confidence in whichever party is governing at the time but
they also found that this effect was actually quite weak during the Great Recession. Others
have also found the electoral response to the crisis to be weaker than might be expected (for
example, Kriesi 2012).1
These results suggest that the electoral response to the Great Recession was an economic
voting one, rather than one motivated by a deep ideological shift among voters, but they still
leave questions unanswered. The key question concerning this chapter is: was the economic
vote stronger during the Great Recession than at other times? Given that the recession was
far deeper, this seems likely, but some of the studies just mentioned have found clues that the
opposite might actually be the case. On the other hand, such findings might also be artefacts
of the particular methods used by those studies, since there has not yet been enough research
done to know how different approaches affect the results found.
This chapter has two key purposes. First, it describes how a multilevel model was constructed to measure the economic vote using the particular theoretical framework of this thesis, and
second, it uses this model to compare the economic vote before, during and after the recession by using multinational survey data from the 2004, 2009 and 2014 waves of the European
Election Studies in order to shed light on the relative strength of the economic vote during
the Great Recession. The chapter begins by reviewing some relevant theory and introducing
the hypotheses that will be tested. After a brief discussion of measurement, a series of models is constructed, starting with a simple spatial voting model and proceeding to models that
measure the economic vote using first prime ministers’ parties alone and eventually all parties.
Finally, the findings will be summarised and the implications discussed.
1
See Chapter 1 for a more thorough review of the literature on economic voting during the Great Recession.
3.1. PARTY SUPPORT THEORY OF ECONOMIC VOTING
3.1
65
Party support theory of economic voting
The theoretical framework of economic voting underlying this thesis was described in depth
in Chapter 1. Following van der Brug, van der Eijk and Franklin (2007, ch. 2), the vote choice
decision is conceptualised as a process in which voters have a certain level of support for each
of the parties in their country and this support level is informed by such considerations as
their ideological congruence with each party or how competent they believe that party to be
in government. Individuals are assumed to vote for the party for whom their level of support
is greatest, irrespective of the absolute level of that support. According to this model, voters
form an assessment about the condition of the economy and this assessment affects their beliefs
about the relative competence of the current government and opposition, which in turn affects
their party support levels and potentially their vote choice. This process can thus be seen as
having three stages: economic assessment, party support adjustment and finally vote choice.
The focus of this thesis is on the second stage—how does a particular sociotropic economic
assessment affect an individual’s party support levels?
Although economic voting is conceptually straightforward, there is no consensus as to how
it should be measured. In order to make the claim that one election had more economic voting
than another election, the concept of economic voting has to be precisely defined and operationalised. Different scholars have used different approaches towards this end. For example,
earlier studies used aggregate data, such as parties’ national vote share as well as national production or unemployment rates to make an argument about economic voting. For example,
Kramer (1971) examined incumbent vote share in US House of Representatives elections.
These were the only sorts of data available during the last comparable crisis, the Great Depression of the 1930s. These methods are limited, however, as it is not generally possible to
make inferences about individual behaviour based on aggregate data.
More recent studies tend to use individual-level data and most of these define economic
voting implicitly, using regression analyses with interacting predictors to determine which contextual variables influence economic voting. It is however possible to define an explicit measure of the economic vote so that a specific figure can be estimated for the level of economic
voting at a particular election. Duch and Stevenson (2008, 44–46) did just this, constructing their measure of the economic vote from survey questions asking for a respondent’s vote
choice and retrospective economic assessment. An important criterion for their vote choice
question was that respondents had to be explicitly offered the option of declaring no intention
to vote, in which case they were excluded from the analysis. The criteria used for selecting
66
CHAPTER 3. VOTING IN A TIME OF CRISIS
an economic assessment question was that it had to be retrospective, ask about the national
economy and ask how the economy had changed rather than its absolute condition. They
explain that the choice of retrospective rather than prospective analysis was largely dictated
to them by practical concerns, particularly in that the majority of surveys available to them
only included the retrospective question.
In order to produce a measure of economic voting from these questions, Duch and Stevenson (2008) estimate a multinomial logistic regression model to predict the likelihood of a
change in vote choice as a result of a change in retrospective assessment, with relevant controls included. For each respondent they construct a vector of the change in vote choice probabilities corresponding to an arbitrary change in assessment. They define their measure of
the ‘general economic vote’ as the average size of this vector across the entire survey (49–52).
This is described as the ‘general’ economic vote because this approach requires no measure of
incumbency—any economically motivated change in vote choice is included in this measure.
Because this is more general than most current definitions of economic voting, which see economic voting as an activity discriminating between government and opposition parties, they
define several refinements of their measure conditioned on various measures of incumbency
(55–59).
Others are sceptical of the widespread tendency to measure economic voting in terms of
vote choice, preferring to measure party support levels instead (van der Brug, van der Eijk and
Franklin 2007, 33–36). Party support questions ask voters to assess their level of support for
each of the relevant parties in their countries. They prefer a two-stage model of vote choice, in
which voters decide who to vote for by selecting the party that they have the greatest level of
support for at that moment, it being the party support levels rather than the vote choice directly
that are influenced by the usual variables, such as economic conditions. They argue that the
widespread use of vote choice as a dependent variable has contributed to the instability of
economic voting results (15–16). Their argument is that when party support levels are quite
close a small change in predictors can cause a change in vote choice but when those support
levels are far apart, it requires a large predictor change to see a change in vote choice. Hence,
according to them, it makes more sense to use party support as a dependent variable and
this should lead to more consistent results. Although their study used individual-level survey
data, they chose to measure economic conditions using national indicators of unemployment,
inflation and economic growth, arguing that these indicators best reflect the information that
voters use to form their own judgements (69–71).
3.2. HYPOTHESES
67
This study is influenced by both Duch and Stevenson (2008) and van der Brug, van der
Eijk and Franklin (2007), and combines features of both approaches. The latter’s argument
in favour of measuring party support levels rather than party choice is persuasive, particularly
in light of the fact that the majority of the countries examined in this thesis are multiparty
systems and therefore any analysis reducing the party system to a government–opposition dichotomy necessarily treats disparate significant parties as identical. Furthermore, using party
support instead of vote choice offers the possibility of measuring not just which party a voter
most prefers but also by how much. This difference in support between a voter’s first and
second choice is important because if that difference is large then even a strong economic
vote might not be sufficient to cause a change in vote choice but it will cause a change in party
support. This choice of dependent variable is combined with Duch and Stevenson’s method of
measuring the degree of economic voting at a particular election by simulating the effect of
an arbitrary adjustment in economic assessment. This study also uses an individual measure
of economic assessment, but a prospective measure is used, rather than their retrospective
measure. There are theoretical reasons behind this choice, which are discussed extensively in
Chapter 1, as well as data-driven reasons discussed in the next section.
3.2
Hypotheses
This chapter tests three specific economic voting hypotheses. The first is:
Hypothesis 3.1 Individuals’ party support levels were influenced by their prospective sociotropic
economic assessments in each survey year.
This hypothesis tests whether the specific framework of economic voting developed in this
thesis so far accurately describes the intended voting behaviour of the respondents in these
surveys. As this framework is built on very well-established principles of economic voting theory, a failure to confirm this hypothesis ought to be seen as a sign of flawed operationalisation,
rather than a deficit in the theory. This hypothesis makes no reference to incumbency as it is
concerned with the most general form of economic voting, namely any change in party support
attributable to economic voting, irrespective of which parties benefit and which suffer.
The second hypothesis relates to the more specific and most usual conception of economic
voting, in which voters are supposed to treat government and opposition parties differently:
68
CHAPTER 3. VOTING IN A TIME OF CRISIS
Hypothesis 3.2 There was an observable tendency for citizens to vote economically in each year.
In particular, voters holding an optimistic economic assessment are more inclined to support government parties and less inclined to support opposition parties than those holding a pessimistic
assessment.
This builds directly on the first hypothesis. If it can be shown that the general form of economic
voting does occur then the next step is to show that voters discriminate between these different
groups of parties.
One of the central claims of this thesis is that voter behaviour during times of severe global
recession cannot necessarily be predicted from theories developed to describe the typical boom
and bust cycle of a national economy. It has long been suspected that economic issues play
a larger role in voting behaviour as they become more severe (for example, Bloom and Price
1975, 1240). Although some recent studies found the crisis response to be weaker than expected (Kenworthy and Owens 2011; Kriesi 2012), these were studies of government vote
share, so they may be concealing movements that would be observable with a party support
measure. This chapter’s third and final hypothesis is thus:
Hypothesis 3.3 The economic voting effects were heightened during the Great Recession. Specifically, there was a greater observable tendency to vote economically in 2009 than in either 2004
or 2014.
As the 2009 survey took place at the peak of the first wave of the crisis, unlike the 2004 survey,
which was conducted during a time of normal economic activity, any form of crisis-specific
behaviour should be observable in a comparison between the two surveys. As the economy
was naturally a highly salient issue during the crisis, it is predicted that the inclination towards
economic voting became stronger at that time. Similarly, once the initial shock of the recession
had passed, it would be expected that normal patterns of behaviour would reassert themselves,
so it is predicted that the inclination to vote economically had receded to their pre-crisis levels
by 2014.
3.3
Measuring the economic vote
There are strong theoretical arguments for both retrospective and prospective models of voting, and it has even been argued that the two are in many respects equivalent, as the former
informs the latter (Downs 1957). Often the choice of which measure to use is driven by the
3.3. MEASURING THE ECONOMIC VOTE
69
Figure 3.1: Support for incumbent prime minister’s party by economic assessment
2004
2009
2014
7
6
prospective
5
mean support
4
3
2
7
6
retrospective
5
4
3
2
-2
-1
0
1
2 -2
-1
0
1
2 -2
-1
0
1
2
economic assessment
Mean support for the incumbent prime minister’s party according to the respondent’s prospective and retrospective economic assessment in each survey year, shown with 95% confidence
intervals and accounting for party-level variance. Both forms of economic assessment are
measured on a five-point scale ranging from −2, indicating a very negative assessment, to +2,
indicating a very positive assessment, with 0 indicating a neutral assessment. Source: EES
available data, and this is an important consideration here too. Although each survey includes
both retrospective and prospective economic assessment questions, descriptive analysis shows
that these questions are not equally useful, particularly in 2009 during the crisis. In both 2004
and 2014, responses to the prospective and retrospective questions are similarly distributed,
appearing approximately binomial besides a marked tendency for respondents to avoid the
most positive category. In 2009, the prospective assessment variable is similarly distributed
but the distribution of the retrospective assessment variable is heavily skewed towards the
negative responses, with fewer than eight percent of respondents giving a positive response.
This is not surprising, since the condition of the economy at that time had in fact worsened
badly, but it does mean that the retrospective evaluation in 2009 is not suitable for use in this
analysis, as there is too little variance.
70
CHAPTER 3. VOTING IN A TIME OF CRISIS
Prospective assessment can be predicted from retrospective assessment by a simple linear
model with moderate success in both 2004 (R2 = 0.30) and 2014 (R2 = 0.39), which confirms
the Downsian view that the two measures can be seen as related. On the other hand, this
relationship breaks down in 2009, with the same model no longer able to predict prospective assessment with any accuracy (R2 = 0.08). It is interesting that, despite the consensus
among respondents about the worsening of the economy over the past year, there was still
a diversity in opinion regarding its future course, with a similar distribution to that of the
other years. Furthermore, since the dependent variable is prospective in orientation too, it is
more consistent to choose a prospective orientation for the economic assessment measure as
well. Finally, the relationship between support for the prime minister’s party in each country
and both economic assessment measures was examined. As Figure 3.1 shows, the relationship
between these measures is approximately linear, except in the case of retrospective assessment
in 2009. For these reasons, only the prospective measures are used in this thesis.
A measure of party support has been constructed from a sequence of questions included
in each of the EES surveys asking voters the likelihood that they would ever vote for each of
a number of parties in their countries, on a scale from zero to ten, where zero is described as
‘not at all probable’ and ten as ‘very probably’. Although the question does ask respondents if
they would ‘ever’ vote for a particular party, this interpretation treats the responses as current
support for that party instead, and there is some evidence that the respondents interpret the
question that way as well. For example, even people who stated that they identify with a
particular party do not overwhelmingly respond with ten for that party. Furthermore, the fact
that there is a relationship, as will be shown, between this measure and questions about recent
economic conditions does suggest that the current feeling interpretation is more appropriate
than a strict reading of the question as posed. As each individual responds to several party
support questions, corresponding to the key parties in their country, there is one measurement
for each party–individual pair, and these measurements have been centred around the group
mean for each individual.2
3.4
A spatial model of party support
Naturally the economy is not the only consideration that influences a person’s party support
levels. As outlined in Chapter 1, there is an enormous vote choice literature describing the various influences that are known to affect the vote choice decision. In order to obtain unbiased
2
More details on the measurement of this and other variables can be found in Chapter 2.
3.4. A SPATIAL MODEL OF PARTY SUPPORT
71
Figure 3.2: Relationship between party support and left–distance
mean support
5
4
3
2
1
0
2
4
6
8
10
left–right distance
Mean support for parties according to the left–right distance between the party and the respondent, shown with 95% confidence intervals and accounting for variance at the individual,
party and country levels. Source: EES
estimates of the influence of economic considerations on party support, other key influences
need to be controlled for. There are two key predictors that will be taken into account, spatial
proximity and party identification.
In order to measure spatial proximity, survey questions asking respondents to place both
themselves and each major party in their country on the left–right spectrum are used. It is
expected that those voters who consider themselves close to a party on the left–right spectrum
will generally have a greater level of support for that party than those who consider themselves further away. There is however no reason to expect that relationship to be linear, as
the difference between zero and one point of distance, representing a change from a complete alignment to a close alignment, is conceivably of greater significance to a voter than the
difference between nine and ten points of distance, both of which represent a fundamental
mismatch. Plotting the mean party support for each level of left–right distance confirms that
this relationship is indeed negative and non-linear, as Figure 3.2 shows.
As well as spatial proximity, party identification is known to be an important predictor of
vote choice. Accordingly, it is expected to be a strong predictor of party support as well and
cursory analysis of the data confirms that these two variables are closely correlated. Plotting
72
CHAPTER 3. VOTING IN A TIME OF CRISIS
mean party support against left–right distance for party identifiers and non-identifiers separately shows that the effect of left–right distance is different for the two groups. In order to
model these characteristics accurately, the model needs to include both a linear and quadratic
term for the left–right distance variable as well as an interaction between left–right distance
and party identification. This leads to the following regression equation for a single party:
y = β0 + β1 ∆ + β2 ∆2 + β3 I + β4 ∆I + ε,
where y represents an individual’s support for that party, ∆ represents the mean-centred left–
right distance between that individual and that party and I is a dummy variable indicating
that the individual identifies with that party.3
The dependent variable, party support, has been centred around the mean for each individual. Most individuals surveyed recorded a level of support for each of several parties, so
the mean response for a particular individual was subtracted from that individual’s response
for each party. This was done because it is the difference between the levels of support for
each party that is of most interest in this models and the absolute levels may be misleading
if different respondents adopted different conceptual reference points when responding to
these questions. Another advantage of centring the dependent variable in this way is that
the centred variable is more approximately normal than the raw variable, which is heavily
skewed because respondents are much more likely to assign low scores than high scores. The
assumption of residual normality is therefore more plausible using the centred variable. The
means are examined separately later in the chapter. The predictors have also been centred in
order to improve the efficiency of the estimation process. This should not in principle change
the particular coefficient estimates but it does mean that the intercept estimate can no longer
be directly interpreted. The left–right distance variable has been centred around the mean
for each election, that is each country and year. The party identification dummy variable was
centred around the grand mean.
In addition to these predictors, a number of further predictors were included to control
for personal attributes generally known to influence voting behaviour. These predictors were
age, sex, education, population density and labour force status. Age is measured in decades,
so that the corresponding coefficient is not too small to be conveniently interpreted. Education
level is divided into three groups, the first being those with a university degree, the second
3
An argument could be made for the inclusion of an interaction between the quadratic distance term and the
party identification dummy variable but empirical tests have shown that the models including that term are not
significantly more accurate, so it has been omitted for the sake of parsimony.
3.5. THE PRIME MINISTER’S PARTY AND THE ECONOMIC VOTE
73
being those who have not completed high school and the reference group being those who
have completed high school but who do not possess a university degree. Population density
is also divided into three groups, those residing in cities, those residing in towns and those
in rural areas, the reference group being towns. Lastly, labour force status is divided into
working, unemployed and other groups. The other group consists of people who are not in
the workforce for any reason, such as retirement. The reference group is workers.4 All of
these control variables were centred around the grand mean. An interaction has also been
included between party identification and age, since it has been known for some time that
party identification tends to be stronger among older people (Campbell et al. 1960, 161–164;
Converse 1976; Berglund et al. 2005).
As was mentioned earlier, the dependent variable, party support, has been centred around
each individual’s mean response, so as to control for differing centres of support. Modelling
the uncentred variable with these control variables has shown that there are relationships
between some of the controls and an individual’s mean party support. For example, women
tend to report higher levels of party support than men do, irrespective of the party, although
the difference in levels of support between the parties does not appear to be related to these
variables. A naive analysis of an arbitrary party would thus be likely to conclude that women
are more likely to support that party, even when this is not the case. Centring party support in
this way avoids this problem, while preserving the differences in support between the parties.
3.5
The prime minister’s party and the economic vote
There are several ways this spatial model can be extended in order to test the hypotheses
posited at the beginning of this chapter. One of the most straightforward ways to do this is to
analyse support for only the prime minister’s party in each country. As this is the party that
could be expected to be most strongly affected by any economic voting effect, if the hypotheses
are true then this ought to be reflected in such a model. This does not mean that the other
parties in each country are ignored. Since the party support variable is centred around the
individual’s mean response, it is not measuring absolute support for the prime minister’s party
but rather the relative support for that party. Therefore, even situations where the absolute
level of support for the prime minister’s party remains steady while that for opposition parties
increases would be observed by such a model. Another advantage of this approach is that the
multilevel structure of the data can be simplified considerably. The data can be described by
4
Further details on how these variables were measured can be found in Chapter 2.
74
CHAPTER 3. VOTING IN A TIME OF CRISIS
Table 3.1: Prime ministers’ parties model
Fixed effect
Intercept
Year 2009
Year 2014
Left–right distance
Left–right distance2
Party ID
Prospective assessment
Female
Age (decades)
High education
Low education
Urban area
Rural area
Unemployed
Not in workforce
Distance × party ID
Age × party ID
Prosp. assess. × year 2009
Prosp. assess. × year 2014
Coeff.
0.067
0.490
0.159
−0.428
0.023
4.706
0.323
0.054
−0.007
0.084
0.028
−0.018
0.065
−0.083
0.052
0.280
0.179
−0.051
−0.038
SE
(0.095)
(0.183)
(0.130)
(0.019)
(0.001)
(0.129)
(0.053)
(0.021)
(0.008)
(0.025)
(0.031)
(0.026)
(0.027)
(0.043)
(0.025)
(0.029)
(0.015)
(0.048)
(0.065)
p
0.492
0.014
0.235
< 0.001
< 0.001
< 0.001
< 0.001
0.011
0.353
0.001
0.357
0.495
0.016
0.055
0.038
< 0.001
< 0.001
0.310
0.565
Fixed effect coefficient estimates from Model 3A. The dependent variable is support for the
incumbent prime minister’s party in the respondent’s country. Several country-level random
effects terms are also included in the model and the corresponding variance estimates can
be found in Appendix B. Sample size is 51962 individuals within 25 countries. Pseudo R2 is
0.541. Source: EES
a two-level model, where individuals are grouped within countries. No party level is required
as each country only has one prime minister’s party.
Several things have to be done in order to extend the spatial party support model to measure economic voting intention for prime ministers’ parties in each of the studied countries.
The intention of Model 3A is to measure the effect of economic assessment on party support,
so a prospective economic assessment predictor was added to the model, centred around the
grand mean. The survey year is indicated by the addition of dummy variables for 2009 and
2014, using 2004 as the reference case. Interaction terms between the time and economic
assessment variables were also added, so that it can be ascertained whether the degree of economic voting differed among the three years. As this is a multilevel model, a random intercept
for the country was added, along with several random slopes. The economic assessment and
time variables, as well as their interactions, were given random slopes so that it is not assumed that the degree of economic voting intention was equal in every country. Similarly, the
party identification and left–right distance variables and their interaction were given random
3.5. THE PRIME MINISTER’S PARTY AND THE ECONOMIC VOTE
75
Figure 3.3: Predicted support for prime minister’s party by left–right distance
predicted support
8
6
group
party identifiers
others
4
2
0
2
4
6
8
10
left–right distance
Predicted support for the incumbent prime minister’s party in 2004 according to the left–right
distance between the respondent and party as well as whether the respondent identifies with
that party or not. Predictions are derived from Model 3A and are shown with 95% predictive
intervals. These predictions assume a neutral prospective economic assessment. Source: EES
slopes because single-party analysis showed that the shape of these relationships differed often considerably from party to party. Random slopes were considered for each of the control
variables as well but the estimated variances of these slopes were very small and the resulting
models did not fit the data significantly better, so these were not included in the final model.
The key results from this model are shown in Table 3.1. As most of the variables have been
centred, it is not trivial to interpret the intercept or the interaction coefficients. For this reason,
post-estimation simulation has been used to derive estimates of the quantities of interest.5
As expected, spatial proximity is an important predictor of party support. Figure 3.3 shows
the relationship between left–right distance and predicted party support for both the party
identifier and non-party identifier groups in 2004. In both groups, it is assumed that the
individual has a neutral prospective assessment of the economy.6 From this plot it can be seen
that party support falls as left–right distance increases but that the size of this drop diminishes
5
The post-estimation simulation approach to interpreting complex models is used throughout this thesis. Since
the regression coefficients are usually not interpreted directly, the coefficient tables for most of the models discussed
are not included in the main text but can be found in Appendix B. See Chapter 2 for a more detailed discussion of
this decision and the reasons behind it.
6
Unless otherwise specified, these predictions are for a 40-year old male who has completed high school but
not university, who lives in a town and is employed.
76
CHAPTER 3. VOTING IN A TIME OF CRISIS
with increasing distance. In other words, the difference between zero and one point of distance
is much more important than the difference between nine and ten points of distance. It can
also be seen that party identifiers have a much stronger level of support than non-identifiers at
any degree of proximity. In fact, the importance of distance is weaker for identifiers than nonidentifiers. There is one surprising feature of this plot, which is that after six points of distance,
it appears that further distance is associated with increased party support. This is most likely
an anomaly resulting from the fact that few respondents identify with a party on the other side
of the political spectrum from themselves. In fact, the error around the predictions is large
enough that a levelling off is perfectly plausible.
The inclusion of prospective economic assessment in the model makes it possible to estimate the economic vote.7 Because interactions were included between this variable and
the time dummy variables, the model gives a measure of the economic vote in each year. In
particular, the economic vote for a particular party will be defined as the difference in party
support between the optimists, those who believed the economy will be much better in twelve
months time, and the pessimists, those who believed the economy will be much worse in
twelve months time. In other words, this quantity is the total change in an individual’s support for a particular party that could be accounted for in the model by a change in economic
assessment. For example, the party support predicted by the prime minister’s party model for
an individual in 2004 who does not identify with that party but who occupies the same position on the left–right spectrum is 5.21 (SE = 0.19, p < 0.001) if that individual is an optimist
but only 3.92 (SE = 0.12, p < 0.001) if that individual is a pessimist. The difference, and
hence the estimated economic vote in 2004, is 1.29 points (SE = 0.21, p < 0.001).
By contrast, the estimated economic vote for 2009 is only 1.09 points (SE = 0.13, p <
0.001). There was thus an apparent decline in the economic vote between the two years
of 0.20 points (SE = 0.19, p = 0.30) but this was not significant. Similarly, the estimated
economic vote for 2014 is 1.14 points (SE = 0.17, p < 0.001), which is not significantly
different from either 2009 (∆ = 0.06, SE = 0.21, p = 0.79) or 2014 (∆ = 0.14, SE = 0.26,
p = 0.58). In other words, this model suggests that the strength of the economic vote was
approximately constant over the entire period, at least when considering prime ministers’
parties only. Figure 3.4 shows the relationship between prospective economic assessment and
7
The term ‘economic vote’ is used in this thesis to describe any change in voters’ support for the different
parties in their countries that can be attributed to their assessment of the economy. This might not necessarily be
manifested as an actual vote choice for several reasons, which are discussed in Chapter 1. The empirical focus of
this thesis is on the stage of the voting process in which party support levels are formed, which precedes the vote
choice stage.
3.5. THE PRIME MINISTER’S PARTY AND THE ECONOMIC VOTE
77
Figure 3.4: Predicted support for prime minister’s party by economic assessment
6.0
predicted support
5.5
year
5.0
2004
2009
2014
4.5
4.0
-2
-1
0
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prospective economic assessment
Predicted support for the prime minister’s party according to prospective economic assessment
for each survey year, based on Model 3A. Economic assessment ranges from −2 (very pessimistic) to +2 (very optimistic). These predictions are for an individual who does not identify
with the party and who occupies the same left–right position as the party. Source: EES
predicted support for the prime minister’s party in each year. The slope of each line indicates
the strength of the economic vote in the corresponding year, so the fact that the three lines are
approximately parallel reflects its stability over time. It is interesting to note that the lines do
not overlap, despite being approximately parallel. This suggests the level of support for the
prime minister’s party was greater in some years than in others, irrespective of the individual’s
economic assessment. In particular, it appears that support was greatest in 2009. For example,
a typical voter holding a neutral economic assessment was 0.48 points (SE = 0.18, p < 0.01)
more supportive of the prime minister’s party in 2009 and 0.32 points (SE = 0.18, p = 0.06)
more supportive in 2014 than in 2004. Thus, although the economic vote for prime ministers’
parties was stable over time, those parties did receive a small boost in support at the height
of the recession, irrespective of economic assessment.
In general, the control variables have little if any effect. Women are slightly more likely to
support the prime minister’s party than men, although the size of this effect is tiny, accounting
for an increase in party support of only 0.05 points (SE = 0.02, p = 0.01). For comparison, an
increase from zero to one point of distance accounts for a decrease of 0.57 points (SE = 0.02,
78
CHAPTER 3. VOTING IN A TIME OF CRISIS
p < 0.001). Education also plays a modest role, with support 0.08 points (SE = 0.02, p <
0.001) greater among university educated voters than voters who have not attended university.
The effect of employment status is likewise small. The employed and unemployed groups are
not significantly different from each other (∆ = 0.08, SE = 0.04, p = 0.06) but the not in
the workforce group is slightly more likely to support the prime minister’s party. Once again
this effect is tiny, accounting for a mere 0.05 point (SE = 0.02, p = 0.04) increase in party
support over the employed group. Age has an even smaller effect, with each extra decade of
age associated with a 0.02 point (SE = 0.01, p < 0.01) decrease in support.8 There is also an
interaction between the effects of age and party identification, and this is more substantial. At
the age of twenty, party identification is associated with a 3.30 point (SE = 0.13, p < 0.001)
increase in party support and this is increased by a further 0.18 points (SE = 0.02, p < 0.001)
for each additional decade of age.
In summary, Model 3A offers evidence that support for the prime minister’s party is linked
to the individual’s prospective assessment of the economy, with optimistic voters being more
supportive than pessimistic voters. These findings support the first two hypotheses. The third
hypothesis predicted that the strength of the economic vote would be greater in 2009 but
this was not supported by the model, which found the economic vote to be stable over time.
It was however found that support for prime ministers’ parties was slightly higher in 2009
than at other times and this was true irrespective of economic assessment. This model has
the advantage of being relatively straightforward to interpret but it is also limited by the fact
that it only analyses support for a single party in each country. This is problematic because
a change in support for the prime minister’s party is of limited import if support for all of
the other parties moved in the same way.9 The next section discusses how this model can
be extended to take into consideration support for multiple parties in this country and thus
address this limitation.
3.6
A multiparty model of the economic vote
The economic voting model developed thus far can be generalised to analyse not only prime
ministers’ parties but also the other parties in each country. In principle, this requires a far
8
This may appear to contradict Table 3.1, which shows no significant age effect. This apparent contradiction
arises because the model includes an interaction between party identification and age. Since the party identification
variable has been centred, it is not actually zero for non-party identifiers. This illustrates why post-estimation
simulation has been used to interpret model estimates rather than discussing regression coefficients directly.
9
As the dependent variable is centred around the individual’s mean party support level, parallel movement
has already been eliminated, so this particular example would not be a problem in practice. Unfortunately, there
are similar situations that are not neutralised by centring the dependent variable, so these possibilities do require
serious consideration.
3.6. A MULTIPARTY MODEL OF THE ECONOMIC VOTE
79
Figure 3.5: Data classification structures
Country
Country
Party
Individual
Party
Measurement
Measurement
(a) Classification structure of the stacked dataset.
Measurements are cross-classified into parties
and individuals, which are also independently
nested within countries.
(b) Simpler, hierarchical classification structure
in which measurements are nested within parties
and parties are nested within countries.
more complex model in order to be analysed accurately. This is because the same individuals
responded to questions about each party within a country, so measurements such as the left–
right distance between a party and an individual are cross-classified within both parties and
individuals. Furthermore, both of those groups are nested within countries. Because of this
data structure, which is illustrated in Figure 3.5a, it is important to be aware of the possibility
that observations within these groups are more alike than observations from different groups.
The degree of this within-group similarity can be measured by the intraclass correlation. The
intraclass correlation of the uncentred party support variable is 0.08 by individual, 0.13 by
party and 0.01 by country. This implies that measurements within a particular individual or
a particular party are more similar than between those groups but once these effects have
been accounted for there is very little further similarity between measurements within a given
country. Since the party support variable has actually been centred around the individual-level
mean, some of the within-group similarity can be expected to have been eliminated. In fact,
the intraclass correlation of the centred party support variable is 0.14 by party and zero by
both individual and country.
This suggests that random intercepts are only required for the party level, despite the
complex structure of the data. Even though the intraclass correlations of the other groups are
low, it is still possible that random slopes might be required for these groups, so a subset of the
data was used to explore various options but in every case the variance of the random slope
was either too small for the model to be estimated successfully or a chi-square difference test
showed the resulting model not to be a significant improvement over the equivalent model
without the random slope. In other words, the full complexity of the cross-classified model is
not required and the data can be modelled with the hierarchical structure shown in Figure 3.5b
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CHAPTER 3. VOTING IN A TIME OF CRISIS
instead. This means that the generalised model need only differ from the prime minister’s party
model in two respects. First, it can include a number of parties in each country, rather than
just the one. And second, whereas the random intercept and slopes are grouped by country
in the simpler model, they are grouped by party in the generalised model. Of course, this
difference is purely a question of interpretation, as with only one party per country in the
prime minister’s party model, the party and country groupings are identical. Model 3B was
formed in this way and differs from the prime minister’s party model in one further respect,
which is the inclusion of party-level random slopes for each of the control variables.10
The results from this model are similar in most respects to those from the prime minister’s
party model but there are some important differences. Since not just incumbent parties but
also opposition parties are included in this model, it would be expected that the fixed effect
for party support would no longer be significantly different from zero. That is, whereas a
unit increase in optimism about the economy is associated with a small increase in support
for the prime minister’s party, it is not expected to be associated with an increase in support for any arbitrary party. Using the same measure of economic voting intention as before,
namely the change in predicted party support associated with the maximum possible increase
in prospective economic assessment, the economic voting intention for an arbitrary party was
−0.13 points (SE = 0.09, p = 0.14) in 2004, −0.07 points (SE = 0.08, p = 0.34) in 2009 and
−0.13 points (SE = 0.08, p = 0.13). None of these is statistically significant, nor is any of the
differences between them. This confirms these expectations.
Although the economic voting intention for an arbitrary party is not significant, this is not
necessarily the case for a specific known party. It has already been shown that for prime ministers’ parties this effect is positive. Furthermore, the theory predicts a negative economic voting
intention for opposition parties. Because the model includes random slopes for the economic
assessment and time variables as well as the interaction between them, it is possible to extract
separate economic voting intention estimates for each party. Figure 3.6 shows the estimated
economic voting intention for each party grouped by year and incumbency status. This shows
that in each year, government parties were usually subject to a positive economic voting intention and opposition parties were mostly subject to a negative economic voting effect, although
there are some exceptions, particularly among opposition parties. Note also that the points in
10
These random slopes make sense from a theoretical perspective because different parties can be expected to
appeal to different groups in society, so the control effects ought to take different values for different parties. This
argument applies equally well to 3A but in practice the variances were low enough to cause convergence problems
with the smaller sample size, so they were omitted from that model.
3.6. A MULTIPARTY MODEL OF THE ECONOMIC VOTE
81
Figure 3.6: Economic vote by year and incumbency status
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Economic vote for each party according to the survey year and whether the party was the
prime minister’s party, a junior coalition partner or an opposition party. Economic vote is the
predicted increase in support for a party associated with the maximum possible increase in
prospective economic assessment, based on Model 3B. Each point represents a single party.
Source: EES & ParlGov
each group are less spread out in 2009 than they are in the other years. The standard deviation of the party economic votes in 2009 was 0.77 points, compared to 1.20 points in 2004,
and 0.91 points in 2014. This suggests that the overall amount of economic voting decreased
during the recession and recovered partially in the following period.
The above analysis suggests that an optimistic economic assessment is typically associated
with an increased support for government parties and a decreased support for opposition
parties but it does not test this hypothesis. In order to do so, the model was extended so as
to include an incumbency term and the interactions between incumbency and the terms used
to measure the level of economic voting intention. The resulting Model 3C allows for the
possibility that economic voting intention differs for government and opposition parties. This
was done by including a dummy variable to distinguish between government parties, those
represented in cabinet, and other, opposition, parties. The model also includes a random
slope at the country level for the interaction between incumbency and prospective economic
assessment, which allows for the possibility that the amount of economic voting was different
in different countries. A random intercept for the country level was also added. Random slopes
82
CHAPTER 3. VOTING IN A TIME OF CRISIS
Figure 3.7: Predicted support by year and incumbency status
2004
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Predicted support for a party according to the respondent’s prospective economic assessment,
the party’s incumbency status and the survey year, based on Model 3C. Economic assessment
ranges from −2 (very pessimistic) to +2 (very optimistic). These predictions are for an individual who does not identify with the party and who occupies the same left–right position as
the party. Source: EES & ParlGov
were considered for related terms, such as the three-way interactions between incumbency,
economic assessment and time, but these had a variance very close to zero and were therefore
omitted.
This extended model was used to estimate the net economic vote, that is, the total predicted change in support for governing parties relative to opposition parties that can be attributed to economic assessment. For example, in 2004, the economic vote for government
parties was 1.02 points (SE = 0.15, p < 0.001) and the economic vote for opposition parties
was −0.70 points (SE = 0.10, p < 0.001). This means that the net economic vote for 2004 was
1.72 points (SE = 0.20, p < 0.001). As expected, the economic vote is significant and positive
for government parties in each year and significant and negative for opposition parties in each
year. The net economic vote in 2009 was only 1.18 points (SE = 0.19, p < 0.001), which is
0.55 points (SE = 0.20, p < 0.01) less than in 2004. By 2014, the net economic vote was
1.22 points (SE = 0.19, p < 0.001), which is not significantly greater (∆ = 0.05, SE = 0.19,
p = 0.81) than in 2009. In other words, the net economic vote became weaker during the
recession and did not recover in the following years.
3.6. A MULTIPARTY MODEL OF THE ECONOMIC VOTE
83
The economic vote in the three different years can be seen graphically in Figure 3.7. The
lines show the relationship between economic assessment and party support in each year. The
solid lines show government support while the dashed lines show opposition support. The fact
that an optimistic assessment is good for government parties and bad for opposition parties
is reflected by the positive slope for each of the government lines and the negative slope for
the opposition lines. The steeper slopes in 2004 show the stronger economic vote in that year.
This figure also shows that support for government parties was greater in 2009 and 2014
than in 2004, irrespective of the voter’s economic assessment. As a result of this increase in
government support, the intersection point of the lines appears to have changed over time.
This would imply that a more strongly pessimistic assessment was required to cause a net
shift in support towards the government in those later years. In fact, it is only in 2004 that the
intersection occurs at a point corresponding to an economic assessment significantly different
from completely neutral, specifically −0.79 [−1.50, −0.22].11 The corresponding assessments
were −0.18 [−0.62, 0.25], in 2004 and −0.39 [−1.01, 0.16], in 2014. On the other hand,
none of the differences between these assessments was significantly different,12 so it cannot
be concluded that this apparent difference did not arise by chance.
So far this discussion has only contrasted government and opposition parties. It has been
suggested that the prime minister’s party ought to be more strongly affected by economic voting than junior coalition partners (van der Brug, van der Eijk and Franklin 2007, 56–58). The
argument is that these parties are often able to criticise the government during election campaigns and make the case to voters that they ought to have more seats and so be in a position
to demand greater influence within a government. The prime minister’s party naturally lacks
this freedom to distance itself so easily from the government’s performance. In order to test
this hypothesis, Model 3D was formed by adding further terms to distinguish prime ministers’
parties from other cabinet parties. Based on this model, it appears that prime ministers’ parties
typically are subject to a somewhat stronger economic vote than other coalition parties. This
stronger economic vote amounts to an additional 0.44 points (SE = 0.23, p = 0.06) in 2004,
0.61 points (SE = 0.21, p < 0.01) in 2009 and 0.70 points (SE = 0.22, p < 0.01) in 2014.
These effect is not significantly different from year to year and the estimated variance of the
corresponding random slope is very close to zero. This suggests that the extra economic vote
that prime ministers’ parties receive is stable across both time and country. The results from
11
Square brackets indicate 95% confidence intervals.
The difference between 2004 and 2009 was −0.61 [−1.42, 0.09], the difference between 2009 and 2014 was
0.40 [−0.41, 1.27] and the difference between 2004 and 2014 was −0.22 [−0.96, 0.50].
12
84
CHAPTER 3. VOTING IN A TIME OF CRISIS
this model are otherwise similar to the results from the previous model, which only distinguishes government and opposition parties.
The multiparty approach to modelling the economic vote has confirmed that a positive
economic vote is not only associated with increased support for governing parties but also
decreased support for opposition parties. These results support for the first two hyptotheses.
It was also found that prime ministers’ parties in particular are more strongly exposed to economic voting effects than other opposition parties. The net strength of the economic vote
became weaker during the recession, rather than stronger as the third hypothesis predicted.
This result also appears to contradict the earlier finding that the economic vote that prime
ministers’ parties were exposed to remained stable over time. This is actually not a contradiction as the net economic vote takes into account the impact of economic assessment on
both government and opposition parties. In fact, the results from Model 3D confirm both
that the economic vote for prime ministers’ parties remained stable over time and that the net
economic vote fell in 2009. This is interesting because it implies that the weakening in the
economic vote occurred not because voters were more forgiving of their governments than
usual but rather because they were less inclined to support opposition parties in their stead.
3.7
Influence of the economy on mean party support
Since the dependent variable has been centred around its individual-level means, the analysis
presented so far only takes into consideration relative changes in party support, not a shift in
mean party support. That is, any increase in support for one party at the expense of another
is observed but an increase in support for all parties would not be evident. Although it is
this relative change that is of primary interest, it is worth exploring the possibility of absolute
change in party support in order to gain a more complete understanding of voter behaviour.
This can be done by examining the means of the various party support scores given by each
respondent to the parties in his or her country. These are the same values that the scores
were centred around to form the centred party support used as the dependent variable in the
earlier models. Model 3E was constructed to predict mean party support from an individual’s
prospective economic assessment, their demographics and the year, as well as the interaction
between their economic assessment and the year. This corresponds to the basic economic
voting model introduced earlier except that none of the party-specific variables are included
as they are not meaningful here. As with earlier models, random slopes have been included
for the time and economic assessment variables and their interactions.
3.7. INFLUENCE OF THE ECONOMY ON MEAN PARTY SUPPORT
85
The results from the mean party support model are consistent with the findings made so
far. The predicted mean party support for an individual with a neutral economic assessment
was 3.62 points (SE = 0.11, p < 0.001) in 2004, 3.27 points (SE = 0.10, p < 0.001) in
2009 and 3.19 points (SE = 0.10, p < 0.001) in 2014. This means that voters were less
supportive (∆ = 0.38, SE = 0.15, p = 0.02) of all parties in 2009 than in 2004. There was no
significant difference (∆ = 0.08, SE = 0.11, p = 0.48) between 2009 and 2014. The predicted
increase in mean party support associated with a maximal increase in economic assessment
was 0.52 points (SE = 0.10, p < 0.001) in 2004. This means that optimistic voters were more
likely to to report higher support for all parties than pessimistic voters. By 2009, this had fallen
by 0.21 points (SE = 0.09, p = 0.02) to 0.31 points (SE = 0.09, p < 0.001) and by 2014 it
had risen by 0.49 points (SE = 0.13, p < 0.001) to 0.80 points (SE = 0.10, p < 0.001). This
is also significantly greater (SE = 0.14, p = 0.04) than the 2004 level. Taken together, this
means that voters became less supportive overall of their political parties during the recession
and their overall support was less strongly influenced by their economic assessment. This is
consistent with the earlier findings that economic voting effects generally were depressed in
2009.
Some of the control variables were also shown to have a significant effect on mean party
support. Each extra decade of age was associated with a 0.09 point (SE = 0.004, p < 0.001)
decrease in mean party support. Women had a slightly higher mean party support level than
men (0.10 points, SE = 0.01, p < 0.001). Those with a university education had a mean party
support level 0.03 points (SE = 0.02, p = 0.02) greater than those who had only finished
high school. The differences between the other education groups were not significant. There
were also no significant differences between people living in cities, towns or rural areas. Unemployed voters had a mean support level 0.04 points (SE = 0.03, p = 0.10) greater than
unemployed voters and 0.07 points (SE = 0.03, p < 0.01) greater than those not in the workforce. Although many of these differences are statistically significant, the effect sizes are very
small. Furthermore, this model has a pseudo R2 of only 0.12, meaning that almost all of the
variance in mean party support is unexplained by these variables.13 These results seem to
confirm the assumption of the earlier models that it is primarily relative party support and not
absolute party support that is affected by the key variables of interest.
13
It is well known that feeling thermometer scales and survey attitude measurements suffer from low reliability
(Wilcox, Sigelman and Cook 1989; Alwin and Krosnick 1991; Alwin 1997), so it should not be surprising that there
is so much residual variation. Individual variation in absolute scores need not affect the substantive results of this
study, so long as relative party support is consistent.
86
CHAPTER 3. VOTING IN A TIME OF CRISIS
3.8
Conclusion
This chapter has tested three main hypotheses. The first hypothesis was that party support
was influenced by an optimistic economic assessment and the second was that this improved
support for government parties and reduced support for opposition parties respectively. The
evidence strongly supports both of these hypotheses. In all three years economic perceptions
had a clearly observable effect on party support and this is apparent in the relatively simple
prime minister’s party model as well as the more complex multiparty models. The former
model further suggests that this operates in the expected direction, inasmuch as prime ministers’ parties benefit from a positive economic assessment. The multiparty model confirms that
this is the case and demonstrates that other cabinet parties also benefit from such an assessment and that opposition parties benefit from a negative economic assessment. These results
are not very surprising as they are in agreement with a large economic voting literature. What
makes these results significant is that they demonstrate the validity of the multilevel party
support approach to measuring economic voting. Although party support models have been
pioneered by van der Brug, van der Eijk and Franklin (2007), the use of party support within
a multilevel model is novel, so it is important to show that this method can replicate what is
already known.
The third hypothesis is that these economic voting effects were heightened during the
Great Recession. This question has generated little prior comparative research, as these events
were quite recent. The expectation was that the increased salience of the economy would
have led voters to be more inclined than usual to hold their governments to account for the
exceptionally bad economic conditions at the time. In fact, these findings show the opposite to
have been the case. The size of the net economic voting effect was shown to be considerably
smaller in 2009 than in 2004 and it had not recovered to its pre-recession levels by 2014.
What this means is that a larger change in economic assessment was required to achieve a
unit change in party support in 2009 than in 2004. However, this may not necessarily have
been to the benefit of incumbents. The change in party support required for a change in vote
choice depends on how close the prior party support levels were, so if the party support levels
were close enough then even the smaller economic voting effect could have led to a change in
government. Although surprising, others have also found the electoral response to the crisis
to be weaker than expected (Kriesi 2012; Kenworthy and Owens 2011).
How do we explain this surprising result? One possibility is that during the Great Recession, the economy may have been seen as a less useful tool to discriminate between parties.
3.8. CONCLUSION
87
As many opposition parties have previously been in government, if the crisis were seen as a
structural issue then even opposition parties may have been held partly responsible for the
situation, depressing the importance of the economic vote. If this were the case, then it would
be expected to see a move away from the centre or that extreme or anti-system parties would
benefit from negative perceptions of the economy. This possibility is explored in detail in
Chapter 5. Another potential explanation is that voter dissatisfaction was not expressed by a
change in party support but by an increased likelihood of abstaining from voting. This idea
is explored in Chapter 6. A third potential explanation is that voters did not hold their own
governments responsible for the crisis. They could hold the financial sector responsible, for example, or the European Union, which has centralised certain aspects of economic governance.
This possibility is explored in Chapter 7, which looks at how attitudes towards the European
Union have evolved over the same period studied in this chapter.
Chapter 4
Clarity of responsibility during a global recession
An enduring difficulty for the theory of economic voting has been the so-called ‘great instability’ of the economic vote, which refers to the fact that empirical studies of certain countries at
particular times have found very strong evidence of an economic voting effect while similar
studies of different countries or different times have found little if any evidence of such an
effect (Paldam 1991, 26). One proposed explanation for this instability is that some countries
and times provide more favourable contexts for economic voting behaviour than others (Powell and Whitten 1993). In particular, it is argued that citizens are most likely to engage in
economic voting when there is a clearly recognisable authority who is seen to be responsible
for the state of the economy. This ‘clarity of responsibility’ is stronger in some countries and
at some times than others, depending upon both institutional design and the degree to which
a single party controls the institutions of government (C. J. Anderson 2000; Nadeau, Niemi
and Yoshinaka 2002). For example, institutional designs in which a single-party executive
effectively dominates the government ought to have a higher economic vote than a country
in which power is shared across multiple parties and multiple branches of government. Thus
parliamentary systems ought to be expected to experience a higher level of economic voting
than presidential systems, for example, since power is less divided. The economic vote should
also be higher during periods of majority government than periods of coalition government,
for the same reason.
An alternative explanation for the instability of economic voting findings is model misspecification. Van der Brug, van der Eijk and Franklin (2007, 16) argue that this instability
results from the widespread use of vote choice as a dependent variable and contend that party
support models, such as the models developed in the previous chapter, are better suited for
measuring the economic vote.1 It is worth noting that both the clarity of responsibility and
party support approaches would explain the weak level of empirical support for economic voting in multiparty systems. In the former case, this is because the coalition governments and
1
See Chapter 1 for a detailed explanation of this argument.
89
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CHAPTER 4. CLARITY OF RESPONSIBILITY DURING A GLOBAL RECESSION
limited executive control that feature in many multiparty systems of government are among
the key characteristics of low-clarity contexts (Whitten and Palmer 1999). In the latter case,
one of the key intentions of the party support approach is to provide a better measure of the
economic vote in contexts where the vote choice cannot meaningfully be described as a binary
one (van der Brug, van der Eijk and Franklin 2007, 8–15). This overlap in intention between
the two approaches raises the question as to whether a clarity of responsibility effect can be
found when party support rather than vote choice is used to measure the economic vote. This
chapter answers this question by extending the economic voting models from the previous
chapter to take clarity of responsibility into account.
The chapter begins by discussing the theory of clarity of responsibility and some recent
relevant developments in that area. Following on from that is a discussion of how clarity
of responsibility will be measured for this analysis. Next, three specific hypotheses are introduced, which test whether clarity of responsibility effects can be found and whether they
appear to be constant over time. The following analysis is broken into two parts. The first
part tests the chapter’s hypotheses by analysing citizens’ support for whichever party happens
to control the office of prime minister at the time. The second part expands the analysis to
take into account all of the parties for which support data is available. The results from these
different analyses are then compared so as to determine whether or not the evidence overall
supports each of the hypotheses. Finally, there is a discussion of the implications of these
findings.
4.1
Clarity of responsibility: economic voting in different
contexts
The notion of ‘clarity of responsibility’ was first introduced into the literature by Powell and
Whitten (1993). Motivated by the failure of earlier cross-national studies of economic voting to
replicate the success of economic voting studies in the United States and the United Kingdom,
they argued that these discrepancies could be explained by differences in the political systems
of the different countries. In particular, they argue that economic voting depends on the ability
of voters to attribute responsibility to the incumbent government:
We suggest that the critical linkage of the voter’s assignment of responsibility to
the government is not merely an individual-level idiosyncrasy or rationalization.
Rather, it will strongly reflect the nature of policymaking in the society and the
4.1. CLARITY OF RESPONSIBILITY: ECONOMIC VOTING IN DIFFERENT CONTEXTS
91
coherence and control the government can exert over that policy. The greater the
perceived unified control of policymaking by the incumbent government, the more
likely is the citizen to assign responsibility for economic and political outcomes to
the incumbents. (398)
In other words, economic voting should be expected to occur predominantly in countries where
power is visibly concentrated and less in countries where it is diffused among different parties
or political actors.
Powell and Whitten (1993, 399–402) nominated five features of a political system that
were thought to interfere with clarity of responsibility and so reduce the level of economic voting in a country. The first of these was a ‘lack of voting cohesion among the major governing
party or parties’, that is, a marked tendency for elected representatives of major parties to vote
in accordance with their own views irrespective of their party’s official position. Although it is
unusual for political parties to tolerate this, especially in parliamentary systems, four countries
were identified as lacking voting cohesion, namely Italy, Japan, the United States and Switzerland. The second feature was a legislative committee system which allocates real power to
non-government parties, for example, one that distributes committee chairs to parties proportionally to their representation in the legislature. They found this to be the case in several
European countries at the time. The third feature they identified was the presence of an upper
house that is both powerful, such as the US and Australian Senates or the German Bundesrat
but unlike the British House of Lords, and controlled by opposition parties. The fourth and
fifth features identified as reducing clarity of responsibility were the presence of minority government and coalition government respectively. Coalition governments in particular tend to
be an enduring characteristic of most European political systems.
The presence or absence of each of these features was used to classify countries as low
or high clarity and it was then shown that the economic voting effects were stronger in the
high clarity group than the low clarity group (405–409). These results were later replicated
by Whitten and Palmer (1999) using a broader data set. Unlike the earlier study, they divided
countries into three distinct groups, described as ‘most clear’, ‘mixed clarity’ and ‘least clear’
(57). Their results showed that high clarity countries generally experienced a greater economic voting effect than mixed clarity countries, with low clarity countries experiencing very
little measurable economic voting (57–63). These findings offered further support for the idea
that voters’ tendency to hold governments to account for economic conditions is mediated by
their ability to identify an incumbent party as the clearly responsible political actor.
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CHAPTER 4. CLARITY OF RESPONSIBILITY DURING A GLOBAL RECESSION
Although groundbreaking, the Powell–Whitten index has been criticised for some details
of its operationalisation. The decision to reduce all of the variation to a simple dichotomy
between high and low clarity countries has been seen as limiting as is the fact that the index
is almost constant over time (Nadeau, Niemi and Yoshinaka 2002, 404; Royed, Leyden and
Borrelli 2000, 677). Scepticism has also been voiced about the idea that such highly technical
features of the political system as the allocation of committee chairs could influence voters’
perceptions of responsibility (Royed, Leyden and Borrelli 2000, 678). At least one aggregatelevel study has been unable to find any clarity of responsibility effect at all (Chappell and
Veiga 2000). Other explanations have also been suggested for the high variation in levels
of economic voting in different times and different countries, such as that the salience of
economic issues also varies across time and space (Singer 2011b). Nonetheless, the clarity of
responsibility model is the most widely accepted.
A number of scholars have since proposed revisions to the Powell–Whitten clarity index in
order to incorporate various factors that are conjectured to contribute to or detract from the
clarity of responsibility of a polity. Many of these proposals pertain to long-term institutional
characteristics. For example, Cutler (2004, 2008), argued that federal structures cloud perceptions of responsibility. He found that, in Canada at least, federalism poses a real obstacle to
voters’ ability to hold the appropriate government responsible for policy failures. On the other
hand, Arceneaux (2006, 748) found that voters in federal states do hold politicans responsible
in the areas where they actually exercise power but only under certain favourable conditions.
C. D. Anderson (2006) extends this idea further, finding that multilevel governance in general
tends to weaken the economic vote, since voters have to determine which level of government
to hold to account. In an extensive comparative study of democratic systems, Hellwig and
Samuels (2008) looked more broadly at the effects of different institutional designs on electoral accountability, arguing that certain designs impede voters’ capacity to hold governments
to account even when they accurately recognise which political actors are responsible. For
example, in parliamentary systems, members of parliament can replace the head of government mid-term, depriving voters of the opportunity to express their dissatisfaction electorally
(69–70). Similarly, when legislative and executive elections are held separately in presidential systems, voters are only able to hold to account the particular branch of government
that happens to be facing election on that occasion (Hellwig and Samuels 2008, 70; Samuels
2004). Thus parliamentary systems as opposed to presidential systems and concurrent elections within presidential systems are both linked to reduced clarity of responsibility.
4.1. CLARITY OF RESPONSIBILITY: ECONOMIC VOTING IN DIFFERENT CONTEXTS
93
Other proposed revisions to the Powell–Whitten clarity of responsibility index are concerned with characteristics of the incumbent government. For example, it has been suggested
that divided government within presidential systems might impede clarity of responsibility,
although there is evidence that American voters, at least, respond to this problem by simply
holding the president accountable, irrespective of which branch is actually responsible (Norpoth 2001). C. J. Anderson (2000) has argued that both ‘governing party target size’—-the
degree to which a single party dominates the incumbent government (154–155)—and ‘clarity
of available alternatives’—the ease with which voters can identify a clear alternative government from among the opposition parties (155–156)—ought to mediate the economic vote.
Using Eurobarometer data from 1994, he showed that both of these features were related to
the level of economic voting measured in a country (160–168). Nadeau, Niemi and Yoshinaka
(2002, 404) also proposed a revision to the Powell–Whitten clarity of responsibility index on
the grounds that the original index is mainly composed of relatively static items, so it offers
little scope to measure any changes over time. They extended the index to include measures
of four transient characteristics that are hypothesised to affect clarity of responsibility. The
first of these is Anderson’s notion of governing party target size. The second is the ‘ideological
cohesion of the governing coalition‘, that is, the proportion of government members of parliament who share the dominant party’s ideology, defined as simply left or right. The third item
is the length of time the current government has been in office. The final item is the number
of parties holding a minimum threshold (three percent) of seats in the parliament, which is
intended to measure Anderson’s clarity of available alternatives (409–411).
These various developments in the clarity of responsibility theory were brought together by
by Hobolt, Tilley and Banducci (2013), who argued that the concept of clarity of responsibility
ought to be seen as a two-dimensional construct, with one dimension measuring institutional
clarity and the other government clarity (168). Institutional clarity refers to those aspects of
clarity of responsibility resulting from stable institutional arrangements, whereas government
clarity refers to the clarity arising from the cohesiveness of the current national government.
Items forming part of the institutional clarity dimension include formal divisions of government power such as a bicameral legislature, a federal state, clear separation between the
legislative and executive branches and a strong committee system in which opposition parties
routinely chair important committees (169). Items forming part of the government clarity dimension are those indicating the cohesion of the incumbent government, such as the number
of parties in the cabinet, the ideological cohesion of the cabinet as a whole and the relat-
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CHAPTER 4. CLARITY OF RESPONSIBILITY DURING A GLOBAL RECESSION
ive strength of the head of government’s party in the cabinet (170). Using the 2009 European
Election Studies survey, they hypothesised that both clarity dimensions would impact the level
of economic voting measurable, with higher clarity countries experiencing a greater level of
economic voting. What they found, however, was that only government clarity was a strong
predictor of economic voting, with institutional clarity playing little if any role (175–178).
They argue that it is not so much the case that institutional clarity is of no importance but
rather that institutional clarity is likely to be a key determiner of government clarity, which in
turn mediates the economic vote (180).
One thing that remains unclear in this body of research is whether the clarity of responsibility mechanisms tend to operate the same way under extreme economic conditions. As
with economic voting in general, most of the research that has been conducted has studied
behaviour during fairly typical economic conditions. Even when studies have examined the
Great Recession specifically, such as Hobolt, Tilley and Banducci (2013), who looked at the
European Union in 2009, they have not explicitly contrasted those times with more typical
periods. Given that the Great Recession was global in scale, it could be argued that individual
governments can hardly be responsible for its occurrence, since that responsibility is presumably shared by the governments of the other affected countries. This is in fact a clarity of
responsibility argument and so it is worth exploring whether the clarity of responsibility effect
changed during the crisis or remained stable throughout. This chapter does just this, by comparing the impact of clarity of responsibility among EU countries at the height of the Great
Recession, in 2009, to that well before the recession, in 2009, and that following the recession
in 2014.
4.2
The dimensions of clarity of responsibility
The two-dimensional approach to measuring clarity of responsibility developed by Hobolt,
Tilley and Banducci (2013) forms the starting point for the measures used in this chapter.
Their approach is used because it consolidates the various predictors identified by previous
studies and because it groups these predictors into two cohesive dimensions, rather than combining into a single index items related to institutional design with those relating to the incumbent government. Each dimension is measured by an index composed of four variables,
which are shown in Table 4.1. The first of their two dimensions, institutional clarity, is made
up of three dummy variables and one scale variable. The three dummy variables indicate a
4.2. THE DIMENSIONS OF CLARITY OF RESPONSIBILITY
95
Table 4.1: Components of institutional and government clarity
Item
Scale
Institutional clarity
Weak committee system
Parliamentary government
Unicameral parliament
Centralisation of government
1 = weak, 0 = strong
1 = parliamentary, 0 = semi-presidential
1 = unicameral or weak bicameral, 0 = strong bicameral
From 0 (highly federal) to 4 (completely unitary)
Government clarity
Single-party government
Absence of cohabitation
Ideological cohesion
Dominance of main party
1 = one party, 0 = coalition
1 = no cohabitation, 0 = cohabitation
Proportion (1 means ideologically unified government)
Proportion (1 means all cabinet posts held by same party)
These are the component items of the government and institutional clarity of responsibility
indices, according to Hobolt, Tilley and Banducci (2013).
weak parliamentary committee system, a parliamentary as opposed to semi-presidential system of government and a unicameral or weak bicameral system respectively. The scale variable
measures the centralisation of power at the level of central government, ranging from zero for
highly federal states to one for completely unitary states. These items are summed and scaled
so that the resulting measure ranges in principle from zero to one (174). Since these institutional features are not expected to change over short time periods (169), this chapter uses the
institutional clarity index reported by Hobolt, Tilley and Banducci (2013, 181) for each of the
countries in this study.
The second dimension is government clarity, which they also measure by an index composed of four variables. The first of these is a dummy variable indicating that there is currently
a single-party rather than a coalition government. The second is a dummy variable indicating,
for semi-presidential systems, that the prime minister and president are of the same party. In
other words, this variable indicates the absence of cohabitation. For parliamentary systems,
this variable is fixed at one, since there can never be cohabitation. The third variable represents the ‘ideological cohesion’ of the government and this is measured by the proportion of
government-held seats belonging to parties sharing an ideology with the head of government’s
party. The final component is the ‘dominance of the main governing party’, which is measured
by the proportion of cabinet positions held by the head of government’s party. Once again, the
final index is scaled so that it ranges in principle from zero to one (174).
Since the government clarity of a country can be expected to change frequently, it is not
sufficient to use the values of the index reported in the original paper (181) as these values are
specific to 2009. Instead, the index has been reconstructed from its components for each year
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CHAPTER 4. CLARITY OF RESPONSIBILITY DURING A GLOBAL RECESSION
and each country under study. In order to determine whether there was a single-party government and to find the dominance of the main governing party, information was needed about
the cabinet composition of each country at the appropriate time. This data was collected from
the European Journal of Political Research Political Data Yearbook.2 The absence of cohabitation variable only needs to be measured in the few countries using a semi-presidential system.
Hobolt, Tilley and Banducci (2013, 184n16) follow Elgie’s definition of a semi-presidential
system, which treats as semi-presidential any system in which there exists both a ‘popularly
elected fixed-term president [and] a prime minister and cabinet who are responsible to parliament’, irrespective of the division of powers between those institutions (Elgie 1999, 13).
Following from this definition, he includes Austria, Finland, France, Ireland, Lithuania, Portugal, Poland and Slovenia3 as examples of semi-presidential systems in Europe (14). This is a
rather broad definition and includes countries that others would classify as parliamentary. For
example, the description of modern Finland as semi-presidential is disputed,4 and in Ireland,
the few constitutional powers reserved to the president are exercised sparingly in practice (Elgie 2012). On the other hand, Hellwig and Samuels (2008, 81) found that ‘the direct election
of a president—whether powerful or weak—introduces a special element into electoral politics
under semi-presidentialism not present in pure parliamentary systems’ (emphasis in original).
This is a strong argument for using the broader definition of semi-presidentialism, so that
practice has been continued here. Among all of these semi-presidential countries, the only
instances of cohabitation coinciding with the relevant surveys were in Finland and Poland in
2009 and Portugal in both 2004 and 20095 (Döring and Manow 2015).
Hobolt, Tilley and Banducci (2013, 174) operationalise ideological cohesion as ‘the proportion of seats held by parties in government that are of the same ideology as the dominant
governing party’. In order to measure this, it is necessary to know which parties were in government at the appropriate times and how many seats each of them held in the parliament.
This data has been collected from the ParlGov database (Döring and Manow 2015). It is also
necessary to assign an ideology to each party. Following the original method, this was done
2
In many cases, this information was gathered from the ‘interactive’ interface to the PDY data (EJPR 2016).
At the time of writing, only a subset of the required data has been made available through that interface, so the
remaining data was gathered from the detailed annual reports published in the yearbook editions of the journal
(EJPR 2003–2015).
3
Of the EU member states, Bulgaria, Croatia and Romania were also included in the list but they do not form
part of this study because they acceded to the EU after 2004. Cyprus is the only EU member state with a pure
presidential system of government but cohabitation is impossible in a pure presidential system.
4
Nousiainen (2001, 108) writes that, since the constitutional reforms of the 1990s, ‘there are hardly any
grounds for the epithet “semi-presidential” ’ with reference to Finland.
5
There were also instances of an independent president in both Lithuania in 2009 and 2014 and Slovenia
in 2009. These were not treated as instances of cohabitation, following the example set by Hobolt, Tilley and
Banducci (2013).
4.3. HYPOTHESES
97
by dividing parties into ‘left’ and ‘right’ groups, according to their European Parliament party
groupings. In particular, parties belonging to the European United Left–Nordic Green Left,
the Progressive Alliance of Socialist and Democrats and the Greens–European Free Alliance
groupings and are treated as ‘left’ and all other parties are treated as ‘right’ (Hobolt, Tilley and
Banducci 2013, 184n18).6
In addition to government and institutional clarity, time in office is also treated in this
chapter as a clarity measure. The fact that the EES survey dates coincided with different
points in the electoral cycles of the surveyed countries means that some of the governments in
power at the time were elected much more recently than others. For example, some ‘incumbent’ governments had only been in power for six weeks at the time the survey took place,
whereas others had been in power for several terms. Time in government is only occasionally
identified in the literature as an aspect of clarity of responsibility—one example is Nadeau,
Niemi and Yoshinaka (2002, 410)—but it is not difficult to imagine that voters who had only
recently elected a new government might be less willing to judge that government harshly
for poor economic conditions—likely inherited from the previous government—than voters in
countries where the same party had been in power for a considerable period of time. There
are two types of models used in this chapter and time in government is measured slightly differently according to the type. For those models that analyse exclusively the prime minister’s
party in each country, time in government is measured by the number of years that the party
has held the office of prime minister. When multiple parties in each country are analysed together, this definition cannot be used because many incumbent parties do not hold the office
of prime minister, so time in government is measured by the number of years that each cabinet
party has held any cabinet posts at all. In both cases, the number of years is expressed as a
fraction, accurate to the number of days in office.
4.3
Hypotheses
The purpose of this chapter is to test whether clarity of responsibility effects existed during
the period under study and whether the magnitude of this effect changed as a result of the
Great Recession. Three potential effects have been derived from the clarity of responsibility
6
This method leads to a handful of surprising codings, particularly as some centrist parties could be plausibly
regarded as belonging to either group. Given that some codings are effectively arbitrary, any consistent dichotomous coding will lead to some minor anomalies. An alternative operationalisation was developed, which amounted
to the negated standard deviation of the left–right positions of each government member of parliament, where
this position is assumed to be the same as that of the party that member belonged to, measured on an eleven-point
scale. Although this eliminated the minor anomalies identified, the resulting measure was in fact less predictive
than the original, so the analysis reported here uses the original dichotomous measure.
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CHAPTER 4. CLARITY OF RESPONSIBILITY DURING A GLOBAL RECESSION
literature: a time in office effect; a government clarity effect; and an institutional clarity effect.
In each case, the chapter will test whether the effect in question exists and, if it does, whether
the magnitude of that effect changed over time. To this end, the chapter tests three specific
hypotheses. The first of these is:
Hypothesis 4.1 Governments that have recently been elected are subject to less economic voting
than governments that have been in power for some time.
As was stated in the previous section, there is good reason to believe that a government that
has been in power for a longer period of time, and therefore had more opportunity to influence
the economy, might be more exposed to economic voting than a government that is relatively
new to office.
The second hypothesis is that there is a traditional clarity of responsibility effect:
Hypothesis 4.2 Countries with high clarity of responsibility experience a greater level of economic voting than countries with low clarity. This applies to both government and institutional
clarity.
Since this chapter measures clarity of responsibility by the two different dimensions of government and institutional clarity, this effectively has two sub-hypotheses, which are tested
separately. If this hypothesis is found to hold, then this would naturally be considered evidence in support of the clarity of responsibility theory. If, on the other hand, this hypothesis is
rejected, then this would support the idea (van der Brug, van der Eijk and Franklin 2007, 16)
that the instability problem of economic voting theory results from model misspecification,
since the method used to measure the economic vote in this thesis is informed by suggestions
for a better approach (26–29).7
The final hypothesis concerns clarity of responsibility during the recession period:
Hypothesis 4.3 Clarity of responsibility had a smaller influence on the level of economic voting
during the Great Recession than it did before or after.
There are two reasons to expect that a global recession of such a scale might erode any economic voting differences between high- and low-clarity countries. The first reason is that the
recession was presumably a highly salient issue for voters, so that even in low-clarity countries, voters might be more motivated than usual to hold someone accountable, even if it is
7
These ideas have not been adopted in their entirety. See Chapter 2 for a detailed explanation of how economic
voting is measured in this thesis and the reasons behind this approach.
4.4. HOW CLARITY AFFECTS THE PRIME MINISTER’S PARTY
99
not necessarily as obvious who ought to be held to account as it is in other countries. The
second reason is that the Great Recession was not a mere local recession but an international
one that began outside of Europe. This being the case, it is possible that voters in high-clarity
countries might find it difficult to hold their own governments to account for a crisis that those
governments arguably had very little control over.
4.4
How clarity affects the prime minister’s party
The economic voting models developed in the previous chapter have been extended in order
to measure how clarity of responsibility impacts the economic vote. This analysis is divided
into two parts. The first part focuses on the head of government’s party in each country
and specifically those factors affecting the degree to which relative support for that party8
was influenced by economic perceptions. This single-party approach has the advantage of
producing relatively simple models which are straightforward to interpret but they do not
take advantage of all the data available. The second part of this analysis uses this extra data
by looking at all parties together, not just the dominant government parties. The resulting
model is inevitably more complex than the single-party model but it offers a different view
of the evidence. Taken together, these two approaches allow for greater insight than either
approach used alone.
The first clarity of responsibility model, Model 4A, is based on Model 3A from the previous chapter, which predicts support for the head of government’s party in each country based
primarily on the left–right distance between the voter and the party, whether that voter identifies with the party and, crucially, the voter’s prospective economic assessment. The effect of
this economic assessment on support for the prime minister’s party is a measure of the overall
level of economic voting. In order to test the hypothesis that clarity of responsibility influences
the economic vote, therefore, this model must be extended to include an interaction between
the economic assessment predictor and the chosen measure of clarity of responsibility. This allows the model to estimate different levels of economic voting for different levels of clarity. As
the underlying model is a multilevel model, the fact that clarity of responsibility is a countrylevel variable is accounted for. The new model includes predictor terms for both institutional
8
As in the previous chapter, the dependent variable is a voter’s level of support for a particular party, centred
around that voter’s mean level of support for all parties. This means that even the models that focus on prime
ministers’ parties only still take some account of voter attitudes towards other parties, which is appropriate because
a voter is not expected to switch their vote unless their support for a new party exceeds their support for the party
they previously supported.
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CHAPTER 4. CLARITY OF RESPONSIBILITY DURING A GLOBAL RECESSION
Table 4.2: Alternative government clarity models
Fixed effect
Intercept
Year 2009
Year 2014
Prospective assessment
Time in office (PM)
Government clarity
Single-party government
Absence of cohabitation
Ideological cohesion
Dominance of main party
Institutional clarity
Prosp. assess. × year 2009
Prosp. assess. × year 2014
Prosp. assess. × time in office
Prosp. assess. × govt clarity
Prosp. assess. × single party
Prosp. assess. × no cohabitation
Prosp. assess. × cohesion
Prosp. assess. × dominance
Prosp. assess. × inst. clarity
Mod. 4A
Mod. 4B
Mod. 4C
0.54 (0.24)
0.33 (0.17)
0.04 (0.13)
0.13 (0.09)
0.02 (0.02)
−0.89 (0.29)
1.91 (0.41)
0.31 (0.17)
−0.06 (0.14)
−0.13 (0.14)
0.01 (0.02)
1.40 (0.32)
0.34 (0.18)
−0.06 (0.14)
−0.11 (0.10)
0.01 (0.01)
0.05 (0.26)
−0.08 (0.03)
−0.07 (0.03)
0.01 (0.00)
0.19 (0.09)
0.14 (0.12)
0.21 (0.19)
−0.27 (0.21)
−1.58 (0.32)
−0.14 (0.34)
−0.15 (0.26)
−0.07 (0.03)
−0.04 (0.03)
0.01 (0.00)
−0.05 (0.06)
−0.03 (0.07)
0.39 (0.09)
0.04 (0.11)
0.19 (0.12)
−1.39 (0.30)
−0.08 (0.24)
−0.07 (0.03)
−0.03 (0.03)
0.01 (0.00)
0.38 (0.08)
0.15 (0.11)
Comparison of clarity of responsibility models. The dependent variable is the individual’s
support for the current head of government’s party. Only the key fixed effect coefficients are
shown here, with standard errors in brackets. The full results can be found in Appendix B.
Source: EES, ParlGov & PDY
and government responsibility, as well as time in office.9 These three variables account for
the different types of clarity discussed in this chapter. The model furthermore includes interactions between each of these and the voter’s prospective economic assessment. This model
has been estimated and the key results are summarised in the first column of Table 4.2.
Before interpreting any figures in detail, it is worth discussing some alternative model specifications. The government clarity index consists of four component variables, as discussed
earlier. Model 4B replaces the index with its four components so that their relative impact can
be assessed.10 The key results from this model form the second column of Table 4.2. Comparing the two models, it is striking that the interaction between economic assessment and
ideological cohesion in the second model appears to be stronger than that between economic
9
This is specifically the number of years this party has held the office of prime minister, even if they held other
cabinet posts before that.
10
Multicollinearity might be a concern, as the components of an index can be expected to be correlated with
each other. In fact, the components are not so strongly correlated as to cause serious difficulties. The highest
correlation is between dominance of the main governing party and single-party government variables (r = 0.70)
but these variables are dependent since single-party government prevails if and only if the prime minister’s party
completely dominates the government. The second highest correlation is between dominance and ideological
cohesion (r = 0.31).
4.4. HOW CLARITY AFFECTS THE PRIME MINISTER’S PARTY
101
assessment and the government clarity index. Although it is not straightforward to compare
model coefficients directly, given the similar scale of the two variables, this difference is suggestive. It is also surprising that the interactions involving the other three component variables
are not even close to significance. This evidence is hardly conclusive but it does suggest that
ideological cohesion alone may be a better predictor of economic voting than the complete
index. In order to test this hypothesis, Model 4C was estimated, in which ideological cohesion alone replaces government clarity. The key results from this model make up the third
column of Table 4.2. Comparing this to the other models shows that this model fits the data
better (∆AIC = 26, ∆BIC = 27) than the government clarity model (Model 4A) and that the
components model (Model 4B) does not improve the model fit enough to justify the extra
complexity relative to this model (∆AIC = 7, ∆BIC = 61).
These results support the hypothesis that ideological cohesion alone is a better predictor
of economic voting levels than the complete index. Consequently, Model 4C forms the basis
of this analysis. As in the previous chapter, post-estimation simulation is used to derive key
quantities of interest rather than interpreting model coefficients directly. This chapter’s first
hypothesis is that governments that have been power for some time experience greater levels
of economic voting than governments which have been recently elected. The level of economic voting predicted from the model can be measured by the difference in support for the
prime minister’s party between a highly optimistic individual and an otherwise similar11 highly
pessimistic individual. Using this definition, the predicted level of economic voting for a government that has just been elected is 1.23 (SE = 0.16, p < 0.001). A party that has been in
power for a full decade, on the other hand, has a predicted economic vote of 1.50 (SE = 0.18,
p < 0.001). This amounts to a difference of 0.26 points (SE = 0.15, p = 0.07), which is not
statistically significant. In other words, it is possible that a longer time in office is associated
with a higher level of economic voting but the effect is weak if it exists at all.
The second hypothesis is that clarity of responsibility increases the level of economic voting. This has two sub-hypotheses, relating to government and institutional clarity respectively. Since government clarity is measured in this model by ideological cohesion alone, the
11
Unless otherwise stated, all predictions in this chapter are made for a context in which ideological cohesion,
institutional clarity and time in office are held at their means. This is 0.86 for cohesion and 0.61 for institutional
clarity. The mean time in office is 4.03 years where this refers to the time the party has held the office of prime
minister, as in this case, or 4.92 years where this refers to the time the party has been part of the governing
coalition, which becomes relevant later in the chapter. As in the previous chapter, the individual is an employed
40 year old male, living in a town, who has completed high school but not university.
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CHAPTER 4. CLARITY OF RESPONSIBILITY DURING A GLOBAL RECESSION
first sub-hypothesis is tested by comparing high-cohesion to low-cohesion contexts.12 The predicted economic vote for a low-cohesion country is 0.79 (SE = 0.21, p < 0.001) whereas the
predicted economic vote for a high-cohesion country is 1.54 (SE = 0.15, p < 0.001). This
corresponds to a difference of 0.75 points (SE = 0.16, p < 0.001) between high- and lowcohesion countries. In other words, the economic vote is almost twice (1.94, [1.39, 3.54]13 )
as high when the ideological cohesion of the government is high than when it is low. The
second sub-hypothesis pertains to institutional clarity. The predicted economic vote in highclarity countries14 is 1.08 (SE = 0.24, p < 0.001) and the predicted economic vote in lowclarity countries is 1.58 (SE = 0.23, p < 0.001). The difference of 0.50 points (SE = 0.38,
p = 0.19) is not significant. Based on this model then, it appears that government clarity, and
in particular ideological cohesion, is the strongest predictor of economic voting in a country.
The third hypothesis is that these clarity of responsibility effects were weaker during the
recession than at other times. As the models introduced so far assume that these effects are
static over time, a new model is needed to test this hypothesis. Model 4D was created by
extending Model 4C with interactions between the time dummy variables and the clarity of
responsibility and time in government measures, thus allowing each of these effects to vary
in strength over time. This model was used to predict the level of economic voting under
different clarity contexts in each year. Figure 4.1 compares the predicted economic vote for a
government that has just been elected with that of a government that has held office for a full
decade. This plot shows that countries whose governments have held power for some time
experienced a greater level of economic voting than countries who have experienced a recent
change in government. This effect changes over time, appearing to vanish in 2009 and return
much stronger in 2014. These impressions are mostly borne out by a numerical analysis except
that the effect falls just short of significance (∆ = 0.77, SE = 0.40, p = 0.06) in 2004. The
effect is clearly not significant (∆ = 0.12, SE = 0.19, p = 0.52) in 2009, and the difference
between the two years is also not significant (∆ = 0.64, SE = 0.42, p = 0.13). By 2014, on
the other hand, the decade in office accounts for a 1.98 point (SE = 0.43, p < 0.001) increase
in the economic vote and this effect is significantly stronger than both earlier years (compared
to 2004, ∆ = 1.22, SE = 0.58, p = 0.04). In other words, time in office was only a strong
12
High cohesion means a cohesion measure of 1, indicating no differences in ideology among government
parties. Low cohesion means a cohesion measure of 0.5, indicating that half of the government-held seats are
held by parties not sharing an ideology with the prime minister’s party. This value has been chosen because lower
values are unlikely, since the prime minister’s party is typically to be the largest governing party. It is also the
lowest value occurring in the dataset but it is by no means an outlier.
13
Square brackets indicate 95% confidence intervals.
14
High clarity means an institutional clarity measure of 1 and low clarity a measure of 0.18, these being the
highest and lowest scores assigned to any country by Hobolt, Tilley and Banducci (2013).
4.4. HOW CLARITY AFFECTS THE PRIME MINISTER’S PARTY
103
Figure 4.1: Economic vote for dominant government party by time in office
2.5
economic vote
2.0
time in office
10 years
1.5
just elected
1.0
0.5
2004
2009
2014
year
Predicted level of economic voting for the head of government’s party according to the time
that the party has held office and the survey year. The economic vote is the difference in support for the party from a highly optimistic and a highly pessimistic voter based on predictions
from Model 4D. Ideological cohesion and institutional clarity are held at their means. Source:
EES, ParlGov & PDY
predictor of economic voting in 2014. The fact that this effect was so weak in earlier years
may explain why no effect was found when it was assumed not to vary over time.
Figure 4.2 shows the predicted level of economic voting for a high-cohesion and a lowcohesion context in each year. A high-cohesion context is a country in which all of the governing parties are ideologically similar and a low-cohesion context is one in which half of
the government-held parliamentary seats belong to parties not sharing the ideology of the
prime minister’s party. This shows a similar pattern to the previous figure, in that there is
an apparent difference between the two groups in 2004, which closes in 2009 before widening again in 2014. These observations are supported by numerical analysis, which shows
that in 2004, the economic vote experienced in a high-cohesion context was 1.65 points
(SE = 0.41, p < 0.001) higher than in a low-cohesion context. By 2009, this difference
had fallen (∆ = 1.37, SE = 0.47, p < 0.01) to 0.29 points (SE = 0.27, p = 0.28) and by 2014
had risen once again (∆ = 0.68, SE = 0.33, p = 0.04) to 0.97 points (SE = 0.24, p < 0.001).
In other words, high ideological cohesion was associated with more economic voting before
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CHAPTER 4. CLARITY OF RESPONSIBILITY DURING A GLOBAL RECESSION
Figure 4.2: Economic vote for dominant government party by ideological cohesion
economic vote
1.5
1.0
cohesion
high
low
0.5
0.0
2004
2009
2014
year
Predicted level of economic voting according to the ideological cohesion of the incumbent
government and the survey year. The economic vote is the difference in support for the prime
minister’s party from a highly optimistic and a highly pessimistic voter based on predictions
from Model 4D. Institutional clarity and time in office are held at their means. Source: EES,
ParlGov & PDY
and after but not during the Great Recession. This supports the third hypothesis with respect
to government cohesion.
Similarly, Figure 4.3 shows the relationship between institutional clarity and economic
voting by comparing the difference between high and low scores on the institutional clarity
index. This figure shows little if any difference between high- and low-clarity contexts in
2004 and 2009 but a large difference in 2014. Numerical analysis confirms that there was
no significant effect in either 2004 (∆ = −0.19, SE = 0.51, p = 0.71) or 2009 (∆ = 0.43,
SE = 0.53, p = 0.42), nor is there a significant difference between these two years (∆ = 0.62,
SE = 0.47, p = 0.18). In 2014 on the other hand, the economic vote was considerably stronger
among high-clarity countries than low-clarity countries (∆ = 1.67, SE = 0.50, p < 0.001),
which is a significant increase over both other years (compared to 2009, ∆ = 1.24, SE = 0.47,
p < 0.01). This means that high institutional clarity was not a predictor of economic voting
either before or during the Great Recession but it did become one in the aftermath of the
recession. This evidence provides only qualified support for the third hypothesis with respect
4.5. THE EFFECT OF CLARITY ON OTHER PARTIES
105
Figure 4.3: Economic vote for dominant government party by institutional clarity
economic vote
2.0
1.5
clarity
high
low
1.0
0.5
2004
2009
2014
year
Predicted level of economic voting according to the institutional clarity of the country and
the survey year. The economic vote is the difference in support for the prime minister’s party
from a highly optimistic and a highly pessimistic voter based on predictions from Model 4D.
Ideological cohesion and time in government are held at their means. Source: EES, ParlGov &
PDY
to institutional clarity, since it was predicted that the importance of institutional clarity would
be weaker during the recession than either before or afterwards.
4.5
The effect of clarity on other parties
The models discussed so far have focused on support for the current prime minister’s party.
This approach has the advantage of simplicity but it also has the disadvantage that it disregards
respondents’ levels of support for coalition partners and opposition parties, so it does not use
all of the data available. This means that while it is possible to determine the degree to which
prime ministers’ parties are supported relative to other parties in general15 it is not possible to
compare them to opposition parties specifically or to examine support for government parties
more generally using this approach. A further model (Model 4E) has been constructed using
the available data for every party so that the contrasting effects of economic voting between
15
Since the dependent variable, party support, is centred around its individual-level mean, these models are
measuring support relative to the other key parties in each country rather than absolute support. This centring
has both theoretical and data-driven motivations, which are discussed in detail in Chapter 2.
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CHAPTER 4. CLARITY OF RESPONSIBILITY DURING A GLOBAL RECESSION
government and opposition parties in general can be examined in different clarity contexts.
This model is based on Model 3D, which predicts party support based on, among other things,
the individual’s prospective economic perceptions and whether the party is a government or
opposition party. This was extended to take into account clarity of responsibility by allowing
the key economic voting terms to interact with the clarity of responsibility predictors. In
the base model, economic voting is measured by a three-way interaction between economic
assessment, incumbency and time. This means that the model allows government and nongovernment parties to be affected differently by an individual’s economic assessment, which is
the key claim of economic voting theory, and further allows the strength of this economic vote
to change over time. Clarity of responsibility theory further asserts that the level of economic
voting varies according to country-level clarity variables and if these effects are to be allowed
to vary over time, this necessitates a four-way interaction.16 This is admittedly a high level of
complexity but it reflects the complexity of the hypotheses.17 Without these interaction terms,
it would not be possible to determine whether clarity of responsibility had a stable effect on
economic voting propensity over time.
Once again, post-estimation simulation has been used to interpret this model. The method
used to ascertain the effect of the clarity of responsible variables on economic voting is to compare the level of economic voting under different clarity scenarios in each year. In this instance,
the level of economic voting is defined in terms of relative government support. Relative government support is the difference between the individual’s support for a government party and
that same individual’s support for an opposition party. This is positive if that individual generally prefers government parties and negative if that individual generally prefers opposition
parties. The economic voting level is the difference between the relative government support
of a highly optimistic individual and that of a highly pessimistic individual. The higher the
economic voting level, the more influence economic conditions have on the decision as to
whether to support the government or the opposition. If this is zero then the economy plays
no role at all. A negative economic voting level would be surprising as this would indicate
that governments are harmed electorally by good economic conditions.
This model has been used to test the chapter’s hypotheses once again, as the different
approaches offer different perspectives. The first hypothesis is that the economic vote is greater
16
Ideological cohesion and institutional clarity are both allowed to interact with the three-way economic voting
interaction. Time in government is only meaningful for parties that are actually in government, so this variable
only interacts with time and economic assessment. As always, the coefficient estimates are shown in Appendix B.
17
Van der Brug, van der Eijk and Franklin (2007, 210–212), who use the same dependent variable, also use
four-way interactions in some of their economic voting models. Although their method of measuring the economic
vote is quite different, this illustrates that this level of complexity inevitably arises when studying the economic
vote through the lens of party support.
4.5. THE EFFECT OF CLARITY ON OTHER PARTIES
107
Figure 4.4: Government and opposition economic vote by time in office
economic vote
1.2
time in office
just elected
10 years
1.0
0.8
2004
2009
2014
year
Predicted level of economic voting in each survey year for a party that has been in government
for ten years compared to a party that has just been elected to government. The economic vote
is the difference in relative support for the party compared to an opposition party between a
highly optimistic and a highly pessimistic voter based on predictions from Model 4E. Ideological cohesion and institutional clarity are held at their means. Source: EES, ParlGov & PDY
in countries where the government has been in power for a longer period of time. Figure 4.4
compares the predicted economic vote of a government that has been in power for ten years to
one that has only just been elected. This figure shows that there is an economic voting effect
favouring both short-term and long-term governments in all years. Surprisingly, governments
that have only been in power for a short period appear to have been subject to slightly stronger
economic voting than those that have been in power for a longer time in both 2004 and 2009.
Neither of these differences is significant, however, the larger of the two being the 0.15 point
(SE = 0.16, p = 0.35) difference in 2009. None of the three groups saw a significant difference
in the economic vote between 2004 and 2009. By 2014, long-term governments were exposed
to a greater level of economic voting than short-term governments (∆ = 0.35, SE = 0.18,
p = 0.04). The increase in the economic vote experienced by long-term governments from
2009 to 2014 was 0.58 points (SE = 0.25, p = 0.02). None of the other differences over
time were significant. Based on these results, it appears that time in office only played an
important role in 2014. One possible explanation for this is that those parties who held power
continuously since the Great Recession were seen differently by voters from parties that had
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CHAPTER 4. CLARITY OF RESPONSIBILITY DURING A GLOBAL RECESSION
Figure 4.5: Government and opposition economic vote by ideological cohesion
economic vote
1.5
1.0
cohesion
high
low
0.5
0.0
2004
2009
2014
year
Predicted level of economic voting according to the ideological cohesion of the incumbent
government and the survey year. The economic vote is the difference in relative support for
a governing party compared to an opposition party between a highly optimistic and a highly
pessimistic voter based on predictions from Model 4E. Institutional clarity and time in government are held at their means. Source: EES, ParlGov & PDY
been elected since that time. This would make sense as it means that parties who could be
seen as responsible for the recession experienced a stronger economic vote.
Figure 4.5 contrasts the predicted economic vote of highly ideologically cohesive governments with that of less cohesive governments. It can be seen that the economic vote experienced by highly cohesive governments was stronger than that experienced by less cohesive
governments. This difference amounts to 2.07 points (SE = 0.59, p < 0.001) in 2004, before
falling by 1.86 points (SE = 0.69, p < 0.01) before 2009, when the difference between the
two groups almost vanishes (∆ = 0.21, SE = 0.37, p = 0.57). The difference between 2009
and 2014 is not significant (∆ = 0.74, SE = 0.50, p = 0.14) but the size of the gap between
the two groups in 2014 is 0.95 points (SE = 0.33, p < 0.01). An anomaly arising from this
analysis is that the predicted economic vote for low cohesion governments in 2004 is actually
negative (∆ = −0.36, SE = 0.54, p = 0.50) but this is not statistically significant. In summary,
it appears that the ideological cohesion of the government does mediate its exposure to the
economic vote but it also appears that this effect was weakened by the Great Recession. These
4.5. THE EFFECT OF CLARITY ON OTHER PARTIES
109
Figure 4.6: Government and opposition economic vote by institutional clarity
1.8
economic vote
1.5
clarity
1.2
high
low
0.9
0.6
2004
2009
2014
year
Predicted level of economic voting according to the institutional clarity of the country and
the survey year. The economic vote is the difference in relative support for a governing party
compared to an opposition party between a highly optimistic and a highly pessimistic voter
based on predictions from Model 4E. Ideological cohesion and time in government are held
at their means. Source: EES, ParlGov & PDY
findings support both the government clarity hypothesis and the hypothesis that this clarity
effect was weaker during the recession than at other times.
Similarly, Figure 4.6 compares the predicted economic vote of governments in countries
with high institutional clarity to that of governments in low-clarity countries. Unfortunately,
owing to the uncertainty in the model estimates, there is little that can be confidently said
about the effect of institutional clarity. The only year in which there is a statistically significant difference between the groups is in 2004, when low clarity countries experienced a
1.18 point (SE = 0.53, p = 0.03) stronger economic voting effect than high clarity countries.
This runs opposite to the hypothesised effect, which was that high clarity countries should
have a stronger economic vote. The point estimate of the effect was in the opposite direction
in both 2009 (∆ = 0.43, SE = 0.48, p = 0.37) and 2014 (∆ = 0.61, SE = 0.49, p = 0.21) but
neither of these estimates is significant. There was significant difference in effect size between
2004 and 2009 (∆ = 1.60, SE = 0.72, p = 0.03) but not between 2009 and 2014 (∆ = 1.78,
SE = 0.69, p = 0.80). Given that the clarity effect is either not significant or significant but
opposite to the expected sign, the conclusion has to be drawn that this model does not support
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CHAPTER 4. CLARITY OF RESPONSIBILITY DURING A GLOBAL RECESSION
the institutional clarity hypothesis. Although there appear to be some significant time effects,
in the absence of clear support for the basic hypothesis, it would be unwise to attempt to find
support for the hypothesis that the effect varies overs time. In summary, institutional clarity
does not appear to be a reliable predictor of economic voting, at least according to this model.
4.6
Conclusion
This chapter has examined the impact of clarity of responsibility as a mediator of the economic
vote before, during and after the Great Recession. Clarity of responsibility was divided into
three facets, each representing a distinct quality of a political context expected to affect the
ease with which voters can hold the incumbent government responsible for the prevailing
economic conditions. These facets were government clarity, institutional clarity and time in
government. Government clarity refers to those characteristics of the incumbent government,
such as consisting of a coalition of parties, that might make it difficult for voters to identify a
party to hold to account. Institutional clarity refers to institutional characteristics having the
same effect, such as the presence of a strong upper house or a federal system of government.
These concepts were measured using the indices proposed by Hobolt, Tilley and Banducci
(2013), except that it was found that the government clarity index was outperformed by one
of its component measures, the ideological cohesion of the government, which was therefore
used in its place as the measure of government clarity. Finally, time in government refers
to the amount of time that a party has held office, since a party that has only recently been
elected could plausibly claim not to have had an opportunity to influence the condition of the
economy.
Three hypotheses were tested in this chapter, the first of these being that a longer period
of time in government was associated with a stronger economic vote. This hypothesis was
not supported by the data. When the effect was modelled as constant over time, there was
no significant difference between parties that had been in power for a short period and those
that had been in power longer. When the size of the effect was allowed to vary over time, a
significant effect was only found in 2014. This was the case whether all parties were analysed
together or the analysis was restricted to prime ministers’ parties, even though time in office
was operationalised differently by the two approaches. This is certainly not strong enough
evidence to claim that time in government is a mediator of the economic vote but it is interesting that there does appear to be some effect shortly after the Great Recession, since this
4.6. CONCLUSION
111
effectively means that time in government does matter when that time implies that the government held office during the recession. In other words, this suggests that voters were more
closely monitoring the economic performance of governments that survived the recession.
The second hypothesis is that greater clarity of responsibility is associated with a higher
level of economic voting. There are two sub-hypotheses, in that this is expected to apply both
to government clarity and institutional clarity. As mentioned earlier, government clarity is
measured in this chapter by the ideological cohesion of the incumbent government. In each
of the models, a positive relationship was found between government clarity and the level of
economic voting. The same cannot be said for institutional clarity. No significant institutional
clarity effect was found when the effect was assumed to be constant over time. Even when
the effect was assumed to vary with time, no significant effect was found apart from in 2014
when considering only prime ministers’ parties. Even worse, when examining all parties together, the effect was found to be in the opposite direction to that hypothesised in 2004 and
otherwise not significant. In other words, the data offers strong support for the hypothesis
with respect to government clarity but little if any support with respect to institutional clarity.
This mirrors the findings of Hobolt, Tilley and Banducci (2013, 177), who first made this distinction explicit and who also found that government clarity outperforms institutional clarity
as a predictor of economic voting levels. The fact that some clarity effects were found using
a contextual model of economic voting shows that existing clarity of responsibility findings
cannot be completely explained away as an artefact of model misspecification. On the other
hand, the lack of support for any clarity effect other than ideological cohesion supports the
argument that model misspecification plays at least some role in the instability of economic
voting results (van der Brug, van der Eijk and Franklin 2007, 16).
The final hypothesis is that clarity of responsibility was a weaker predictor of the economic
vote during the recession that it was at other times. With respect to government clarity, this
hypothesis has strong support. Whether all parties were analysed together or the analysis
focused solely on the parties of the heads of government, the findings were the same. The level
of economic voting was considerably higher in high cohesion countries than in low cohesion
countries in both 2004 and 2014. In 2009, during the recession, this effect was not nearly
as strong and, in fact, was not significantly different from zero, although the point estimates
were still positive. In other words, the recession temporarily eroded the distinction between
high- and low-clarity countries. In principle this hypothesis also applies to institutional clarity
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CHAPTER 4. CLARITY OF RESPONSIBILITY DURING A GLOBAL RECESSION
but since institutional clarity was not shown to a predictor of the economic vote at all, this
sub-hypothesis is not meaningful.
In summary, it was found that there was a clarity of responsibility effect, in that voters in
countries with highly ideologically cohesive governments were more likely to vote economically than those in countries with ideologically fragmented governments. It was further found
that the level of economic voting was greater before and after the Great Recession than it was
during its peak. Considered in conjunction with the finding from the previous chapter that the
overall level of economic voting was reduced during the same period, this suggests that the
event itself had the effect of clouding economic responsibility. This makes sense as the recession was international in scale and had begun in the United States before spreading to Europe,
so governments could plausibly deny responsibility for it. By 2014, however, governments had
had plenty of time to react to the crisis and economic voting levels as well as the mediating
effect of government clarity had returned to their pre-crisis levels. Any governments that had
held power continuously since the crisis were held responsible to an even higher degree than
before for the condition of the economy.
This chapter and the preceding chapter have looked at the circumstances under which
poor economic conditions voters are likely to cause voters to abandon their support for their
incumbent governments. This raises the question of which party or parties are likely to benefit
from this shift in allegiances. The next chapter seeks to answer this question by examining
whether some types of parties are more likely to receive the support of economic voters than
others.
Chapter 5
Extreme and Eurosceptic parties: the changing
policy preferences of European voters
In the Greek parliamentary elections of 25 January 2015, Syriza won 149 of the 300 seats
in the Hellenic Parliament. This allowed the radical left party to seize power from the grand
coalition that had governed Greece since 2012 (Matakos and Xefteris 2016). The coalition
parties, which included the centre-right New Democracy, the centre-left PASOK and at various
times other parties, had desired to maintain Greece’s membership of the Eurozone, which
meant imposing the austerity measures demanded by their creditors (Rori 2016). Syriza,
on the other hand, stood on an anti-austerity platform.1 Much has been written about the
situation in Greece but it raises interesting questions about how voters respond to economic
crises. Why did Greek voters turn to Syriza specifically? Was it because they were leftist, or
because they were Eurosceptic, or simply because they promised to end austerity? Was this
event part of a greater Europe-wide trend?
This chapter explores these questions by examining which types of parties have benefited
from the events of the Great Recession and which have not. It will be seen that there are many
different ways that parties have been classified but for the purposes of this chapter they will be
primarily classified according to their positions on economic issues, social issues and European
integration. Previous chapters have shown that incumbent parties lost support from voters
with a pessimistic economic outlook during the recession, although not by as much as at other
times. The purpose of this chapter is to establish which parties gained support as a result. The
focus is not on individual parties so much as types of parties. Syriza, for example, is a radical
left party (Stavrakakis and Katsambekis 2014), so it could be argued that Greek voters shifted
to the left. On the other hand, it could also be argued that they voted for Syriza not because
1
Despite this platform, the Syriza-led coalition government ultimately negotiated a new bailout programme
as well as further austerity. They were nonetheless re-elected at the snap September election in the same year
(Matakos and Xefteris 2016; Rori 2016).
113
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CHAPTER 5. EXTREME AND EUROSCEPTIC PARTIES
it is specifically left-wing but because it advocated a radical rejection of the status quo. In
other words, a right-wing party that promised to abandon austerity might have been equally
successful. Syriza has also been described as a ‘soft Eurosceptic’ party (Verney 2015, 280),
although their leader Alexis Tsipras is reported to have rejected all forms of Euroscepticism
(To Vima 2014). These are the three categories of parties that might be expected to benefit
from the crisis: parties on one side of the spectrum, parties far from the centre, and Eurosceptic
parties.
This chapter begins by discussing the specific hypotheses that will be tested. After that,
there is an explanation of how parties’ positions have been measured. Spatial position in
particular is measured in two different ways, both as a single-dimensional left–right spectrum
and as a two-dimensional space. The remaining sections discuss how parties’ positions towards
European integration and their spatial positions, in both one and two dimensions, affected
support for those parties before, during and after the crisis. This is done by extending the
multilevel model of party support introduced in Chapter 3 so as to control for the economic
voting effect that has already been observed. The results show that the parties who benefited
most from the recession are Eurosceptic parties and those holding more extreme2 economic
positions and conservative social positions. Since most of these changes took place well after
the recession, it is argued that the political response to the recession, rather than the recession
itself, has been the main driver of these changes.
5.1
Hypotheses
The purpose of this chapter is to determine which types of parties benefited from the Great Recession. The previous chapter showed that there was an economic voting effect, that is, voters
who held a pessimistic economic assessment preferred opposition parties over government
parties. Not all opposition parties are equal, however. This chapter tests several hypotheses
about which groups of parties were most likely to receive new support from voters as a result
of the Great Recession. As in previous chapters, the primary sources of data are the 2004,
2009 and 2014 waves of the EES voter surveys. The first of these waves was collected well
before for the beginning of the crisis, the second set of surveys was collected at a time when
most of the EU countries were in recession as a result of the crisis and the third wave was
collected well after the initial crisis but at a time when many countries were suffering from
2
The term ‘extreme’ in this chapter is used to mean positions that are relatively far from the political centre of
the relevant country, irrespective of whether they lie to the left or the right.
5.1. HYPOTHESES
115
double-dip recessions or grappling with controversy resulting from severe austerity measures.
It is therefore expected that the voter response to the recession would have first arisen in
2009 and perhaps receded somewhat by 2014 and this is the time frame used in each of the
following hypotheses.
One possibility is that there was a voter backlash against those parties that were seen as
supportive of the institutions of the European Union. Given the international scale of the recession, domestic politicians may have been able to attribute some of the blame for the recession
to the EU (Hobolt and Tilley 2014), particularly since EU institutions do control important
aspects of European economic policy, notably including monetary policy for countries using
the shared currency. Furthermore, the EU was also seen to play a role in the often unpopular
austerity measures that were imposed in many countries as a result of the crisis. This being the case, it would not be surprising if parties advocating more national autonomy gained
some support at the expense of those parties advocating further integration within the EU.
This question has been addressed by Hobolt and Leblond (2014), who found that support for
remaining in the Eurozone has remained consistently high throughout the crisis and its aftermath, even in countries with severe economic problems or stringent austerity measures. They
argue that this is because citizens estimate the risks associated with leaving to be greater than
those of staying. As theirs was an aggregate study, it will be interesting to see whether their
results can be replicated with an individual-level model. The first hypothesis is thus:
Hypothesis 5.1 The Great Recession provoked a shift in support away from parties supporting
further European integration and towards those opposing it.
Differing convictions about how best to manage the economy are one of the fundamental
divisions underlying the political spectrum. This being the case, it is not difficult to imagine
that a severe economic shock could shake voters’ confidence in some of these positions and lead
them to prefer parties with somewhat different approaches. This naturally raises the question
of whether voters should be expected to move to the left or the right following an event like
the Great Recession. The popular expectation seems to be that voters should move towards the
left. For example, commenting on the results of the 2009 European Parliament elections, one
commentator described the left as having ‘failed to capitalise on an economic crisis tailor-made
for critics of the free market’ (The Economist 2009). This analysis is typical of the newspaper
commentary of those election results, with the left seen as the natural beneficiaries of the Great
Recession, but somehow failing to take advantage of their position (Lindvall 2012, 514).
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CHAPTER 5. EXTREME AND EUROSCEPTIC PARTIES
Within the academic literature, on the other hand, the tendency is to find that poor economic conditions are typically followed by a rightwards shift. For example, Durr (1993) attempted to explain why the policy preferences of US voters were further to the left in some
periods, such as the 1960s, than in other periods, such as the 1980s. He proposed that there
was a link between support for redistributionary policies and economic conditions, namely
that voters were more in favour of these policies when they felt economically secure but saw
them as a form of discretionary spending to be reigned in when they felt the economy itself
was threatened. This has been described as a ‘luxury model’ (Anderson and Hecht 2014, 57).
It was found that support for leftist policies was indeed highest when optimism about the
economy was high and low when attitudes were more pessimistic (Durr 1993, 167). These
results were later replicated in a comparative study of fourteen European countries (Stevenson 2001). An alternative to the luxury model is the idea that the different kinds of economic
troubles are associated with different policy preferences and there is some support for this idea
too. Erikson, MacKuen and Stimson (2002) studied aggregate trends in US politics in diverse
specifics between 1952 and 1996. One of their findings was that ‘policy mood’, a composite
measure of survey responses to specific policy questions, tends to the left when unemployment
is high and to the right when inflation is high (232–235).
There have been some observations of a rightwards shift during the Great Recession but
these are hardly decisive. Soroka and Wlezien (2014), for example, found that there has been
a long-term drop in support for redistribution among British Labour supporters in particular
and that the Great Recession has not arrested the long term trend. Bartels (2014) found that
there did appear to be a general move towards the right following the Great Recession but
also that the effect size is much smaller when controlling for economic conditions—the countries with left-wing incumbents were often those where the recession was worst (199). They
conclude that ‘the Great Recession produced surprisingly little overall change in the ideological proclivities of voters—and that retrospective voting was a stronger and more consistent
factor than ideology in accounting for observed shifts in electoral behavior in OECD countries’
(200). Lindvall (2014) argues that the change in preference over time is more complex than a
simple leftwards or rightwards trajectory. By comparing the patterns of electoral results in the
aftermath of the Great Recession and the Great Depression, he found that in both cases voters
tended to move towards the right in the short term and back towards the centre or even the
left as the crisis continued, even when controlling for economic voting. He proposes that this
5.1. HYPOTHESES
117
could be explained as a result of the different social classes becoming exposed to the severity
of an economic downturn at different times (Lindvall 2013, 149).
These studies inform the second hypothesis:
Hypothesis 5.2 The Great Recession led to increased support for the parties of the right relative
to those of the left, particularly in the short term.
Alternatively, it may be the case that rather than turning to the parties of the left or the
right specifically, voters have turned away from the mainstream parties and towards extreme
or fringe parties. The argument behind this idea is that the mainstream parties suffered a
loss of credibility by failing to prevent the Great Recession from taking place. Since this event
had deep structural causes, even those mainstream parties that were in opposition at the time
could have been tainted by the crisis. In any event, the parties in power across Europe were not
uniformly of the left or the right, so neither the centre-left nor the centre-right could plausibly
claim to be blameless so long as any blame could be attributed to national governments. This
being the case, it might be expected that voters would turn to those parties who are critical of
the established political order or who seek to advance radical or anti-system policy platforms.
Aggregate cross-national election result data has already shown some evidence of an increase
in support for the populist parties of the radical left and radical right, especially in Western
Europe (Hernández and Kriesi 2015). This forms the basis of the third hypothesis:
Hypothesis 5.3 The Great Recession led to a shift in support from centrist parties to more extreme
parties.
There have already been studies on whether the recession has helped more extreme parties
but no consensus has been reached. For example, Allen (2015) studied support for extreme
right parties, defined as those occupying the right–authoritarian corner of the political space,
also using the 2004, 2009 and 2014 waves of EES survey data. He found some support for his
hypothesis that economic grievances were associated with greater support for far right parties
in the period after the crisis. Similarly, Mayer (2014) looked at whether the French working
class moved to the far right as a result of the Great Recession. It was found that although
the far-right National Front has increased its support, particularly among the working class,
a former stronghold of communist support, the magnitude of this increase was actually quite
small (288–290). Grittersova et al. (2015), on the other hand, argued that austerity actually
suppresses support for extreme parties. Using election-year panel data from sixteen countries
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between 1978 and 2009, they found that austerity policies are associated with reduced support
for both radical left and radical right parties. Importantly, their data does not extend to the
period of austerity following the Great Recession, although the authors argue that there is
no obvious cause to believe that this would change their results (21). Given this diversity in
results, the hypothesis is worth testing here.
Lastly, it should not be assumed that all voters would respond to the recession in the same
way. One obvious question is whether poor and rich voters responded similarly. Duch and
Sagarzazu (2014, 225) use panel data to examine voter behaviour from the 2009 German and
2010 British parliamentary elections. They measured the economic vote and economic preferences of different groups of voters, finding that both rich and poor voters had similar levels of
economic voting but that the poor continued to prefer greater levels of redistribution than the
rich (253). This still leaves open the possibility of an individual’s economic assessment influencing their policy preferences. It is also well known that voters are influenced by economic
conditions when deciding whether to vote for the incumbent government and it was shown
in Chapter 3 that this economic voting effect can be observed in the party support levels of
survey respondents before, during and after the recession. In particular, it was found that a
voter’s prospective economic assessment influenced that voter’s support for incumbent parties.
Similarly, if any of the predicted trends are observed then it is also expected that these effects
would be influenced by the voter’s economic assessment. For example, if support for extreme
parties did increase as a result of the recession, it is expected that the size of this increase
would be greatest among those voters who were pessimistic about the future of their national
economy and least among those who were optimistic. The final hypothesis for this chapter is
thus:
Hypothesis 5.4 These effects are strongest among voters believing that the economy was worsening and weakest among those believing it was improving.
5.2
Classifying parties
In order to test these hypotheses, the various parties need to be classified by some means.
There is a large literature on the classification of political parties, beginning with Duverger
(1959, 61–132), who drew a distinction between ‘mass’ and ‘cadre’ parties. This distinction
pertains to the basic structure of the party. Cadre parties are elite organisations, loose associations of like-minded politicians, with no true membership beyond their candidates and
5.2. CLASSIFYING PARTIES
119
office holders. Mass parties, on the other hand, have a large membership drawn from the general population, who are committed to the party’s ideological project, pay dues to the party
and exercise some influence over the party’s elites. The cadre party model is the traditional
model of conservative parties, whereas the mass party model originated with European socialist parties and has been successful enough that some conservative parties have tried to emulate
it. Kirchheimer (1966) later introduced the notion of the ‘catch-all party’, observing that large
mass parties have a tendency to start trying to appeal to people outside of their traditional
support bases in order to improve their electoral performance. Building upon both earlier
works, Panebianco (1988, 262–274) argued that the key distinction among modern parties
is now between ‘mass bureaucratic’ and ‘electoral–professional’ parties. Mass bureaucratic
parties are still heavily membership-driven, depending upon financial dues and motivated by
ideology, whereas electoral–professional parties have only weak ties between their elites and
their membership, are often financed externally and seek primarily to appeal to the electorate. Katz and Mair (1995, 2009) take this further, arguing that the major parties have evolved
into ‘cartel’ parties, which are effectively part of the state itself. On the other hand, they note
that these parties face new opposition from emerging radical parties who challenge their close
relationship with the state (Katz and Mair 1995, 24).
These ideas are helpful for understanding the evolution of the party system but it is not
clear that these terms are particularly useful for classifying individual parties (Koole 1996).
Moreover, these approaches focus on party structure, whereas this thesis is concerned with
individual behaviour. It may well be the case that party structure can explain why some parties
are more able than others to capitalise on the crisis but the question motivating this chapter is
which parties voters have turned to in response to the crisis. Given that party structure is not
highly visible to most voters, this is unlikely to be a motivating factor for them. As a result, this
chapter will instead focus on parties’ spatial location, which is also more congruent with the
hypotheses introduced in the previous section. There is evidence that voters do understand
the terms ‘left’ and ‘right’ and can accurately locate a party on the left–right spectrum (Busch
2016). It is thus reasonable to hypothesise that voters might treat parties differently according
to their left–right location when responding to the crisis. There is also evidence that party
positions tend to remain stable over time (Dalton and McAllister 2015), and that this continued
to be the case during the Great Recession (Dalton 2016).
This thesis focuses on those parties for which the EES surveys include party support questions. The set of these parties differs somewhat from wave to wave, as the researchers have
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CHAPTER 5. EXTREME AND EUROSCEPTIC PARTIES
tried to include whichever parties were deemed to be most important at the time. The number
of parties in a particular wave varies from 147 to 195. In total, there are 274 unique parties
included for the relevant countries. In order to test this chapter’s hypotheses, it was necessary
to determine whether each of these parties was a left-wing or right-wing party, whether it was
an extreme or moderate party and whether it was a pro- or anti-European integration party.
Leftist parties are reasonably straightforward to identify, since the EES surveys ask respondents where they would place each party on the left–right political spectrum. Other datasets
also include left–right placement data, such as the Manifesto Project, the Comparative Study
of Electoral Systems and the Chapel Hill Expert Survey. Those parties which are generally
agreed to be on the left by survey respondents or with leftist manifestos could be considered
leftist parties. Another possibility is to use a qualitative descriptor of the party’s family, information which is also included in the CHES dataset. Parties which are classified as socialist
or communist parties would then be considered leftist parties.
It is less straightforward to identify extreme parties, as there are different kinds of parties
that could be described as extreme. For example, extreme parties might be identified with
anti-system parties, which oppose the existing political order. This can be problematic when it
comes to regionalist parties who seek greater autonomy or even independence for a particular
region. These parties are common in Europe and are sometimes seen as outsider parties and,
as such, even subject to the cordon sanitaire, whereby mainstream parties informally agree to
minimise cooperation with them or exclude them from any coalition (McDonnell and Newell
2011, 444). On the other hand, within the affected regions, these parties often occupy a
mainstream position, making it inappropriate to categorise then as extreme. For example, the
Scottish National Party has become the dominant party in Scotland, managing to win majority government in 2011 in a proportionally elected parliament (Johns, Mitchell and Carman
2013).3 The party does however argue for Scottish independence from the United Kingdom,
a position that could well be seen as extreme in other parts of the UK, yet the party is clearly
mainstream within Scotland. This illustrates the difficulty of classifying parties as centrist or
extreme by using qualitative family descriptors.
Another way to identify extreme parties could be to consider one of the same measures of
left–right position discussed above. Those parties which are held to be far to the left or far to
the right could be classified as extreme and the others as moderate. This too is not without
problems, as there are some parties where EES respondents do not agree on a position. The
3
They have since lost that majority at the 2016 Scottish Parliament election but they remain the dominant
party, having won 63 seats, more than twice the 31 seats won by the next biggest party (BBC News 2016b).
5.2. CLASSIFYING PARTIES
121
British National Party (BNP), for example, has been described as an extreme right party (Ford
and Goodwin 2010, 1), owing principally to its positions on race and immigration (5). The respondents in the EES survey do not uniformly agree with this classification, however. In 2009,
the only wave in which respondents were asked about the BNP, 30.4 percent of respondents
assigned them a left–right position of ten, indicating a far-right position but 21.6 percent assigned a position of zero, indicating a far-left position. Some 16.3 percent of respondents
assigned no position and none of the remaining possibilities attracted more than 5.5 percent
of the response. Although the party’s positions on certain issues is right-wing, they also work
hard to win support from traditional Labour voters (5). This could be behind the pattern of
responses here, with some voters indicating that they would consider the party to be on the
left and that they find it to be extreme, even though the party is certainly not a communist
party, the usual meaning of far left.
The problem of inconsistent party placement is likely to affect all populist parties, whose
positions on various issues are not necessarily motivated by a coherent, or at least conventional, ideology. One way of addressing this issue could be to measure the mean distance
from the centre reported by respondents, rather than aggregating the reported positions and
then measuring the distance from the centre. This means that a voter reporting that a party is
far left and one reporting that it is far right are considered to be in agreement that the party
is extreme. This is an improvement but it still leads to some surprising classifications, such
as the centre-right New Democracy party in Greece being classified as extreme—presumably
as a reaction to the austerity measures they introduced when they were in government. An
alternative to using the EES measures of left–right position is to use expert surveys of party
positions. The CHES dataset includes a similar measure of general left–right position as well as
separate measures of position on the left–right economic and authoritarian–libertarian scales.
Parties could be classified as extreme according to their positions on any of these scales. Being
an expert survey, the CHES dataset has highly reliable measures of party position and so does
not suffer the same problems described above. In any event, Dalton, Farrell and McAllister
(2011, 121) have shown that expert surveys, citizen surveys and manifesto assessments of
party position display a ‘striking consistency’.
Finally, parties need to be classified according to their position regarding European integration. The EES surveys include a question asking voters to assign parties a score from zero
to ten, where zero means unification ‘has already gone too far’ and ten means it ‘should be
pushed further’. This measure would be ideal except for the fact that there were problems
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CHAPTER 5. EXTREME AND EUROSCEPTIC PARTIES
with this particular variable in the 2014 wave of the survey, and as a result, this measure is
simply unavailable in that wave at the time of writing of this thesis. Most of the parties asked
about in the 2014 wave were also asked about in either or both of the earlier waves, and the
data from those first two waves shows that party positions tended not to change by very much
if anything between 2004 and 2009. By assuming that this was also true between 2009 and
2014, the missing data could be imputed for about three quarters of the parties in the 2014
wave, although this solution is not highly satisfactory, as some of the party position data is ten
years out of date at that point. The CHES dataset also includes a question asking experts to
assign parties a score indicating the party’s orientation towards European integration, as well
as a question asking the relative salience of that position in the party’s public image. This is
useful because it makes it possible to distinguish between parties that are overtly Europhile
or Eurosceptic and those parties who may hold similar positions but are less strongly associated with them in the public mind. Unlike the leftism and extremism measures, there is no
appropriate family classification that could be used for this, as even parties defined by their
Euroscepticism are coded into other families. For example, the United Kingdom Independence
Party is coded as belonging to the conservative family.
The method this chapter uses to classify parties is to use the party position measures from
the CHES dataset. The CHES project surveys experts regarding the parties in their countries.
Each party is positioned on a variety of scales, such as left to right, authoritarian to libertarian
and pro- to anti-integration.4 The experts use their own professional judgement to assign these
positions rather than specified policy items but the mean scores assigned by each expert are a
reliable measure of a party’s position. This approach has several benefits. There are a number
of relevant scales, so party positions on a particular issue can be identified more precisely
than with the other approaches. Because these are scales, rather than nominal measures, this
method also makes it possible to compare party positions, unlike the party family approach.
Finally, the CHES surveys include the relevant questions in each of its waves, unlike the EES
data, which lacks a key question in the 2014 wave.5
It must be noted that the CHES data is not a perfect fit either. There have been five waves of
the survey so far, taking place in 1999, 2002, 2006, 2010 and 2014, which do not correspond
precisely with the three relevant EES survey waves, which took place in 2004, 2009 and 2014.
4
Most of these items are measured on an eleven-point scale ranging from 0 to 10. The support for further
integration item is measured on a five-point scale, which has been recoded to range from −3 to +3 for this analysis.
5
Although the party unification position questions were intended to be included in the 2014 wave, this has
not been released owing to ‘an error in the questionnaire development’ (Popa et al. 2015, 5). The project intends
to address this omission with a follow-up survey but at the time of writing this data is not available.
5.2. CLASSIFYING PARTIES
123
Table 5.1: Variation in party position over time
Fixed effect
Left–right position
Coeff.
SE
p
Intercept
Year 2006
Year 2010
Year 2014
5.206
0.028
0.030
0.094
(0.149)
(0.055)
(0.066)
(0.073)
Var.
SD
Var.
SD
4.580
0.143
0.410
0.542
(2.140)
(0.378)
(0.640)
(0.736)
2.643
0.208
0.421
0.514
(1.626)
(0.457)
(0.649)
(0.717)
Party random effect
Intercept
Year 2006
Year 2010
Year 2014
< 0.001
0.612
0.649
0.200
Integration position
Coeff.
SE
p
0.996
−0.109
−0.071
−0.094
(0.115)
(0.051)
(0.061)
(0.067)
< 0.001
0.034
0.249
0.162
Multilevel model predicting expert assessment of party position for each year that the CHES
survey was conducted. Sample size is 7661 responses for left–right position and 7878 responses for European integration position for 226 parties in both cases. Pseudo R2 is 0.785
for left–right position and 0.772 for integration position. Source: CHES
A model was constructed to estimate the amount of variation in two of the key measures among
the parties included in the CHES data. Table 5.1 summarises the results. The low magnitude of
the fixed effects indicates no evidence for a shift affecting all parties, which is to be expected.
The random effects are more interesting, as these show the degree to which individual parties
changed position over time. The variance of the intercepts associated with the year increases
over time, which is consistent with a random walk. The magnitude of these variances is also
quite low, suggesting that parties do tend to change position over time but somewhat slowly.
Since party positions appear to change slowly over time, the misalignment between the EES
and CHES survey waves should not pose exceptional difficulties. This misalignment has been
addressed by interpolating the party position at the required time.6 Luxembourg, Cyprus
and Malta were not included in the CHES dataset so these countries were excluded from this
chapter. The resulting data set includes 251 unique parties, or 464 total parties,7 within 22
countries.
Using another dataset to measure objective party position represents a change from the
practice used in Chapter 3, where each individual’s subjective measures were preferred. Apart
from the issues discussed above, there is a further reason for this difference. In that chapter, it
was argued that an individual’s support for a particular party ought to decrease as the distance
6
Not every party for which party support data exists in the EES surveys has corresponding data in the CHES
surveys. This affects approximately one quarter of the EES parties, but these are mostly parties of little practical
importance, such as parties that only existed for a short time or which had a very low vote share. These parties
have been excluded from analysis in this chapter.
7
As in previous chapters, parties in different years are treated as different entities because some countries have
highly fluid party systems.
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CHAPTER 5. EXTREME AND EUROSCEPTIC PARTIES
Figure 5.1: Relationship between party support and position
European integration
left–right
mean party preference
3
2
1
0
-1
-2
-3
-2
0
2
0
party position
2
4
6
8
10
Scatter plots showing the relationship between the mean relative support for a party and that
party’s position on European integration, from −3 (anti-integration) to +3 (pro-integration),
as well as its position on the left–right spectrum, from 0 (left) to 10 (right). The best linear
fit is shown for the European integration plot. The left–right plot shows a relationship that is
clearly non-linear, so it is shown with the best quadratic fit. Both curves are shown with their
95% confidence intervals. Source: EES & CHES
increases between the party’s position and their own on the left–right scale, which was found
to be the case. In this spatial model of party support, it makes sense to use that individual’s
subjective measure of the party’s position, since the measure of their own party position is
necessarily subjective and this means the two positions can be meaningfully compared. Although the spatial model remains the underlying model of party support used in this chapter,
and the subjective party distance variable is therefore still included, the focus here is on the
parties themselves. This is why, when classifying the parties, the individual’s perception of the
party is less important than its objective position.
5.3
How party position relates to voter support
Before building an individual-level model, it is necessary to examine the relationship between
aggregate party support and the new party position variables introduced in this chapter. The
5.3. HOW PARTY POSITION RELATES TO VOTER SUPPORT
125
mean relative party support8 was estimated for each party in the study, and this functions
as a measure of that party’s support relative to the other parties in the same country. For
this purpose, the data from all three survey years is combined but each party is treated as a
separate entity in all three years since the party position variables are time-dependent as well.
Figure 5.1 shows the relationship between mean party support and the party’s position on
European integration, in the first panel, and its left–right position, in the second panel. The
former ranges from −3 for strongly anti-integration parties, often referred to as ‘Eurosceptic’
parties, to +3 for strongly pro-integration, or ‘Europhile’, parties and the latter ranges from 0
for far-left parties to 10 for far-right parties.
The scatter plot in Figure 5.1 shows that pro-integration parties are more popular than
anti-integration parties and this relationship is approximately linear, as can be seen from the
linear fit line also shown. Similarly, centrist parties are more popular than far left and far
right parties. Curiously the parties that are in the very centre of the spectrum appear to be
rather less popular than those on the centre-left and the centre-right. This plot is shown with
a quadratic fit line, which is a good fit but does not account for the depressed support among
centrist parties. The quadratic fit is preferred to one attempting to account for this anomaly
for two reasons. First, there is a risk of overfitting the data. The quadratic fit is already a good
fit and the residuals from this model are not significantly different from a normal distribution.
Second, it has been shown in previous chapters that incumbent parties are more popular than
opposition parties and these parties tend to occupy the centre-left and centre-right. When
controlling for incumbency, the centrist parties are no longer outliers.
It is also clear from these plots that party position on either of these scales only explains a
small proportion of the variance in mean party support. The regression R2 is 0.18 for the party’s
integration position and 0.14 for its left–right position. Although these are small numbers, it
would be surprising if party position alone explained a large proportion of the variance in
party support. It is remarkable that even eighteen percent of this variance can be explained
by a party’s position on European integration alone, although it must be stressed that this
proportion is likely to be less when controlling for other variables. This is particularly likely
for the incumbency variable, as governing parties are generally more pro-integration than
opposition parties. The mean position on European integration among the cabinet parties in
the dataset is 1.85 [1.69, 2.02], and that for opposition parties is 0.46 [0.26, 0.67].
8
Party support is a measure of how likely an individual is to support a particular party on an eleven-point scale.
Relative party support is party support centred around each individual’s mean support level for the parties in their
country. These measures are explained in detail in Chapter 2. Mean relative party support is thus an aggregate
measure of a party’s support compared to other parties in the same country.
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CHAPTER 5. EXTREME AND EUROSCEPTIC PARTIES
Figure 5.2: Relationship between integration position and salience
1.0
salience
0.8
0.6
0.4
0.2
-3
-2
-1
0
position
1
2
3
Scatter plot showing the relationship between a party’s position on European integration, from
−3 (anti-integration) to +3 (pro-integration), and the salience of the integration issue in that
party’s public image, from 0 (low) to 1 (high). This is shown along with the best quadratic fit,
which is clearly a better fit than a linear one. The shaded area represents the 95% confidence
interval around the curve. Source: CHES
The plots for the other party position variables are not shown here because they are similar
to these. The social and economic dimensions follow similar distributions to the left–right
spectrum. The integration salience measure is almost unrelated to party support, although
parties with high salience are slightly more popular, even when controlling for the party’s
position on that issue. It might be expected that salience would affect support differently
for parties with different positions on integration but surprisingly the interaction between
integration position and salience is not significant. There is no significant quadratic interaction
either. This is likely because these two variables are actually related. As Figure 5.2 shows,
salience is higher for parties that are more strongly pro- or anti-integration than it is for parties
with more moderate positions. This makes sense because it is difficult to see how a party could
make a moderate position on this issue an important part of its public image. Although the
correlation is not high (R2 = 0.23), this may explain why the expected interactions are not
observable. Because of this, the rest of the analysis will ignore the salience variable, focusing
on integration position only.
5.4. THE CHANGING FORTUNES OF PRO-EUROPEAN INTEGRATION PARTIES
127
Table 5.2: Regression model predicting general left–right position
Predictor
Intercept
Economic position
Social position
Coeff.
−0.197
0.683
0.412
SE
(0.117)
(0.021)
(0.019)
p
0.094
< 0.001
< 0.001
This model predicts a party’s general left–right position from its position on the economic and
social dimensions. Sample size is 434 parties. Adjusted R2 is 0.847. Source: CHES
The relationship between the three left–right position variables was also explored. These
variables are the party’s positions on the general left–right spectrum as well as an economic
spectrum and a social spectrum. These latter variables allow parties to be positioned in a
two-dimensional political space, rather than along a single line, according to the theory that
positions on economic and social issues are often orthogonal (for example, see Lester 1994;
Swedlow 2008). The correlation between social and economic position is r = 0.38, which
suggests that these positions are not quite orthogonal but it is low enough that it may be
worth examining the two dimensions separately. It should be expected that a party’s general
left–right position can be predicted from its positions on the social and economic dimensions
and this is in fact the case. As Table 5.2 shows, position on the social and economic dimensions
explains 85 percent of the variation in general left–right position. It is interesting to note that
economic position contributes more strongly to a party’s general position than social position
does. This model shows that the economic and social position scores are a meaningful decomposition of a party’s left–right position, so party support models will be constructed using both
the general left–right positions and the two-dimensional positions.
5.4
The changing fortunes of pro-European integration parties
The first hypothesis is that support for pro-integration parties fell after the Great Recession.
In order to test this, Model 5A was constructed. It is based on Model 3D, which measures
the economic vote using a three-way distinction between prime ministers’ parties, other cabinet parties and opposition parties. That model was extended by including as a predictor the
variable measuring each party’s position on European integration. As with the other terms
in the model, this predictor has been centred around the grand mean. In the original model,
economic voting is measured by the interaction between an individual’s prospective economic
assessment and a party’s incumbency status. This is because economic voting theory posits
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CHAPTER 5. EXTREME AND EUROSCEPTIC PARTIES
that economic conditions affect government and opposition party support differently. A further term measuring the three-way interaction between economic assessment, incumbency
and the survey year made it possible to discern how the strength of the economic voting effect
has changed over time.
The new model includes a similar interaction between economic assessment and party position on European integration, so that it can be tested whether support for pro-integration
parties is higher among optimistic or pessimistic voters. In other words, this inclusion makes
it possible to determine whether economic voting affected not just incumbent parties but also
pro-integration parties. Furthermore, a three-way integration term between economic assessment, European integration position and survey year has been included so as to be able to
determine whether these effects changed over time.9 The incumbency terms mentioned are
still included so as to control for the standard economic voting effect, which has already been
shown to have existed.
In addition to the random effects terms included in the original model, corresponding
random effects terms were considered for each of the new fixed effects. Since the European
integration position variable was measured at the party level, it would only be meaningful to
include these random effects at the country level. It was previously shown that the countrylevel variance in this data is almost zero, which is why the basic model does not include a
random intercept for the country level, so it is not surprising that the variance of all of these
random effects terms was also very small and in most cases their inclusion did not improve the
model fit significantly. A random effect for the integration position variable alone was included
because, although the measured variance was very low, it was large enough to improve the
model fit significantly and does not hinder convergence.
As in previous chapters, post-estimation simulation has been used to derive key predictions along with the corresponding uncertainty estimates from the model coefficients and it
is these that will form the basis of the following discussion. The table of coefficients can be
found in Appendix B. Figure 5.3 shows how support changed over time for parties with different positions towards European integration. The points plotted correspond to the predicted
support of an individual with a neutral prospective economic assessment towards each type
of party in each survey year. According to this plot, neutral voters were more likely to support pro-integration parties than anti-integration parties across all three years, although the
9
Although not always mentioned explicitly, the models discussed in this chapter also include the additional
terms implied by any interactions mentioned. For example, where a three-way interaction is mentioned, all of the
pairwise two-way interactions as well as the single factor terms are also included.
5.4. THE CHANGING FORTUNES OF PRO-EUROPEAN INTEGRATION PARTIES
129
Figure 5.3: Party support by position on European integration
predicted preference
4.4
4.2
party position
pro-integration
4.0
neutral
anti-integration
3.8
3.6
3.4
2004
2009
year
2014
Predicted support in each survey year of a voter holding a neutral prospective economic assessment for a strongly pro-integration party, a neutral party and a strongly anti-integration
party. These correspond to measured European integration positions of +3, 0 and −3 respectively. The modelled relationship between a party’s integration position and a voter’s support
for that party in a particular year is linear, so the predictions for moderate parties lie between
those shown here. Source: EES, ParlGov & CHES
magnitude of this difference decreased dramatically over time. In 2004, a neutral voter has
a predicted support of 4.54 points (SE = 0.10, p < 0.001) for a pro-integration party, 3.99
points (SE = 0.07, p < 0.001) for a neutral party and 3.44 points (SE = 0.14, p < 0.001)
for an anti-integration party. This means that, compared to an anti-integration position, a
pro-integration position was associated with an extra 1.10 points (SE = 0.20, p < 0.001) of
support. In 2009, this difference had declined to 0.76 points (SE = 0.20, p < 0.001) and by
2014, the difference was no longer statistically significant (0.15 points, SE = 0.19, p = 0.41).
This suggests that voters were quite supportive of a pro-European integration position before
the beginning of the crisis, slightly less supportive after the crisis had begun and indifferent to
such a position after the end of the crisis.
So far, this analysis has only discussed voters with a neutral assessment of the course of
their country’s economy. Figure 5.4 compares the predicted difference in support for prointegration and anti-integration parties for optimistic, neutral and pessimistic voters. A positive value in a given year, indicates that such a voter is expected to prefer a pro-integration
130
CHAPTER 5. EXTREME AND EUROSCEPTIC PARTIES
Figure 5.4: Relative support for pro-integration parties
predicted difference
1.0
group
0.5
optimistic
neutral
pessimistic
0.0
-0.5
2004
2009
year
2014
Difference in predicted support for a strongly pro-integration and anti-integration party in
each year among voters holding different prospective economic assessments. This difference
represents the relative support for a pro-integration party (measured position of +3) over an
anti-integration party (−3). The economic assessments shown are those for highly optimistic
voters (measured assessment of +2), neutral voters (0) and highly pessimistic voters (−2).
Source: EES, ParlGov & CHES
party and a negative value indicates that the voter would prefer an anti-integration party. In
2004, pro-integration parties were preferred by all three voter groups. The pessimistic group
had a relative support towards these parties of +0.89 points (SE = 0.24, p < 0.001) and the
optimistic group had a relative support of +1.31 points (SE = 0.20, p < 0.001). The difference between the groups was not significant however (0.42 points, SE = 0.28, p = 0.14). By
2009, it appears that this relative support had fallen in each group by an approximately equal
amount but this change is not significant either (0.34 points for the neutral group, SE = 0.26,
p = 0.18). By 2014, the three groups had become less similar to each either. Optimistic voters
still had a strong relative support towards pro-integration parties, of +0.89 points (SE = 0.23,
p < 0.001). This is a mere 0.04 points (SE = 0.31, p = 0.87) less than in 2009. Neutral voters
had become almost indifferent to a party’s European integration position, with a relative support of +0.15 points (SE = 0.19, p = 0.41), a 0.60 point (SE = 0.23, p = 0.01) fall from
2009. Pessimistic voters in 2014 were the only group to prefer anti-integration parties, with a
5.5. LEFT–RIGHT POSITION: A SHIFT TO THE EXTREMES
131
relative support of −0.59 points (SE = 0.23, p < 0.001). This is a huge 1.16 point (SE = 0.30,
p < 0.001) fall from 2009.
In short, parties arguing for closer European integration were at an advantage in 2004,
before the beginning of the recession and this was probably independent of a voter’s economic assessment. In 2009, during the recession, this was still the case, except that the size of
that advantage had declined slightly. By 2014, well after the recession, voters with different
economic assessments had started to behave differently in this respect. Optimistic voters continued to prefer the pro-integration parties but pessimistic voters now preferred those parties
arguing for weaker European integration, with voters of a neutral assessment having no clear
preference. This seems to be evidence in support of the hypothesis that the recession provoked
a shift away from pro-integration parties. It is worth noting, however, that the bulk of the shift
and the stratification according to economic assessment did not take place until well after the
recession.
5.5
Left–right position: a shift to the extremes
At the beginning of this chapter, it was hypothesised that voters shifted their support away
from leftist parties and towards those on the right during the recession. This is not necessarily
expected to be a long-term shift, as earlier work has found that such shifts tend to be shortlived (Lindvall 2014). It was also hypothesised that it was extreme parties that benefited from
the recession. These hypotheses can be tested by modelling the impact of a party’s left–right
position on voter support for that party. Model 5B is based on, once again, Model 3D, which
has this time been extended to measure the impact of a party’s general left–right position on
party support. It was shown earlier in this chapter that the relationship between mean support
for a party and that party’s left–right position is approximately quadratic, with voters generally preferring centrist parties over extreme parties. The new model therefore represents this
relationship quadratically. Both the linear and quadratic terms are interacted with economic
assessment and time, allowing this relationship to be compared between the different groups
of voters in each year, as with previous models. No further random effects terms have been
included in this model because the variance of these terms is extremely low.
This model was estimated and used to predict the support in each year of optimistic, pessimistic and neutral voters for various party left–right positions. These predictions are summarised visually by Figure 5.5. This figure shows that, in 2004, all groups of voters generally
preferred centrist parties over extremist parties, leading to an inverted U-shaped curve for
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CHAPTER 5. EXTREME AND EUROSCEPTIC PARTIES
Figure 5.5: Party support by general left–right position
2004
2009
2014
5
optimistic
4
2
5
neutral
predicted preference
3
4
3
2
5
pessimistic
4
3
2
0
2
4
6
8 10 0
2
4
6
8 10 0
party position
2
4
6
8 10
Predicted party support by party’s general left–right position in each year among voters holding
different prospective economic assessments. These are shown along with their 95% predictive
intervals. Party position ranges from 0 (far left) to 10 (far right). The economic assessments
shown are those for highly optimistic voters (measured assessment of +2), neutral voters (0)
and highly pessimistic voters (−2). Source: EES, ParlGov & CHES
each. It can also be seen that optimistic voters leant slightly to the right and pessimistic voters
leant slightly to the left, with neutral voters leaning in neither direction. This is an interesting
pattern, because it suggests that before the recession, voters had a small tendency to support
the right when they believed that the economy was likely to do well and the left when they
believed otherwise. By 2009, this pattern had changed, with all three groups preferring the
centre. This suggests that, rather than enhancing the tendency for optimists to support the
right and pessimists the left, the recession in fact suppressed it. The year 2014 is particularly
interesting because it shows a rather different pattern altogether. Optimists in 2014 still preferred centrist parties overall but the flatter shape of the curve indicates an increased openness
to more extreme parties. The curve for neutral voters appears completely flat, suggesting that
this group of voters was indifferent between extreme and centrist parties. Finally, pessimistic
5.5. LEFT–RIGHT POSITION: A SHIFT TO THE EXTREMES
133
Table 5.3: Left–right and extreme tendency by year and voter group
Group
Year
Left–right tendency
Extreme tendency
Optimistic
2004
2009
2014
+1.45 [+0.08, +2.82]
−0.54 [−1.72, +0.65]
−0.08 [−1.29, +1.13]
−0.102 [−0.149, −0.055]
−0.079 [−0.120, −0.037]
−0.036 [−0.078, +0.006]
Neutral
2004
2009
2014
−0.23 [−1.29, +0.84]
−0.60 [−1.53, +0.34]
−0.30 [−1.24, +0.65]
−0.076 [−0.113, −0.039]
−0.069 [−0.101, −0.036]
+0.003 [−0.030, +0.035]
Pessimistic
2004
2009
2014
−1.90 [−3.22, −0.59]
−0.65 [−1.82, +0.51]
−0.52 [−1.70, +0.67]
−0.050 [−0.095, −0.005]
−0.059 [−0.098, −0.019]
+0.041 [+0.001, +0.081]
Left–right tendency is the difference between a group’s cumulative predicted support for rightwing and left-wing positions, positive for a right-wing tendency and negative for a left-wing
tendency. Extreme tendency is the curvature of the quadratic function describing the relationship between party position and predicted support for that party. A positive curvature indicates
a preference for more extreme parties and a negative curvature a preference against extreme
parties. These figures are shown with their 95% predictive intervals. The three groups correspond to highly optimistic voters (measured assessment of +2), neutral voters (0) and highly
pessimistic voters (−2). Source: EES, ParlGov & CHES
voters in 2014 appear to be more supportive of extreme parties than centrist parties, with a
U-shaped curve that is no longer inverted.
In order to determine whether these apparent differences are statistically significant, it is
helpful to find a relevant quantity that can be measured. In order to determine whether a
particular group had an overall left-wing or right-wing tendency, the total area under the left
half of the curve was compared to that under the right half.10 This difference is positive in
the case of a right-wing tendency and negative in the case of a left-wing tendency. Similarly,
support for extreme parties over centrist parties can be described by the curvature of the
function.11 A positive curvature indicates that support increases towards the extremes and
a negative curvature indicates that support decreases towards the extremes. A curvature of
zero implies that the curve is in fact a line. This curvature will be referred to as the extreme
tendency.
The left–right and extreme tendencies for optimistic, pessimistic and neutral voters in each
year are given in Table 5.3 along with their predictive intervals. These figures confirm the
10
A more precise description of this quantity follows: the party support curve for a particular year and group
can be described by a quadratic expression of the form y = ax 2 + bx + c, where x represents the party’s left–right
Rb
position and y support for that party. Let the function C(a, b) = a ax 2 + bx + c d x be the cumulative support
between a and b. Then ∆ = C(5, 10) − C(0, 5) = (875a/3 + 75b/2 + 5c) − (125a/3 + 25b/2 + 5c) = 250a + 25b
describes the difference in support between all right-wing and all left-wing parties. This quantity was computed
along with its predictive interval to produce the figures described here.
11
The curvature of a function is described by its second derivative. In this case, d 2 y/d x 2 = 2a. Once again,
this was computed with its predictive interval to produce the figures given here.
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CHAPTER 5. EXTREME AND EUROSCEPTIC PARTIES
visual findings described above. In 2004, optimistic voters had a rightward tendency and
pessimistic voters a leftward tendency. Neutral voters in 2004 and all voters in the other years
had no significant left–right tendency. In 2004 and 2009, all groups of voters had support
functions with negative curvature, that is, they tended to prefer parties with more centrist
positions than parties at the extremes. By 2014, this was no longer the case. Pessimistic voters
had a positive support curvature, meaning that extreme parties were supported more strongly
than centrist parties. Neutral and optimistic voters had support curvatures not significantly
from zero and the point estimate for neutral voters was very close to zero. The point estimate
for optimistic voters was somewhat less than zero, hence the visibly curved shape in Figure 5.5,
but a flat line or a curve in the other direction are both plausible. On this evidence, it is likely
that both groups of voters were almost indifferent to a party’s left–right position in that year.
5.6
Beyond left and right: social and economic dimensions
Political position is sometimes modelled in two dimensions rather than the traditional continuum. These dimensions are usually a left–right economic dimension and a libertarian–
authoritarian social dimension. The argument for this more complex approach is that the two
dimensions are in practice orthogonal, so it is not unusual to find that parties that are, for
example, right-wing on economic issues still have a diversity of positions on social issues. This
being the case, the hypothesis that voters moved to leftist parties as a result of the recession
could be more accurately tested using a two-dimensional model, since movement along each
dimension can be analysed separately. In two-dimensional terms, this hypothesis posits that
voters would have moved left along the economic dimension, with no motion along the social
dimension. The alternative hypothesis that voters were inclined to move to the extremes also
applies particularly to the economic dimension. There is no expectation of movement along
the social dimension. This is because in a harsh economic environment voters may be more
open to more radical economic policies but there is no obvious reason why this should lead to
a change in their other views.
The one-dimensional model discussed in the previous section has been modified to produce
a two-dimensional model, designated Model 5C. As discussed earlier in the chapter, party
position has been measured along both the economic and social dimensions, as well as a
general left–right dimension. The general dimension used in the previous model has been
substituted with the economic and social dimensions, each of which is modelled quadratically
like the original. The two dimensions are not modelled as interacting because it as assumed
5.6. BEYOND LEFT AND RIGHT: SOCIAL AND ECONOMIC DIMENSIONS
135
Figure 5.6: Party support by economic left–right position
2009
2014
optimistic
5.5
5.0
4.5
4.0
3.5
3.0
2.5
neutral
predicted preference
2004
5.5
5.0
4.5
4.0
3.5
3.0
2.5
pessimistic
5.5
5.0
4.5
4.0
3.5
3.0
2.5
0
2
4
6
8 10 0
2
4
6
8 10 0
party position
2
4
6
8 10
Predicted party support by party’s economic left–right position in each year among voters
holding different prospective economic assessments. These are shown along with their 95%
predictive intervals. Party position ranges from 0 (far left) to 10 (far right). The economic
assessments shown are those for highly optimistic voters (measured assessment of +2), neutral
voters (0) and highly pessimistic voters (−2). Source: EES, ParlGov & CHES
that motion along one dimension is completely independent of motion along the other. As
before, no further random effects terms have been included owing to the very low variance of
those terms.
Figure 5.6 shows predicted support according to year and economic assessment towards
a party at different positions along the economic dimension. In making these predictions, the
party’s social position has been held to be in the centre. This only affects the vertical offset
of the curve, not its shape, as the model does not allow the two dimensions to interact. Once
again, it appears that optimistic voters in 2004 had a tendency to prefer right-wing parties
and pessimistic voters left-wing voters, although these tendencies appear stronger than in
the unidimensional model. Neutral voters still appear to prefer the centre. Similarly, the
magnitude of these tendencies has declined by 2009 but unlike the unidimensional model,
the optimistic and pessimistic voters still have a visibly right-wing and left-wing tendency
136
CHAPTER 5. EXTREME AND EUROSCEPTIC PARTIES
Table 5.4: Directional and extreme tendency along economic dimension
Group
Year
Directional tendency
Extreme tendency
Optimistic
2004
2009
2014
+3.09 [+1.50, +4.68]
+1.06 [−0.40, +2.52]
+0.72 [−0.64, +2.08]
−0.042 [−0.093, +0.009]
−0.057 [−0.102, −0.013]
−0.015 [−0.058, +0.027]
Neutral
2004
2009
2014
+0.07 [−1.19, +1.32]
−0.52 [−1.69, +0.64]
−0.54 [−1.59, +0.52]
−0.048 [−0.089, −0.007]
−0.064 [−0.099, −0.028]
+0.005 [−0.029, +0.038]
Pessimistic
2004
2009
2014
−2.96 [−4.47, −1.44]
−2.10 [−3.50, −0.70]
−1.80 [−3.10, −0.50]
−0.054 [−0.103, −0.005]
−0.070 [−0.112, −0.027]
+0.025 [−0.015, +0.065]
Similar to Table 5.3, except that the dimension analysed is the economic left–right spectrum
rather than the general left–right spectrum. A positive directional tendency here indicates a
preference for the economic right and negative a preference for the economic left. Source:
EES, ParlGov & CHES
respectively. Each group in both years prefers parties near the vertex over parties further away
but given the extreme location of the vertices for optimistic and pessimistic voters in 2004, this
may not be a particularly useful measure for those groups. In 2014, the directional tendency
of each groups appears to be maintained, with optimistic voters preferring right-wing position
and pessimistic voters left-wing positions, with neutral voters still close to indifferent. Each of
the 2014 curves has flattened somewhat compared to the previous years. This suggests that
voters in 2014 were more open to extreme positions and less open to centrist positions while
retaining the same directional preference as voters in previous years.
Numeric measures of each group’s directional and extreme tendencies are given in Table 5.4
along with their predictive intervals. These mostly confirm the predictions made above. Optimistic voters do have a rightward directional tendency in 2004, with rightward point estimates in the other years as well, although the latter are not significant. Similarly, pessimistic
voters have a leftward directional tendency and this is significant in every year. Neutral voters
have no significant directional tendency in any year. The extreme tendency, described by the
quadratic curvature, of optimistic voters is negative but not quite significant in 2004. In the
context of the strong rightward tendency for this group, this figure is not especially meaningful. In 2009, however, this figure is significantly negative suggesting that optimistic voters in
this year generally preferred centrist parties over more extreme parties. By 2014, this figure
was no longer significant with a point estimate close to zero, suggesting that these voters were
less averse to extreme economic positions than they had previously been. Pessimistic voters
had a similar trajectory, with their extreme tendency significantly negative in 2004 and 2009
5.6. BEYOND LEFT AND RIGHT: SOCIAL AND ECONOMIC DIMENSIONS
137
but with a positive albeit no longer significant point estimate in 2014. The extreme tendency
for neutral voters was not significant in any year, although even in this group the point estimate in 2014 was significantly greater than in 2009 (difference is 0.068 points, SE = 0.025,
p < 0.01).
In summary, generally speaking, voters who believed the economy was improving generally supported economically right-wing parties and those who believed it was getting worse
generally supported economically left-wing parties, even when controlling for the spatial distance between the party and the voter. Not much changed between 2004 and 2009, other
than a slight weakening of this tendency. This suggests that the recession sparked no major
immediate shift in economic policy preferences. There was little if any change in this tendency between 2009 and 2014 but there was an increased tendency to prefer more extreme
economic positions over centrists ones. Since this took place in the post-crisis period, it suggests that the political response to the crisis did lead to a shift in policy preferences away from
the economic mainstream towards the extremes.
Turning to the social dimension, the relevant predictions are summarised in Figure 5.7.
For these predictions, the economic position is held in the centre while the social position is
varied. The expectation was that these relationships would not have changed over time but
this does not appear to have been the case. Optimistic voters do have similar looking curves
in each year, with a directional tendency towards libertarian positions in all years, though
somewhat less so in 2014. Pessimistic voters appear to have a strong centrist tendency in
2004, with an evidently negative curvature and a vertex close to the centre. By 2009, the
curvature has flattened considerably, though the vertex is still close to the centre. In 2014,
the curve is still quite flat but there is now a clear tendency to prefer authoritarian positions.
Neutral voters are quite similar, starting with a clear negative curvature and a preference
for libertarian positions. In 2009, the curve starts to flatten and by 2014 the curve remains
relatively flat but the vertex has moved to the centre. In other words, it appears that optimistic
voters in 2004 were libertarian-leaning and pessimistic voters centrist but by 2014, all groups
had shifted to the right, so that optimists were centrist and pessimists authoritarian-leaning.
Table 5.5 presents the directional tendency and curvature of the relationships between
party preference and social political position in the same manner as previously. Once again,
these confirm the patterns shown in the visual analysis. In 2004, optimistic voters had a clearly
libertarian tendency, neutral voters a not quite significant tendency in the same direction and
pessimistic voters an approximately centrist tendency. The point estimates of directional tend-
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CHAPTER 5. EXTREME AND EUROSCEPTIC PARTIES
Figure 5.7: Party preference by social libertarian–authoritarian position
2004
2009
2014
5.0
optimistic
4.5
4.0
3.0
5.0
4.5
neutral
predicted preference
3.5
4.0
3.5
3.0
5.0
pessimistic
4.5
4.0
3.5
3.0
0
2
4
6
8 10 0
2
4
6
8 10 0
party position
2
4
6
8 10
Predicted party preference by party’s social libertarian–authoritarian position in each year
among voters holding different prospective economic assessments. These are shown along
with their 95% predictive intervals. Party position ranges from 0 (libertarian/post-materialist)
to 10 (traditional/authoritarian). The economic assessments shown are those for highly
optimistic voters (measured assessment of +2), neutral voters (0) and highly pessimistic
voters (−2). Source: EES, ParlGov & CHES
ency have changed slightly between 2004 and 2009 but these changes are not significant. The
largest of these differences, that for optimistic voters, is 0.53 points (SE = 0.92, p = 0.57).
Between 2009 and 2014, on the other hand, the estimates for all three groups has shifted
in the authoritarian direction. In the case of optimistic voters, this change is not significant
(difference is 1.11 points, SE = 0.86, p = 0.20) but it is significant for neutral (1.38 points,
SE = 0.69, p = 0.04) and pessimistic voters (1.66 points, SE = 0.84, p = 0.05). Interestingly, not only were pessimistic voters the most authoritarian group in 2004 but they were
also the group with the greatest shift in that direction between 2009 and 2014. As for the
extreme tendency or curvature, this was not significantly different from zero for optimistic
voters in any year. For neutral voters, this was significantly negative in 2004 but not in 2009
and 2014 and the increase between 2004 and 2009 was not significantly different in any case
(0.022 points, SE = 0.023, p = 0.34). The curvature for negative voters was significant and
5.6. BEYOND LEFT AND RIGHT: SOCIAL AND ECONOMIC DIMENSIONS
139
Table 5.5: Directional and extreme tendency along social dimension
Group
Year
Directional tendency
Extreme tendency
Optimistic
2004
2009
2014
−1.90 [−3.21, −0.59]
−2.42 [−3.65, −1.20]
−1.32 [−2.50, −0.14]
−0.014 [−0.057, +0.028]
−0.013 [−0.051, +0.025]
−0.020 [−0.060, +0.019]
Neutral
2004
2009
2014
−1.08 [−2.12, −0.03]
−1.27 [−2.24, −0.30]
+0.11 [−0.82, +1.04]
−0.036 [−0.070, −0.002]
−0.014 [−0.044, +0.015]
−0.014 [−0.045, +0.017]
Pessimistic
2004
2009
2014
−0.25 [−1.50, +0.99]
−0.12 [−1.28, +1.04]
+1.54 [+0.40, +2.69]
−0.058 [−0.099, −0.017]
−0.016 [−0.052, +0.020]
−0.007 [−0.046, +0.031]
Similar to Tables 5.3 and 5.4, except that the dimension analysed is the social libertarian–
authoritarian spectrum rather than the general left–right spectrum. A positive directional
tendency here indicates a preference for the more traditional or authoritarian parties and
negative a preference for more libertarian or post-materialist parties. Source: EES, ParlGov &
CHES
negative in 2004 but by 2014 this curvature was almost zero. This difference was not quite
significant (0.051 points, SE = 0.029, p = 0.08).
The changes in directional tendency over time are surprising. The fact that there is no
significant change between 2004 and 2009 suggests that there was no immediate reaction to
the crisis in terms of social preferences, which accords with the expectation that the social
dimension would be unaffected by the crisis. On the other hand, the change in directional
tendency between 2009 and 2014 indicates that in the years after the initial recession, there
was a general shift in preference towards traditional or authoritarian positions. Furthermore,
the more pessimistic the voter about economic conditions, the stronger the shift. This is particularly interesting, since pessimistic voters were also the least libertarian in the first place. This
suggests that there is a link between concern for the condition of the economy and embracing
traditional values. As for extreme tendency, given that the changes in curvature between the
years was not significant for any of the groups, if there was any increase in openness towards
extreme positions on the social spectrum, this would have been modest at best. In fact, in
most of the cases the curvature is not significantly different from zero, suggesting that a linear
relationship is possible. In these cases, there is also a clear preference for one or the other end
of the social dimension. This suggests that, unlike for the general and economic dimensions,
the social dimension is not one on which there is a strong tendency to prefer the centre over
the extremes.
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5.7
CHAPTER 5. EXTREME AND EUROSCEPTIC PARTIES
Conclusion
This chapter has tested four hypotheses relating to the changing patterns of support for parties
occupying different political positions during the Great Recession and its aftermath. In particular, levels of support were examined for parties at different positions along several political
dimensions. The first hypothesis concerned party position along the European integration
spectrum. The hypothesis was that pro-integration parties lost support to anti-integration
parties as a result of the recession. It was found that there was such a shift in support but that
it mostly occurred not so much during the recession but in the following years. It was further
found that this change was mediated by an individual’s prospective economic assessment—the
more pessimistic the voter, the more likely that voter was to support anti-integration parties.
This was only true in 2014, voters in previous years having preferred pro-integration parties
irrespective of economic assessment. Taken together, these findings indicate that an increase
in anti-integration sentiment did not arise during the recession but in the post-recession period
and that this increase was economically motivated. This suggests that voters started to feel
that the institutions of the EU were at least partly responsible for either the continuing economic problems or the unpopular austerity policies introduced in response. This would also
explain the division according to economic assessment, as those voters who were optimistic
about the economy at that point were presumably those who believed that austerity polices
would be effective.
The second and third hypotheses concerned support for parties at different positions along
the left–right political spectrum. The hypotheses were that the Great Recession led to a shortterm increase in support for right-wing positions and to an increase in support for extreme
positions respectively. The results supported the hypothesis of increased extreme support but
not the hypothesis of increased right-wing support. It was found rather that voters became
increasingly indifferent to whether a party was on the left or the right immediately following
the recession, whereas voters beforehand had tended to prefer right-wing parties when they
were optimistic about the economy and left-wing parties when pessimistic. Support for more
extreme parties increased both during the recession and during the years following it. These
findings challenge both the luxury goods model of Durr (1993), in which optimists are supposed to prefer the left and pessimists the right, and the idea that the Great Recession pushed
voters to the right (for example, Lindvall 2013; Bartels 2014). This thesis differentiates itself
from those earlier studies by controlling for the economic voting effect, so it is likely that this
5.7. CONCLUSION
141
discrepancy can be explained by left-leaning and right-leaning voters having different attitudes
towards the economy.
The second and third hypotheses were also tested using a two-dimensional model of the
political space rather than the usual spectrum. The results for the economic dimension were
similar to those for the single left–right spectrum. Before the recession, optimism was a predictor of right support and pessimism of left support but this difference closed over time. Most
of the change occurred during the recession rather than in the period following it. Unlike the
single dimension, there was still a discernible difference between optimistic and pessimistic
voters by 2014. There was also an increase in support for more extreme economic positions
and this mostly took place in the aftermath period. It was expected that the social dimension would be unaffected by the recession but this proved not to be the case. There was an
increase in support for parties holding traditional or authoritarian values, particularly in the
period between 2009 and 2014, and this increase was strongest among pessimistic voters. Unlike the economic dimension, voters seemed to be indifferent to the extremeness of a position
on the social spectrum, a linear relationship between position and support being plausible in
most cases.
The final hypothesis was that the other hypothesised effects were strongest among those
voters who believed their national economy was worsening and this was indeed found to be
the case. This is an important finding because this links the other findings with the economy
and is evidence that those results are in fact related to the recession and the timing is not
merely a coincidence.
Taken together, these findings suggest that the changes in party support during the recession itself were quite distinct from those of the following period, during which austerity
measures were widely introduced. By far the most prominent change during the recession
itself was a shift towards the centre on economic issues, regardless of economic perception.
Then, in the period following the recession, support for extreme economic positions increased,
with no strong pattern of preference for whether those positions were on the left or the right.
At the same time, support shifted from parties holding libertarian or post-materialist social
views to those holding traditional or authoritarian ones. Similarly, there was a shift in support from pro-integration to anti-integration parties. As previously mentioned, all of these
effects were strongest among those voters who were pessimistic about the economy. This is
interesting because it suggests that the most striking political effects of the Great Recession
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CHAPTER 5. EXTREME AND EUROSCEPTIC PARTIES
may actually have been induced more by the political response to the recession than by the
recession itself.
This and the two preceding chapters have examined patterns of party preference during
the Great Recession from several different perspectives. In order to gain a more complete
understanding of the political consequences of the recession, the following chapters explore
the same time period using different dependent variables. The next chapter looks at the change
in patterns of turnout during the crisis and whether that too is mediated by an individual’s
prospective economic assessment. Chapter 7 then examines voters’ attitudes towards the EU
and further European unification, so as to establish whether the voter response to austerity
politics was indeed stronger than the response to the recession itself.
Chapter 6
Economic abstention: turnout intention in the face
of economic pessimism
An understanding of what drives voter turnout is an essential part of electoral behaviour analysis. Studying vote choice alone risks missing an important part of the electoral decision process and therefore introducing a bias into the analysis, since voters usually have the option of
not voting at all and the exercise of this option may depend upon some of the same factors that
also influence the party choice of those who do vote. Despite this possibility, voter turnout has
long been neglected in studies of economic voting (Weschle 2014). It is widely acknowledged
that turnout is in long-term decline in most of the mature democracies (Gray and Caul 2000;
Blais 2000, 34–36), so this omission is becoming increasingly problematic. Furthermore, low
turnout potentially has serious implications for democratic government, since certain social
groups may be less likely to vote than others, and if these groups would vote differently from
those who do vote, then this would introduce biases into the electoral process. In fact, there
is some evidence that these biases exist, particularly in the United States (Leighley and Nagler 2014). Recognising the importance of voter turnout in electoral behaviour studies, this
chapter builds upon the economic voting analysis of the previous chapters by testing for the
existence of a hypothesised economic turnout relationship.
This chapter begins by discussing the previous research on the relationship between economic conditions and turnout, which has produced conflicting results. There were mixed results within the earlier literature, with some scholars theorising that harsh economic conditions
would motivate more people to vote, either in order to alleviate the situation (Lipset 1981,
192; Schlozman and Verba 1979, 235–239) or to punish the incumbent government (Kernell
1977), and others proposing the opposite, that such conditions would lead people to become
too preoccupied with day-to-day survival to become politically involved (Wolfinger and Rosenstone 1980, 20–22). There was also a third perspective arguing that in fact no relationship
143
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CHAPTER 6. ECONOMIC ABSTENTION
between economic conditions and voter turnout ought to be expected. The empirical evidence
at that time largely supported either the no effect (Lane 1959, 329–331; Fiorina 1978) or the
withdrawal viewpoints (Brody and Sniderman 1977; Schlozman and Verba 1979, 254; Rosenstone 1982, 23–26). More recent studies have tried to explain these contradictory findings,
either by looking at individual rather than purely aggregate data (Arceneaux 2003; Stevens
2007; Passarelli and Tuorto 2014) or by examining multiple countries comparatively (Radcliff
1992; Martins and Veige 2013). Nonetheless, conclusions remain divided. This chapter contributes to this research in two key ways. First, although there have been a number of recent
individual-level studies and cross-national studies of the economic influences on turnout, very
few of them are both individual-level and cross-national. This chapter examines individuallevel behaviour across nations, which sets it apart methodologically from most of the existing
literature. Second, this analysis focuses on the Great Recession, which as previous chapters
have discussed is an excellent opportunity to examine voter behaviour under exceptionally
difficult economic circumstances. If economic conditions do indeed affect voter turnout then
this time is an excellent opportunity to examine these effects, since it represents an extreme
case of adverse economic conditions.
6.1
Theory
The extensive economic voting literature has largely focused solely on vote choice and not on
voter turnout. This is an omission that has been noted as early as Radcliff (1992, 451), who
argued that the same economic conditions that were expected to promote an increased opposition vote also discourage those same individuals from voting at all. This same observation
has since been picked up by others. Lacy and Burden (1999, 235), for example, make the
point that vote choice models that do not account for the option of not voting are potentially
biased, as they necessarily exclude from consideration all individuals in the random sample
who did not vote—even though the decision not to vote is likely to have been conditioned on
some of the independent variables in the model—and these individuals may have different
opinions from those who do vote. In other words this approach risks introducing a selection
bias. Their empirical evidence supports this criticism (252). Even in more recent years, this
criticism is still being made of the economic voting literature (Stevens 2007; Tillman 2008;
Weschle 2014). Stevens (2007, 167) points out that abstention is in most cases an equally
viable option to supporting one or other of the parties and should therefore be included in
voting behaviour models. He even goes so far as to claim that the ‘the reward–punishment
6.1. THEORY
145
model of economic voting, which informs almost all research in this field, needs to broaden
its focus [because it] misses a large and important part of the story’ (183).
Given the increasing acknowledgement of the importance of abstention as an alternative
to making a vote choice decision, this chapter examines the factors that explain the turnout
decision. Despite the tendency for turnout to be overlooked in economic voting models, there
is still a literature examining the influence of economic conditions on turnout alone. Unfortunately, this literature has long been divided about the direction of this influence. In his study
of the economy’s influence on voter turnout, Rosenstone (1982) identified three competing
schools of thought about the effect of poor economic conditions on voter turnout. The first of
these is that citizens are more likely to vote, which he refers to as ‘mobilisation’ (25–26). The
argument behind this claim is based on the reward–punishment model of economic voting. If
economic adversity encourages a portion of the population to punish the government electorally, then they would need to vote in order to do so, hence turnout ought to increase. The
second, ‘withdrawal’, school of thought is that rather than increasing turnout, poor economic
conditions should have the opposite effect (26). The argument here is that a person whose
livelihood is at risk is going to be heavily focused on meeting their material needs and will thus
have few resources remaining for less immediate concerns such as politics. Lastly, Rosenstone
(1982, 27–28) also identifies a third, ‘no effect’, school of thought, namely that neither the
mobilisation nor the withdrawal theory is accurate and that there is no consistent link between
economic conditions and turnout. The argument for this position is that voters might consider
any economic suffering they experience to be a personal rather than a systemic issue and not
blame the government or alternatively that the modern welfare state has reduced economic
suffering to the point that it is no longer a dominant issue for voters.
More recent studies have tried to explain these disparate sets of findings in different ways.
For example, Radcliff (1992) argues that both mobilisation and withdrawal effects can be observed, the former in developing countries and the latter in developed countries. This claim
is based on an analysis of aggregate national election data from 29 countries and US timeseries election data. He further argues that this effect is mediated by the level of development
of the welfare state and that withdrawal is a characteristic of marginal welfare states, and
mobilisation of both non-welfare and institutional welfare states. Martins and Veige (2013),
on the other hand, argue mobilisation occurs under both extremely positive and extremely
negative conditions, when the economy is presumably a salient issue, and that withdrawal
occurs otherwise. This is based on an examination of turnout at local government elections
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CHAPTER 6. ECONOMIC ABSTENTION
in Portugal and Flanders. One limitation of these two studies is that they were both based on
aggregate data and so cannot examine the effects of individual-level differences. Arceneaux
(2003) criticises some previous papers, including Radcliff (1994), for trying to draw inferences about individual behaviour from aggregate data. He argues that this is the source of
evidence supporting the withdrawal hypothesis and, using data from the American National
Election Studies 1990–98, finds that economic adversity increases turnout, particularly for
those individuals who blame the government for the state of the economy. Another recent
individual-level study of turnout and vote choice at Italian elections during the Great Recession and its aftermath found that voters critical of the government’s handling of the economy
divided their response between non-voting, voting for the mainstream opposition and voting
for a new radical anti-establishment party (Passarelli and Tuorto 2014). In other words, this
paper also offers qualified support for the withdrawal hypothesis. The withdrawal hypothesis
is further supported by Stevens (2007), based on American National Election Studies data
from 1956 to 2000.
It is interesting to note that, of these studies, the individual-level single-country studies
have tended to find some variety of withdrawal effect, whereas the cross-country aggregate
studies have found the direction of the effect to be somewhat dependent upon context, although there is no agreement on precisely how this is mediated by the context. This chapter
aims to unify these different approaches by conducting multilevel analysis of turnout across
a number of EU member states. By using multilevel analysis, it is possible to conduct an
individual-level analysis while still taking into consideration relevant contextual factors. This
chapter also examines, as previous chapters have done, three survey waves, which took place
before, during and after the Great Recession. Comparing these surveys allows for an analysis
of the effect of unusual severity of economic conditions, which Martins and Veige (2013) found
to be an important contextual variable.
Looking beyond economic conditions, the turnout literature more generally identifies several factors that are linked to turnout. There are a number of studies looking into the political and systemic factors that influence turnout and these are widely acknowledged to outweigh the importance of individual variation. For example, Powell (1980) studied turnout in
30 democracies between 1960 and 1978 and found compulsory voting, ease of registration,
proportional representation and strong party–group alignment to be important predictors of
high turnout. In later studies he confirms the importance of compulsory voting, party–group
linkage and ease of registration, although with the exception of the United States, automatic
6.1. THEORY
147
registration is the norm for countries where registration is not compulsory (Powell 1982, 111–
132; 1986). Crewe (1981, 240–250) also discusses the strong effect of compulsory voting on
turnout rates. He discusses other administrative incentives, such as postal voting and automatic registration, but largely dismisses the importance of these apart from in the United
States. He also finds that countries where the party system closely corresponds to social cleavages generally experience a higher turnout (251–253). Jackman (1987) similarly finds compulsory voting to be strongly correlated with high turnout. He also finds nationally competitive districts and unicameralism to have a positive turnout effect. Multipartyism and electoral
disproportionality, on the other hand, were found to have a negative effect. This last is consistent with the earlier findings that proportional representation systems generally have higher
turnout than majoritarian systems. These results were largely confirmed by Jackman and
Miller (1995). In their aggregate study of elections in twenty Western industrialised countries
between 1847 and 1982, Blais and Carty (1990) identify compulsory voting and use of proportional representation as the most important contributors to high turnout, with population
size and female suffrage also having a modest negative effect on turnout.
More recent studies have once again confirmed many of these same findings. Franklin
(1996) compares turnout in 37 democracies, looking at both national-level and individuallevel differences, and finding that the differences between countries eclipse the differences
between individuals. At the country level, compulsory voting was once again found to be an
important predictor of high turnout, along with postal voting and proportionality. Blais (2000,
25–31) finds, in addition to the well-established effects of compulsory voting, multipartyism
and proportional representation, that the closeness of the election, degree of literacy and level
of economic development (but not economic growth) contribute to higher turnout rates. He
also argued that turnout was higher in elections that voters perceived to be close contests
(74). In his review of aggregate-level turnout research, Geys (2006) discusses the importance
of a number of aggregate variables, including population size and concentration, perceived
closeness of the election, campaign expenditure, political fragmentation, previous levels of
turnout and the characteristics of the political system.
Although the largest variation in turnout exists between countries rather than individuals, there have also been a number of studies seeking to explain this individual variation.
Wolfinger and Rosenstone (1980, 102–103) found education to be by far the most important
predictor of an individual’s likelihood to vote, with age the second most important. Students
and married individuals were also more likely to vote. Blais (2000, 51–52) used the Compar-
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CHAPTER 6. ECONOMIC ABSTENTION
ative Study of Electoral Systems post-election surveys from 1996–97 to analyse some of the
causes of individual variation in turnout. He argued that education and age were the most
important factors, followed by religiosity then wealth and marriage. Lewis-Beck et al. (2008,
82–107), in their individual-level study of US electoral behaviour discuss the importance of
vote preference, political involvement, sense of political efficacy, sense of citizen duty, closeness of the election, education and age, mobilisation and registration. Of the socio-economic
variables, age and education consistently feature as the two most important in turnout studies
(Blais 2007, 630–631). Franklin (2004) sought to explain the long-term decline in turnout
among established democracies. This is a particularly interesting study because it uses both
aggregate and individual data to test its hypotheses. Franklin found that the decline can be
explained by a combination of changing demographics and political changes. Besides some
idiosyncratic political changes, such as the abolition of compulsory voting in a couple of countries, he identifies the extension of the franchise to eighteen year olds in most countries as
one of the key causes (213). He specifically rules out changes in civic virtue or disaffection as
causes (215).
It is clear from this review that the importance of particular variables has been confirmed
again and again. Compulsory voting is undoubtedly an important predictor of high turnout,
as is the use of a proportional rather than a majoritarian electoral system. Small population
sizes and elections that are perceived to be close are also associated with higher levels of
turnout. Some of the earlier studies considered the extension of the franchise to women and
eighteen year olds to be of some significance but, owing to the universality of these practices
within the European Union, these are no longer relevant considerations. Similarly, as none
of the countries studied in this chapter have a policy of compulsory voting that is also not
relevant here.1 As for individual-level variables, the most important variables mentioned in
the literature are education and age. Religiosity, wealth and marital status are also noteworthy.
6.2
Hypotheses
This chapter uses survey data to test several hypotheses drawn from the theory outlined above.
The competing ideas about the nature of the relationship between economic conditions and
turnout lead to the following two hypotheses:
1
Belgium, Cyprus and Luxembourg do use compulsory voting but they are not included in the analysis for this
chapter.
6.3. MEASURING TURNOUT INTENTION
149
Hypothesis 6.1 Voters are more inclined to vote when they believe that economic conditions will
worsen, that is, there is a mobilisation effect.
and alternatively:
Hypothesis 6.2 Voters are less inclined to vote when they believe that economic conditions will
worsen, that is, there is a withdrawal effect.
These hypotheses take a prospective orientation, that is, it is assumed that voters act upon
their beliefs about the future course of the economy rather than their judgement about its past
performance. This is because, as has been discussed in previous chapters, there is considerably
more variance in reported prospective assessments immediately after the crisis than there is
in retrospective assessments, which were almost uniformly negative at that time.
The third hypothesis is based on the idea that an economic crisis intensifies economically
driven electoral behaviour beyond what would normally be expected, which is one of the
motivating ideas behind this thesis. The importance of unusual severity of economic conditions
has also been advanced as a key factor in predicting turnout by Martins and Veige (2013). This
leads to the hypothesis that:
Hypothesis 6.3 The effect of economic perceptions on turnout was of a greater magnitude or
a different sign during the Great Recession than during a period of relatively normal economic
conditions.
The null hypothesis then is that the sign and magnitude were the same at both times. It is
important to note that this would still allow for a stronger apparent turnout effect during the
Great Recession, simply because voters would be expected to have stronger views about the
condition of the economy at that time. For this hypothesis to be supported, there would have
to be evidence that any observable difference in turnout cannot be explained by a simple linear
model.
6.3
Measuring turnout intention
In order to test these hypotheses, the EES survey data is used once again. As in previous
chapters, the 2004, 2009 and 2014 waves are compared, so as to be able to contrast behaviour during the Great Recession and its aftermath with behaviour during relatively normal
economic conditions. Compulsory voting is presumed to dampen any abstention effect considerably, so this analysis excludes countries where compulsory voting is enforced. This means
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CHAPTER 6. ECONOMIC ABSTENTION
that Belgium, Cyprus and Luxembourg are excluded, while Greece is still included because
compulsory voting is not enforced there and Switzerland is also included because voting is
only compulsory in one canton (Birch 2008, 36). The dependent variable, turnout intention,
was measured with the question ‘And if there was a general election tomorrow, which party
would you vote for?’2 In the first two waves, this was asked as an open-ended question and
no list of options was read out. Those respondents who named a party are coded as having
turnout intention and those who said they would not vote, would spoil their vote or would vote
blank are coded as having no turnout intention. Unfortunately, in the most recent wave, a list
of parties was read out, and this appears to have affected the response. Whereas 89.6 percent
and 87.8 percent of voters reported intending to vote in 2004 and 2009 respectively, a full
95.8 percent of voters reported intending to vote in 2014, when refusals are excluded. On the
other hand, the proportion of refusals also increased considerably in 2014. Furthermore, it has
been known for some time that survey respondents tend to overreport turnout (for example,
see Traugott and Katosh 1979). It has also been shown that offering face-saving responses
tends to mitigate this tendency (Zeglovits and Kritzinger 2014). Based on these ideas, it was
decided to code refusals to answer this question as as non-intention to vote. Although not
a perfect solution, this produces a similar turnout intention rate in year, with 72.7 percent,
76.4 percent and 65.9 percent of respondents coded as having turnout intention in 2004, 2009
and 2014 respectively.
These estimated turnout rates are much closer to the actual recorded turnout rates than
the implausibly high rates obtained by ignoring refusals. Figure 6.1 shows the average turnout
at national elections in the EU since 1990, with the survey years indicated by dashed vertical
lines. As the figure shows, the average turnout at national elections in the EU since the year
2000 has been between the mid-sixties and the low-seventies. The estimated turnout rates
also lie in this range, although they are still slightly higher than the recorded turnouts in the
corresponding years. This remaining discrepancy might result partly from the fact that the vote
choice question is hypothetical and therefore not a perfect indicator of what voters would do
at an actual election. Moreover, the recorded average turnout for a particular year is of course
affected by which countries actually held general elections in that year, as typical turnout rates
vary from country to country. It is also likely that the sample is not perfectly random.3 The
effects of this are mitigated by including appropriate controls in the model but it means that
2
This question was worded identically in all three survey waves, except that ‘was’ became ‘were’ in 2014. As
always, the local translations may vary.
3
Sampling, as well as related questions like interview mode and response rates, are discussed in Chapter 2.
6.3. MEASURING TURNOUT INTENTION
151
Figure 6.1: Average turnout at national elections in EU countries
EU average turnout
77.5
75.0
72.5
70.0
1990
1995
2000
2005
2010
2015
year
Average turnout at national elections in the European Union (EU-27) by year. The three dashed
lines indicate the survey years of 2004, 2009 and 2014. Source: Eurostat
there may be problems in estimating the population turnout rate from this data, which is not
the purpose of this chapter anyway. It can also be seen from this figure that turnout has been
in decline over the medium term. This decline has been seen across the developed world and
has attracted much scholarly attention (see for example Lyons and Alexander 2000; Franklin
2004; Blais 2007, 2013). Its relevance to this chapter is simply that this medium-term decline
should be kept in mind when making inferences about any change in the absolute turnout rate
across the period 2004–2014.
As was discussed earlier, the variables that are expected to predict turnout are largely
the same as those predicting vote choice. The substantive independent variables are party
identification and economic assessment. Additionally, whether the voter reported voting at
the previous general election is a new independent variable for this chapter. This inclusion
is based on the evidence that voting tends to be habitual (Green and Shacar 2000; Plutzer
2002; Coppock and Green 2015), so those who have voted before are more likely to vote
again. This variable is measured in the same way as the dependent variable, except that it is
based on a question asking voters who they voted for at the preceding general election in their
country, rather than a hypothetical election held tomorrow. In addition to these, the same
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CHAPTER 6. ECONOMIC ABSTENTION
Figure 6.2: Proportion intending to vote by year and economic assessment
2004
2009
2014
0.9
0.8
prospective
0.7
0.6
proportion
0.5
0.4
0.9
0.8
retrospective
0.7
0.6
0.5
0.4
-2
-1
0
1
2 -2
-1
0
1
2 -2
-1
0
1
2
economic assessment
Proportion of respondents intending to vote according to the respondent’s prospective (first
row) and retrospective (second row) economic assessment in each survey year, shown with
95% confidence intervals. Both forms of economic assessment are measured on a five-point
scale ranging from −2, indicating a very negative assessment, to +2, indicating a very positive
assessment, with 0 indicating a neutral assessment. Source: EES
control variables are used as in previous chapters. See Chapter 2 for a discussion of how these
variables were measured.
The mobilisation and withdrawal hypotheses (Hypotheses 6.1 and 6.2) predict that voters
are respectively more or less inclined to vote when they believe that economic conditions are
poor. The pattern of responses to the vote intention question suggests that voters were more
likely to abstain from voting during the recession than they had been beforehand and that they
became even more likely to abstain in the years after the recession. The mean proportion of
respondents indicating an intention to vote was 72.7 percent [72.1%, 73.3%]4 in 2004, which
fell to 67.7 percent [66.7%, 68.0%] in 2009 and 65.9 percent [65.3%, 66.5%] in 2014. These
changes are all statistically significant. This decline in turnout intention over the 2004–2014
4
Square brackets indicate 95% confidence intervals.
6.4. AN ECONOMIC MODEL OF TURNOUT
153
decade is consistent with the decline in actual turnout at national elections shown over the
same period in Figure 6.1.
In order to shed light on the questions of mobilisation and withdrawal, it is necessary
to look at the relationship between economic assessment and turnout intention. Figure 6.2
shows the proportion of respondents indicating that they would vote at an election held the
following day disaggregated by their assessment of the economic conditions in their country.
The first row shows the breakdown by prospective economic assessment. An estimate of zero
indicates the belief that the economy will remain more or less the same over the following
year; negative numbers indicate the belief that it will get worse; and positive numbers indicate
the belief that it will improve. A similar pattern is observable in all three years. A positive
assessment of future economic conditions is associated with a greater likelihood of voting.
This pattern is preliminary evidence in support of the withdrawal hypothesis. The second row
shows the proportion intending to vote according to their retrospective economic assessment.
Here negative numbers indicate the belief that the economy has become worse over the past
year and positive numbers the belief that it has improved. In 2004 and 2014 the pattern of
turnout according to retrospective economic assessment closely resembles that according to
prospective assessment. In 2009, however, the proportion intending to vote does not increase
monotonically as the retrospective assessment improves and the confidence interval for the
most positive category is very wide because only a handful of voters claimed that the economy
in 2009 was much better than it had been previously. This mirrors the relationship between
mean party support and both kinds of economic assessment, which was discussed in Chapter 3.
Accordingly, the prospective measure will continue to be used, as it has throughout this thesis.
6.4
An economic model of turnout
A multilevel logistic regression model (Model 6A) has been constructed so as to test whether
there was a mobilisation or withdrawal effect and whether the magnitude of that effect was
altered during the crisis. This has two main advantages. First, it makes it possible to control for confounding variables that might otherwise explain the differences discussed above.
Second, because this is a comparative study of multiple countries, multilevel analysis helps to
to separate the variance due to individual variation from the variance due to the cross-national
design. This model predicts the probability of an individual intending to vote based primarily on his or her prospective economic assessment, party identification and past behaviour
and past behaviour. Prospective economic assessment is on a five-point scale, with negative
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CHAPTER 6. ECONOMIC ABSTENTION
scores indicating a pessimistic assessment, positive scores an optimistic assessment and zero
a neutral assessment. Since each country has its own set of political parties, the various party
identifications have been grouped into government identifiers, those who identify with one of
the parties included in the cabinet, opposition identifiers, who identify with any other party,
and non-identifiers, who did not feel close to any particular party. The reference group is nonidentifiers. Past behaviour is represented by a dummy variable indicating whether or not the
respondent reported having voted at the previous national election. Naturally, the survey year
is also included in the model so that changes over time can be detected. As is usual, 2004 is
the reference year and the other two years are represented by dummy variables. Interaction
terms are also included between the survey year dummies and each of these primary predictors. In addition to these substantive variables, several demographic variables have also been
included in the model. As previously, these control for the sex, age, level of education and
labour force status of the respondent, as well as the population density of his or her area of
residence. Interaction terms have also been included between age and the party identification variables, as in previous chapters. As a multilevel model, this model also controls for the
country where the interview occurred by means of a random intercept. Random slopes were
also included for the two party identification dummy variables, in order to account for the fact
that these do represent different parties in the different countries.
This model was used to predicted the probabilities of an individual reporting that they
would vote at an election held the following day under various circumstances. Figure 6.3
shows these predicted turnout probabilities according to the survey year and whether the
individual reported voting at the previous general election.5 These particular predictions are
for an individual holding a neutral prospective economic assessment. The predicted likelihood
of someone voting if they had voted at the previous election was 72 percent [68%, 75%] in
2004, 61 percent [57%, 66%] in 2009 and 71 percent [68%, 75%] in 2014. For people who
had not voted at the previous election, these probabilities were much lower, at 29 percent
[25%, 32%] in 2004, 20 percent [17%, 23%] in 2009 and 22 percent [20%, 26%] in 2014. All
of these differences are significant, except for the difference between 2004 and 2014 among
people who had previously voted (odds ratio 0.99, CI 0.91–1.07). It thus appears that people
were less inclined to vote during the recession than beforehand or afterwards, although this
inclination did not recover its previous level among people who had not voted previously. As
the figure shows, having voted at the previous election is a very strong predictor of future
5
Unless otherwise specified, all predictions are for a 40 year old male, who has completed high school but not
university, is employed and lives in a town.
6.4. AN ECONOMIC MODEL OF TURNOUT
155
Figure 6.3: Predicted probability of voting by past behaviour
predicted turnout probability
0.7
0.6
at last election
0.5
voted
did not vote
0.4
0.3
0.2
2004
2009
2014
year
Predicted probability that an individual would vote in each survey year. The top line is for an
individual who reported voting at the previous national election and the bottom line is for an
individual who did not. These predictions are all for individuals with a neutral prospective
economic assessment. Source: EES & ParlGov
voting intention. This is unsurprising in light of the evidence that voting is a habit (Green
and Shacar 2000; Plutzer 2002; Coppock and Green 2015). Voters in 2004 were 2.5 times as
likely (odds ratio 6.3, CI 5.8–6.8) to vote if they had voted at the previous election, compared
to 3.1 times as likely (OR 6.4, CI 5.9–6.9) in 2009 and 3.2 times as likely (OR 9.7, CI 8.0–
9.3) in 2014.6 The difference between 2004 and 2009 is not statistically significant (OR 1.01,
CI 0.91–1.13) but appears larger owing to the lower base rate in that year. The difference
between 2009 and 2014, on the other hand, is significant (OR 1.36, CI 1.22–1.51). This
suggests that there was increased divergence between habitual voters and non-habitual voters
in the years following the recession.
The other key independent variables were party identification and prospective economic
assessment. Figure 6.4 shows the relationship between these and the predicted likelihood of an
6
In order to keep the interpretation of these results as intuitive as possible, the effect sizes reported here are
relative probabilities. This has the disadvantage that relative probabilities are sensitive to the initial conditions,
that is, the same effect size can produce quite different relative probabilities if the base probability changes. For
this reason, the corresponding odds ratios are also reported. The uncertainty of the estimates is indicated by giving
the 95% confidence interval around the odds ratio. The reason confidence intervals are used for this model, rather
than the standard errors and p-values used elsewhere in the thesis, is that standard errors are only meaningful on
the log odds scale for logit models, not the odds ratio or relative probability scales.
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CHAPTER 6. ECONOMIC ABSTENTION
Figure 6.4: Predicted probability of voting by economic assessment and party identification
predicted turnout probability
2004
2009
2014
0.9
0.8
0.7
0.6
-2
-1
0
1
2 -2
-1
0
1
2 -2
-1
0
1
2
prospective economic assessment
party ID
government
opposition
none
Predicted probability that an individual would vote according to that individual’s prospective
economic assessment as well as the survey year. Economic assessment ranges from −2 (very
pessimistic) to +2 (very optimistic). The top line in each year represents an individual who
identifies with one of the parties currently represented in the cabinet. The middle line represents an individual identifying with one of the other parties and the bottom line represents
an individual who lacks any party identification. These predictions are for an individual who
reported having voted at the previous national election. Source: EES & ParlGov
individual voting in each survey year. It can be seen that party identification plays a large role
in predicting the likelihood of someone voting. In all years, party identifiers are considerably
more likely to vote than non-party identifiers and those identifying with opposition parties are
slightly more likely to vote than those identifying with government parties. For example, in
2004, government identifiers were 27 percent more likely to vote (OR 4.3, CI 3.5–5.2) than
those identifying with no party and opposition identifiers were a further 3.1 percent as likely
to vote (OR 1.6, CI 1.3–1.9). The difference between non-party identifiers and government
identifiers gradually widened (OR 1.33, CI 1.14–1.55) between 2004 and 2014, while the
gap between government and opposition identifiers narrowed somewhat (OR 0.80, CI 0.66–
0.96) over the same period. The differences between 2009 and the other two years were
not significant, or in one case barely significant. As the figure shows, the likelihood of voting
dropped among all three groups in 2009 before recovering in 2014. It is interesting to note that
6.4. AN ECONOMIC MODEL OF TURNOUT
157
the likelihood of voting in 2014 actually exceeded that of voting in 2004 among government
identifiers, with this group 2.0 percent more likely (OR 1.31, CI 1.14–1.51) to vote than before.
The relationship of most substantive interest for this chapter is that between an individual’s economic assessment and his or her likelihood of voting. It can be seen from Figure 6.4 that optimistic voters were more likely to vote than pessimistic voters in all three
years. This can be described as an economic abstention effect. Among non-party identifiers
who had voted at the previous election, highly optimistic voters were 13 percent more likely to
vote (OR 1.55, CI 1.33–1.81) than highly pessimistic voters in 2004, compared to 8.8 percent
more likely (OR 1.24, CI 1.08–1.43) in 2009 and 23 percent more likely (OR 2.06, CI 1.75–
2.42) in 2014. These findings support the withdrawal hypothesis, which states that pessimistic
voters are less inclined to vote, and contradict the mobilisation hypothesis, which asserts the
opposite. This economic abstention effect was weaker (OR 0.80, CI 0.65–2.05) in 2009 than
in 2004, which can be seen from the shallower slopes in the figure. The effect became stronger
again (OR 1.65, CI 1.34–2.05) in 2014, actually exceeding (OR 1.32, CI 1.05–1.66) its 2004
strength. This is reflected in the steeper slopes in the 2014 panel in the figure. These findings
contradict Hypothesis 6.3, which predicts that the Great Recession would either strengthen
the magnitude or reverse the sign of any economic abstention effect. In fact, these findings
suggest that the effect was partly suppressed during the recession. On the other hand, the
effect was strengthened beyond its initial level during the post-recession period. This fits the
pattern, found in previous chapters, of economic voting effects becoming weaker during the
crisis itself, with the most striking effects occurring in its aftermath.
In addition to these effects, there were also some small demographic differences in the
likelihood of voting. Women were 3.7 percent less likely (OR 0.88, CI 0.84–0.92) to vote than
men.7 The model allows age to interact with party identification, so the effect of age varies accordingly. For non-party identifiers a 30 year old was 3.0 percent less likely (OR 1.04,
CI 1.01–1.07) than a 20 year old, compared to 0.4 percent less likely for either a government (OR 0.94, CI 0.92–0.97) or opposition (OR 0.92, CI 0.89–0.94) identifier. It is surprising
that older people are apparently less likely to vote than younger people but this is only true
because everything else is held equal. In particular, many older poeple are no longer in the
workforce and older people are also less likely to hold a pessimistic economic assessment
than younger people. Education had a small effect, with those who had not finished high
school 2.7 percent more likely (OR 1.11, CI 1.04–1.18) to vote than those who had, when
7
All of these comparisons are for a non-party identifier in 2004 holding a neutral economic assessment.
158
CHAPTER 6. ECONOMIC ABSTENTION
everything else is held equal. University graduates were not significantly different from high
school graduates (OR 1.03, CI 0.98–1.09). Urban density had no significant effect on turnout.
Employed people were not significantly different from the unemployed (OR 0.99, CI 0.91–
1.07) but those not in the workforce, which includes retired people, were 4.9 percent more
likely (OR 1.20, CI 1.14–1.26) to vote than the employed.
As this is a multilevel model, it is also possible to investigate the degree to which individual countries deviate from what has already been discussed. The intercept varies considerably between countries, which is unsurprising given that the actual level of turnout varies
from country to country. According to this model, among those holding no party identification, respondents in the United Kingdom, Ireland and Denmark were the most likely to report
intending to vote, whereas voters in Portugal, Malta and Poland were the least likely to do
so. Among those identifying with government parties, however, respondents in Finland, Denmark and Slovakia were the most likely to indicate an intention to vote, whereas voters in
Ireland were the least likely, followed by Slovenia and Malta. In fact, the random intercept
is negatively correlated with the random slopes for both government (ρ = −0.70) and opposition (ρ = −0.89) identification, which means that the countries with the highest turnout
among non-party identifiers also tend to be those with the lowest turnout among party identifiers. The two party identification random slopes are positively correlated (ρ = +0.55),
indicating that turnout among the government and opposition identifiers of a country tends
to be similar, given that the corresponding fixed effects are also close.
6.5
Conclusion
Previous research has produced inconsistent findings on the nature of the relationship between
economic conditions and voter turnout. The competing hypotheses are that voters are mobilised to vote by poor economic conditions in order to hold the government responsible and
that voters withdraw under poor economic conditions because they are demotivated by the
situation (Rosenstone 1982). A multilevel analysis of survey data collected from 22 countries
in three different years was conducted in order to shed further light on this question. This
approach has the advantage of being less sensitive to country-specific circumstances as well as
the increased statistical power of the larger sample size. This analysis supports the withdrawal
hypothesis and is inconsistent with the mobilisation hypothesis. In all three years, it was found
that voters with a more optimistic prospective economic assessment were more likely to vote,
often considerably more likely.
6.5. CONCLUSION
159
It was also hypothesised that any economic abstention effect would be either stronger
or reversed in direction during a time of deep international crisis than under more typical
economic conditions. Although the other hypotheses have been examined by earlier studies,
there have been comparatively few that have studied voter behaviour under crisis conditions.
It has previously been argued that withdrawal occurs during ordinary conditions but that
mobilisation takes place under extremely positive or negative conditions (Martins and Veige
2013). By comparing the two surveys, one collected during the Great Recession and one well
before it began, this chapter has been able to examine this hypothesis. It was found that the
withdrawal effect still prevailed during the crisis, although it was weaker, as well as after
the crisis, when it was even stronger than it had been previously. This evidence does not
support the hypothesis. Rather, as in previous chapters, it suggests that the normal economicdriven electoral tendencies of voters were muted during the crisis itself, with the strongest
effects taking place well after the initial crisis. This pattern suggests that voters may have
been responding more to the political reaction to the recession than to its mere occurrence.
Since this pattern has emerged in the findings made in this thesis, focus will now turn to
establishing whether there is other evidence that the political reaction to the Great Recession
has had a greater impact on voters than the crisis itself. A possibility that presents itself is
that voters were primarily reacting against the austerity policies implemented in the wake of
the crisis. If this is indeed the case, then it ought to affect attitudes towards the European
institutions, which in many cases promoted those policies. The next chapter will examine
European voters’ attitudes towards the European Union and the prospect of further integration.
By examining the relationship between these attitudes and voters’ economic assessments, as
well as the evolution in these patterns over time, further light will be shed on this question.
Chapter 7
Attitudes towards European integration and
institutions
On 23 June 2016, the citizens of the United Kingdom voted to leave the European Union.
Despite the government and all three major parties opposing the move, 51.9 percent of the
votes cast in the referendum were in favour of leaving, with a high turnout of 72.2 percent
(BBC News 2016a). This surprising result is just the most visible manifestation of increasing
anti-EU sentiment. It follows the 2014 European Parliament elections, which delivered a record number of seats to overtly Eurosceptic parties. The three strongly pro-EU groups lost
65 seats at the election, largely to radical right, Eurosceptic groups (Brack and Startin 2015,
242). The Greek results, which saw a surge of support to the far left Syriza and far right
Golden Dawn at the expense of the centrist parties, have been described as a ‘seismic shock’
(Verney 2015). These events followed not just the Great Recession but also a period in which
many European governments introduced harsh austerity policies in attempt to stave off further economic troubles. Do these results represent an underlying shift in sentiment away from
the institutions of the EU, and if so, can this shift be linked to the politics of austerity? This
chapter explores these questions.
A pattern has emerged in the findings made in this thesis so far, which is that the most
striking shifts in voter behaviour took place later than expected. It was shown in Chapter 3
that the economic vote was strongest not in 2009, when the first wave of the Great Recession
was at its peak, but in 2014 when this had given way to a period of austerity politics. A
similar pattern was found for voter turnout in Chapter 6. It was also shown in Chapter 5 that
voters were more likely to show support for parties holding far left and far right positions
in this post-recession period than they were either before or during the recession. In all of
these cases, voters holding pessimistic views about the economy showed stronger changes
in behaviour than those holding optimistic views. These trends suggest that the instigator for
161
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CHAPTER 7. ATTITUDES TOWARDS EUROPEAN INTEGRATION AND INSTITUTIONS
the change in voter behaviour may have been the unpopular austerity policies introduced after
the recession, rather than the mere fact of the recession itself. These policies were strongly
associated with the European Union, partly because the EU did encourage them and partly
because national governments found it convenient to remind voters of the European scope
of many of the economic problems facing their countries in order to diffuse responsibility
for politically troublesome measures (Hobolt and Tilley 2014, 100–119). If this theory is
true, then accordingly there ought to be evidence of a change in attitudes towards European
institutions between 2009 and 2014. This chapter analyses survey data in order to test whether
such a change took place.
The chapter begins by discussing the data that was used to explore these questions. The
European Election Studies surveys used throughout this thesis include a number of questions
addressing attitudes towards different aspects of EU politics and this section explains which
were used for this analysis and the reasons behind that selection. Following that, the chapter
introduces the four hypotheses that will be tested and discusses the theoretical motivation
behind them. Three statistical models have been constructed to test these hypotheses and
these are discussed in the following sections. The first of these concerns attitudes towards
European unification and whether voters believe that this should go further or that it has
already gone too far. The second looks at whether voters believe that membership of the EU
is a good thing or a bad thing for their country. The final model addresses the attribution
of responsibility for the economy and the degree to which voters hold the EU as opposed to
their national governments economically responsible. The evidence offered by these models
supports the theory that the post-recession austerity period produced a greater response in
voters than the recession itself did. The chapter concludes with a summary of these findings
and a discussion of their implications.
7.1
Austerity and the European Union
The European Union has had an active role in introducing austerity measures to four of its
member states: Ireland, Greece, Portugal and Cyprus. This took the form of ‘economic adjustment programmes’—bailouts conditional on specified economic reforms—negotiated by the
European Commission in conjunction with the European Central Bank and the International
Monetary Fund. These three institutions are sometimes referred to collectively as the Troika.1
1
The term ‘Troika’ is occasionally even used in official documents, e.g. European Commission (2011b, 4).
7.1. AUSTERITY AND THE EUROPEAN UNION
163
In May 2010, Greece was the first country to negotiate such an agreement, receiving 80 billion euros in loans from other Eurozone countries and a further 30 billion euros from the IMF.
In return, Greece was required to implement a number of structural reforms, including expenditure cuts amounting to seven percent of its GDP, much of which came from pensions and
government wages, and new tax measures equivalent to four percent of GDP (European Commission 2010). In December 2010, in the wake of its banking crisis, Ireland was the second
country to negotiate a bailout package with the Troika, receiving 85 billion euros of financial
assistance while being required to increase taxation and cut expenditure drastically, among
other things (European Commission 2011a). This pattern of loans granted by the Troika to
member states contingent upon the imposition severe austerity measures was continued in the
following years, with an agreement signed by Portugal in May 2011 (European Commission
2011b), a second agreement signed by Greece in March 2012 (European Commission 2012),
and finally an agreement with Cyprus signed in March 2013 (European Commission 2013).
Although European institutions have only been actively involved in the austerity policies of
these four countries, their influence is broader than that. The rules of the Economic and Monetary Union, which are binding on Eurozone members, include a ‘Stability Growth Pact’ (SGP).
The SGP requires member states to keep government deficits to within three percent of GDP
and also requires that they aim for surpluses in the medium term, with strict sanctions specified
for breaches, although these are not automatic in operation and have proven easily avoidable
(Heipertz and Verdun 2010, 3–7). These rules have effectively institutionalised the monetarist
position, which is favoured by German politicians, but conflicts with competing economic theories preferred by others, such as Keynesianism (Scharpf 2013, 109–114). It has been argued
that monetarist policy has not been effective for the Eurozone, owing to the large size of the
currency union (114–125). McBride (2015) argues that the SGP and other EU rules such as
the limit the capacity of member states to adopt any expansionary policies—in other words,
austerity is enforced from the centre. It has also been argued that not only do the economic
adjustment programmes reflect a German belief in austerity as the only option, but they also
put economic pressure on the remaining member states to adopt similar policies, if they wish
to avoid losing competitiveness (Flassbeck and Lapavitsas 2015).
The EU is thus directly responsible for the austerity programmes of four countries and
arguably indirectly responsible for austerity measures taken elsewhere. Even in countries
where austerity has not been adopted, taxpayers have funded a large proportion of the bailout
loans paid out under the four economic adjustment programmes. Moreover, there is evidence
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CHAPTER 7. ATTITUDES TOWARDS EUROPEAN INTEGRATION AND INSTITUTIONS
that national politicians have been able to persuade many voters that the European Union,
rather than themselves, is responsible for disliked policies, economic or otherwise (Hobolt
and Tilley 2014). It will also be shown later in this chapter that voters typically regarded the
EU as having more responsibility for the economy in 2014 than they had done in 2009. This
suggests that attitudes towards the EU would be likely to reflect any anti-austerity sentiment
arising in the post-recession period.
7.2
Measuring attitudes towards the European Union
One important question that arises when examining attitudes towards the EU is whether these
attitudes are merely a reflection of the voter’s position on the left–right spectrum, or whether
they represent an additional, orthogonal dimension to Europeans’ political views. There are
competing views in the literature as to the relationship between these two dimensions. The
‘international relations model’ sees the European question as unrelated to domestic left–right
questions, pertaining instead to questions of national interest, so that the major parties in a
particular country might be expected to show some agreement on the question of integration
(Steenbergen and Marks 2004, 5–6). Hix and Lord (1997, 26), on the other hand, argue
that the integration question is a second, orthogonal dimension to the politics of EU member states. This still assumes that the two dimensions are independent but it also permits
intranational contestation of the integration question. An analysis of European manifestos
has given this model some empirical support (Hix 1999). A third model is the ‘regulation
model’ (Steenbergen and Marks 2004, 7–8; Tsebelis and Garrett 2000), which argues that the
European dimension is not independent of the left–right dimension. In this model, those on
the left are supposed to be in favour of higher regulation and therefore greater European integration and those on the right of less regulation and so looser European integration. Finally,
Hooghe and Marks (1999) argue that the two dimensions cannot be collapsed together but
are not independent either. Political positions along these two dimensions are constrained by
‘the emergence of a cleavage ranging from center-left supranationalists who support regulated
capitalism to rightist nationalists who support neoliberalism’ (76).
Efforts to compare these models empirically have made similar findings, although their interpretations differ in some respects. Gabel and Hix (2004, 111) found that the best performing model was the traditional unidimensional left–right model in their analysis of European
election manifestos. Gabel and Anderson (2004, 30) agree that European politics is effectively
7.2. MEASURING ATTITUDES TOWARDS THE EUROPEAN UNION
165
unidimensional in their study of citizen attitudes, although they appear to favour a HoogheMarks interpretation of that dimension. According to Hooghe, Marks and Wilson (2004, 139),
European integration is structured by the traditional left–right dimension but the two dimensions are not identical because parties at the extreme left and right are less supportive of the
EU than parties occupying the centre. Others argue that attitudes towards the EU are a reflection of not so much a person’s political views but their perceptions of their own government.
Harteveld, van der Meer and De Vries (2013, 561) observed that trust in national institutions
was an important predictor of trust in the EU in Eurobarometer survey data from 2009. This
was seen as confirming their hypothesised ‘logic of extrapolation’, which proposed that ‘the
legitimacy of the EU is actually derived indirectly, through the legitimacy of the individual
member states’ (546–547).
Even when a second, European dimension of political space has been recognised, its importance has not been undisputed. Van der Eijk and Franklin (2004, 32) have shown that this
dimension has not had a large influence on European political behaviour, although this work
took place well before the Great Recession. They argue that this is not because this dimension
is unimportant to European voters but rather because the parties on offer at EU elections do
not cover the entire political space, which means that voters are not currently free to select
parties according to both their left–right preferences and their preferences towards the EU.
For this reason, they describe this European preference dimension as a ‘sleeping giant’ that
has the potential to awaken as a new motivator for vote choice if either new parties emerge to
fill those empty parts of the political space or unforeseen events lead voters to prioritise their
EU preferences over their left–right preferences (van der Eijk and Franklin 2004). One of the
goals of this chapter is to determine whether either the Great Recession or the austerity period
could be said to have constituted such an event.
For the purposes of this chapter, it is assumed that there is an EU dimension to Europeans’
political views and that this can be measured through survey questions. It is not however
assumed that this is completely independent of the left–right dimension and this is why the
models introduced later in the chapter control for the individual’s left–right position. As will
be seen, the results from those models suggest that, while there is a relationship between the
two dimensions, this relationship is not overwhelmingly powerful nor is left–right position the
dominant predictor of attitudes towards the EU.
Precisely how to measure these attitudes is also not uncontested. Boomgaarden et al.
(2011, 242) argue that discussion of attitudes towards the EU requires more precise terms
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CHAPTER 7. ATTITUDES TOWARDS EUROPEAN INTEGRATION AND INSTITUTIONS
than ‘EU support’ or ‘Euroscepticism’. To this end, they conducted a survey in the Netherlands
in 2008 asking various different questions about attitudes towards the EU (246). A factor
analysis of their results found five factors, each consisting of five items. These factors were
identified as negative affection, identity, performance, utilitarianism and strengthening (247–
251). Unfortunately, the EES survey data does not include items that would allow each of
these dimensions to be measured in every year but it does include one question corresponding
to the utilitarianism dimension and one corresponding to the strengthening dimension. These
are the questions asking whether membership of the EU is a good thing and whether European
integration has gone too far respectively. Since these two questions appear to be measuring
somewhat different things, they are both analysed in this chapter.
There are several questions in the EES surveys asking about attitudes towards the European
Union. Often there are corresponding questions about the respondent’s national government,
which would allow attitudes towards the EU and towards national governments to be compared directly. Unfortunately, the set of questions asked is different in each wave and some
otherwise promising questions were not asked in each of the years under study. For example,
questions about satisfaction with democracy in both the EU and the respondent’s country
were asked in both 2004 and 2009 but not in 2014. Questions about trust in the national and
European parliaments were missing from the 2009 survey and questions about whether the
national and European parliaments respond to the concerns of citizens were not asked in the
2004 survey.
Despite these difficulties, a set of survey questions was selected that does offer a useful
comparison of attitudes towards the EU across the years of interest. The first of these questions
asked voters to give an opinion as to whether further European unification is desirable. The
precise wording of the question is:
Some say European unification should be pushed further. Others say it already has
gone too far. What is your opinion? Please indicate your views using a scale from 0
to 10, where 0 means unification ‘has already gone too far’ and 10 means ‘it should
be pushed further’. What number on this scale best describes your position?
This wording is almost identical in all three years, except that in 2004 a ten-point scale was
used rather than an eleven-point scale. The use of ten-point scales was a consistent feature
of the 2004 survey wave. In order to make these responses comparable to the responses in
the later waves, the same correction was applied as was used for similar scales in previous
chapters. See Chapter 2 for full details.
7.2. MEASURING ATTITUDES TOWARDS THE EUROPEAN UNION
167
The second question asks voters their opinion of their country’s EU membership. The
wording is almost identical in all three years:
Generally speaking, do you think that [country’s] membership of the European
Union is a good thing, a bad thing, or neither good nor bad?
Although this question is superficially similar to the first question, they are measuring different
things. It is quite consistent to believe, for example, that European integration has gone too far
while still holding that membership of the EU is a good thing. This interpretation is supported
by the fact that the two variables are only weakly correlated (Spearman’s ρ = 0.38). If the
two variables were in fact measuring the same thing, this correlation ought to be considerably
stronger.
The final two questions ask voters to attribute a degree of responsibility for their country’s
economic conditions to their country’s government and to the EU respectively. The 2009 survey
worded the questions as following:
Now I would like to ask you some questions about how much responsibility the
[country’s] government and the European Union have for some of the things going
on in [country]. Of course you may think that neither is responsible.
First, thinking about the economy, how responsible is the [country’s] government
for economic conditions in [country]? Please indicate your views using any number on a scale from 0 to 10, where 0 means ‘no responsibility’ and 10 means ‘full
responsibility’.
And what about the European Union, how responsible is the EU for economic
conditions in [country]?2
Unfortunately, these questions were only introduced in 2009, so they are not included in the
2004 survey. These questions are, however, so directly relevant to this thesis that they are
worth using despite this omission. This means that it is not possible to gain as complete a picture of this variable as it is other variables, in that 2004 cannot serve as a baseline comparison
year in this case. On the other hand, this pair of variables does make it possible to examine the
relative responsibility assigned to governments compared to the EU in the key years of 2009
and 2014.
2
The wording used in the 2014 survey is: ‘Now I would like to ask you some questions about how much
responsibility the different institutions have in the current economic situation in [country]. Please use a scale from
0 to 10, where 0 means that you think they have “no responsibility” and 10 means that they “full responsibility”.’
The country’s government and the European Union are the first two institutions read out.
168
7.3
CHAPTER 7. ATTITUDES TOWARDS EUROPEAN INTEGRATION AND INSTITUTIONS
Hypotheses
The focus of this chapter is voter attitudes towards the European Union. The objective is to
determine whether there was an observable shift in attitudes in the post-recession period.
Such a shift could help to explain the results of the previous chapters, in which it was found
that the most notable changes took place in the period following the recession rather than
during the recession itself. Based on these findings, it was theorised that it was chiefly the
austerity policies characterising the post-recession period that voters were reacting against.
If this theory is accurate, then it would be expected that voters reacted against the EU in
particular during this period, as the EU was strongly associated with these austerity policies.
This chapter tests four specific hypotheses derived from these ideas.
The first hypotheses concern support for the institutions of the EU. This chapter has introduced two relevant variables, namely the respondent’s opinion about the desirability of
continued European integration and whether membership of the EU is a good thing for the
respondent’s country. Although it has been argued that these variables are measuring different things, it is hypothesised that they have been affected similarly by the events of the Great
Recession and its aftermath. One of the key ideas in previous chapters has been that the economic voting concept is generalisable beyond vote choice to related variables, such as turnout.
Accordingly, it is expected that support for EU institutions is likewise influenced by a citizen’s
prospective economic assessment. This leads to the chapter’s first hypothesis:
Hypothesis 7.1 Support for both EU membership and further European integration is greater
among voters who have an optimistic economic assessment.
Previous work using Eurobarometer survey data has already shown that support for both of
these is linked to an individual’s retrospective economic assessment (Gabel and Whitten 1997).
It has also been shown, using the 2009 EES survey data, that a positive retrospective assessment is associated with greater satisfaction with democracy at the EU level (Hobolt 2012, 99).
Although this thesis uses some of the same data, it is still useful to test this hypothesis for two
reasons. Firstly, those studies tested a retrospective version of the hypothesis, whereas this
thesis is focused on the effects of prospective economic assessment.3 The second reason is
that this study includes the data from not only 2009 but also 2004 and 2014, which provides
3
The main reason for this is that the respondents in the 2009 EES survey were broadly in agreement that the
economy had worsened over the past year, whereas there was considerably more variation in the responses to the
prospective question. Chapter 1 discusses this issue in greater depth.
7.3. HYPOTHESES
169
an opportunity to examine how this relationship has evolved over the course of the recession
and its aftermath.
The main idea that this chapter seeks to test is that the post-crisis austerity policies are
responsible for a voter backlash against the European Union. If this is indeed the case, then
it would be expected that there would be an observable decline in support for the institutions
of the EU in the period following the crisis. The second hypothesis is thus:
Hypothesis 7.2 Support for both EU membership and further European integration fell between
2009 and 2014.
Some existing studies have looked at related questions. For example, Armingeon and Ceka
(2014, 83) observe that trust in the EU has fallen considerably over the course of the Great
Recession, particularly in Greece. They argue that this can largely be explained by falling support for national governments (103). In a multilevel analysis of European Social Survey data
from 2002, Kumlin (2009, 416) found a link between support for further European integration
and both national public service dissatisfaction and national social spending. In particular, he
found that both greater public service dissatisfaction and national social spending were associated with reduced integration support. He also found an interaction between these effects, so
that the effect of public service dissatisfaction was even stronger in countries with high social
spending. Mau (2005) looked at support for EU membership and for social policy-making at
the European level. Most relevantly for this study, he found that support for EU membership
was greater among those with higher socioeconomic status (79). Garry and Tilley (2014) argue that European citizens are more likely to favour European integration when they perceive
that EU policies are preferable to those of the national government. For example, left-leaning
citizens in countries where the prevailing consensus is right-leaning ought to be more supportive of the EU than right-leaning citizens. Their analysis of the 2009 EES survey data supports
this theory. While none of these studies address this hypothesis directly, they do add weight to
the idea that any decline in economic wellbeing ought to be linked to a reduction in support
for the EU.
Furthermore, it would be expected that this decline in support would be concentrated
among those voters holding a pessimistic assessment of the economy. The reason for this is
that optimistic voters presumably believe that the current economic policy is effective, whereas
pessimistic voters would be more critical. Since this relationship has already been hypothesised to exist, it is expected that it would become stronger during the post-crisis period. The
third hypothesis is thus:
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CHAPTER 7. ATTITUDES TOWARDS EUROPEAN INTEGRATION AND INSTITUTIONS
Hypothesis 7.3 The effect of economic assessment on support for both EU membership and further European integration was strengthened between 2009 and 2014.
The final hypothesis concerns the way citizens allocate responsibility for the economy
between their national governments and the European Union. If voters did hold the EU to
some degree responsible for the austerity policies that were put in place following the recession, then it would be expected that more responsibility for the economy was attributed to
the EU during this period than beforehand. Another way of measuring the allocation of responsibility is to look at the difference between how responsible an individual holds the EU
and how responsible that same individual holds the national government for the condition of
the economy. This then gives an indication of where that individual believes the balance of
responsibility between the two institutions lies. For the same reasons as before, it is expected
that this balance would have shifted towards the EU during the post-recession period. The
final hypothesis is therefore:
Hypothesis 7.4 More economic responsibility was attributed to the EU, both in absolute terms
and relative to that attributed to national governments, in 2014 than in 2009.
Hobolt and Tilley (2014, 33–34) looked at the responsibility questions in the 2009 EES survey,
including those for non-economic areas of responsibility and they also conducted an expert
survey asking the same questions. They noted that citizens tended to attribute more responsibility for the economy to the EU than experts did (35). While both citizens and experts show
some awareness of the various competencies of the EU and of national governments, the complexity of EU structures seems to make it difficult for people to make a definitive attribution of
responsibility (44-45). This is concerning because it means that European voters’ perceptions
of responsibility do not accord with the reality of which institutions hold the relevant powers
and this discord is likely to damage long-term trust in those institutions (147). This hypothesis
offers an opportunity to further this analysis with respect to responsibility for the economy by
examining whether voters attributed responsibility differently following the implementation
of austerity policies in so many countries.
7.4
Attitudes towards further European integration
The first three hypotheses all concern attitudes towards continuing European integration as
well as EU membership. This section focuses on integration, with the following section discussing membership. In order to test these hypotheses, a model was constructed to explain
7.4. ATTITUDES TOWARDS FURTHER EUROPEAN INTEGRATION
171
attitude towards European unification in terms of a number of relevant predictors. As in previous chapters, a multilevel model has been used so as to take into account the hierarchical
structure of the data. In particular, it is believed that two individuals randomly selected from
within a single country are more likely to have similar attitudes towards the EU than two individuals randomly selected from the entire EU. In order to take this into account, a two-level
model has been built in which individuals are nested within countries.
Model 7A describes a voter’s attitude towards European integration chiefly in terms of time,
prospective economic assessment and left–right position. As has been the case throughout
this thesis, time is modelled as a categorical variable so as not to assume anything about the
pattern of change in the dependent variable across the years under study. This means that
two dummy variables are included in the model to represent the 2009 and 2014 waves of the
survey respectively, with the 2004 wave being the base case. Prospective economic assessment
is included in the model in order to test the first hypothesis, namely that voters with a more
positive assessment are likely to be more in favour of European unification. The voter’s left–
right position is included as a predictor because it is likely that a voter’s assessment of the
economy is influenced by their own political beliefs. In particular, austerity policies are likely
to be more palatable to voters on the right than those on the left. Controlling for left–right
position makes it possible to examine these effects separately and ensure that any relationship
found between support for integration and prospective economic assessment is not merely
an artefact of the voter’s ideology. Left–right position is modelled quadratically to account
for the possibility of a curvilinear relationship, like those found between voter support for
parties and the left–right position of those parties in Chapter 5. Indeed, it has been argued
that Eurosceptic positions should be more common among the far left and the far right, simply
because policies made at the European level will generally represent centrist preferences (Hix
2007, 136–137). The modelled quadratic relationship can account for this.
This model additionally includes variables controlling for a number of demographic variables. As in previous chapters, these variables are age, gender, level of education, urban
density and workforce status. This choice of controls has been informed by previous research
showing that many of these factors are related to attitudes towards the EU. It is well-known,
for example, that Euroscepticism is more prevalent among people with lower levels of education than among the highly educated (Hakhverdian et al. 2013, 523). Hakhverdian et al.
(2013, 526–529) argue that this effect ought to have become stronger over time as a result of
continuing market integration, the development of organised Eurosceptic political parties and
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CHAPTER 7. ATTITUDES TOWARDS EUROPEAN INTEGRATION AND INSTITUTIONS
weakened national sovereignty. Using Eurobarometer data from 1973 to 2010, they show that
there has indeed been an increasing educational gap in Euroscepticism since the signing of the
Maastricht Treaty (531–535). Other political beliefs are also believed to be related. For example, C. J. Anderson (1998, 574–575) argues that people are not sufficiently well-informed
about international politics, including EU politics, for direct questions about attitudes towards
European integration to be independent of domestic attitudes. He proposes that models of
public opinion regarding the integration process control for other relevant political factors
(594). Similarly, Tillman (2013) argues that individuals holding authoritarian values are less
likely to support European integration, supporting this argument with data from the 2008
wave of the European Values Survey. Domestic political beliefs are accounted for by the inclusion of the individual’s left–right position in the model.
The model also includes interactions between time and each of the other variables. Without
these interactions the model would assume a static relationship between the dependent and
independent variables, which is unrealistic. The interaction between time and prospective economic assessment in particular is necessary in order to test the third hypothesis, which asserts
that this relationship has strengthened between 2009 and 2014. As different subpopulations
are likely to have been affected to different degrees by both the recession and the austerity
policies that followed it, it also makes sense to allow for the possibility that the level of support for European integration has evolved in different ways among those different groups.
Including interactions between time and each of the control variables achieves this goal.
Finally, as this is a multilevel model, it was necessary to determine which, if any, random
slopes should be included in addition to the random intercept for the country. Random slopes
were included for the two time dummy variables, as it expected that there are country-specific
circumstances that would cause the level of support for unification to evolve in different ways
in different countries. Random slopes were also included for the economic assessment term
and its interaction with the time dummy variables. Random slopes were not included for any
of the controls. The reason for this was that the estimated variance of these random slopes was
very small, which can lead to convergence problems and also means that they do not improve
the model particularly.
This model has been used to predict an individual’s attitude towards further European
integration based on his or her prospective economic assessment.4 These predictions are displayed in Figure 7.1, which shows that support for unification progressively decayed across
4
Unless otherwise specified, all predictions are for a 40-year-old employed male having completed high school
but not university and living in a mid-sized town.
7.4. ATTITUDES TOWARDS FURTHER EUROPEAN INTEGRATION
173
Figure 7.1: Support for European integration by prospective economic assessment
support for integration
6.0
5.5
year
2004
5.0
2009
2014
4.5
4.0
3.5
-2
-1
0
1
prospective economic assessment
2
Predicted support for European integration in each survey year according to the individual’s
prospective economic assessment, ranging from −2 for a highly pessimistic assessment to +2
for a highly optimistic assessment. These predictions are for an individual holding a left–right
position in the centre of the spectrum. Source: EES
the survey years irrespective of economic assessment. For a voter with a neutral economic assessment, this decay in support amounts to a 0.41 point (SE = 0.14, p < 0.01) drop between
2004 and 2009 with a further 0.70 point (SE = 0.14, p < 0.001) drop by 2014. From this
plot, it is also apparent that a more optimistic economic assessment is associated with a greater
support for further European unification, although the shallower slope of the 2009 line suggests that this effect may have been weaker then than in the other two years. The size of
this effect in a given year can be summarised by the difference in the predicted support for
further integration of an individual holding a strongly optimistic and one holding a strongly
pessimistic economic assessment. This is the greatest change in support that can be attributed
to variation in economic assessment. This amounts to 1.49 points (SE = 1.49, p < 0.001) in
2004, 0.91 points (SE = 0.17, p < 0.001) in 2009 and 1.89 points (SE = 0.24, p < 0.001) in
2014. As the plot suggests, the effect size in 2009 is significantly smaller than in both 2004
(∆ = 0.58 points, SE = 0.19, p < 0.01) and 2014 (∆ = 0.99 points, SE = 0.32, p < 0.01). The
difference between 2004 and 2014 is not significant (∆ = 0.41 points, SE = 0.29, p = 0.16).
Figure 7.2 shows the relationship between a voter’s left–right position and that voter’s predicted level of support for further European integration. Comparing to Figure 7.1 also shows
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CHAPTER 7. ATTITUDES TOWARDS EUROPEAN INTEGRATION AND INSTITUTIONS
Figure 7.2: Support for European integration by left–right position
5.8
support for integration
5.6
5.4
5.2
year
2004
5.0
2009
2014
4.8
4.6
4.4
0
2
4
6
left–right position
8
10
Predicted support for European integration in each survey year according to the individual’s
assessment of their own left–right position, ranging from 0 (left) to 10 (right). These predictions are for an individual holding a neutral prospective economic assessment. Source: EES
that the effect of left–right position is very modest compared to that of prospective economic
assessment. Once again, it is clear that the level of support has generally declined across the
survey years. It also appears that support has decayed more among right-wing voters than leftwing voters. In 2004 and 2009, it appears that left-wing and right-wing voters are both more
supportive of integration than those in the centre, with right-wing voters perhaps slightly more
supportive than left-wing voters. By 2014, this pattern has changed, with left-wing voters the
most supportive of further integration and right-wing voters the least supportive in an approximately linear relationship.
In order to confirm the relationships suggested by this figure, it is helpful to derive numeric
estimates of the quantities of interest as well as estimates of their statistical significance. In this
case, there are two key quantities of interest. The first is the degree, if any, to which right-wing
voters are more likely to support integration than left-wing voters.5 This directional tendency
is positive if voters on the right are more supportive than voters on the left and negative if
the opposite is the case. In 2004, the directional tendency was +0.44 (SE = 0.22, p = 0.05),
5
The measurement of both directional tendency and curvature is discussed in more detail in Chapter 5. In
summary, directional tendency is the difference between the definite integrals of the two halves of the curve and
curvature is the second derivative of the curve function, which in the case of a quadratic is a constant.
7.4. ATTITUDES TOWARDS FURTHER EUROPEAN INTEGRATION
175
indicating that support for integration was very slightly higher among right-wing than leftwing voters. The directional tendency in 2009 was not significant at +0.08 (SE = 0.20, p =
0.71). This means that it was unlikely that there was a difference in support between those
on the left and the right in that year. In fact, the difference between the directional tendency
of the two years is also not significant (∆ = 0.37, SE = 0.30, p = 0.22). In any case, the
difference between left- and right-wing voters is either small or absent in both years. The
directional tendency for 2014 on the other hand is −0.71 (SE = 0.21, p < 0.001), which is
significantly different from the other years (∆ = 0.78, SE = 0.29, p < 0.01, compared to
2009). This is still a modest tendency but it does suggest that by 2014 European unification
had become somewhat more popular on the left than on the right, albeit less popular among
both groups than in previous years.
The second quantity of interest is the curvature of the relationship in each year. This
represents the degree to which voters near the extremes differ from those in the centre.6
A positive curvature indicates that support is greater towards the extremes and a negative
curvature indicates that support is greatest at the centre. If the curvature is zero, then the
curve is in fact a line. The estimated curvature is +0.008 (SE = 0.005, p = 0.11) in 2004,
+0.016 (SE = 0.005, p < 0.001) in 2009 and −0.002 (SE = 0.005, p = 0.76). The only year
in which this is significantly different from zero is 2009, in which there is a slight tendency
for centrist voters to be less supportive of integration than those on the left and the right.
The estimated curvature for 2009 is not significantly different from that of 2004 (∆ = 0.008,
SE = 0.007, p = 0.27) but it is significantly different from that of 2014 (∆ = 0.018, SE =
0.007, p < 0.01). Taken together, the directional tendency and curvature findings provide
evidence that there was a change in the relationship between left–right position and support
for European integration between 2009 and 2014. There is only weak evidence of a change
between 2004 and 2009. It must also be emphasised that this is a weak effect in any case.
Many of the control variables included in the model were also found to be related to
support for unification. As the effect sizes are small for these variables, the results will be
summarised briefly. Older people appear to be very slightly less supportive of European integration than younger people, which each additional decade of age associated with a 0.060
point (SE = 0.015, p < 0.001) decrease in support in 2004. The size of this effect was not
significantly different in the other years, being 0.012 points (SE = 0.021, p = 0.58) stronger
6
The ‘centre’ in this case is not necessarily the centre of the political spectrum but rather the vertex of the
curve. If, however, the directional tendency is close to zero, which has been shown to be the case, then the vertex
will be close to the point corresponding to the political centre. The location of the vertex can also be visually
ascertained from the plot.
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CHAPTER 7. ATTITUDES TOWARDS EUROPEAN INTEGRATION AND INSTITUTIONS
in 2009 and 0.016 points (SE = 0.021, p = 0.44) stronger in 2014 per decade of age than
in 2004. Gender also played a very modest role, with women being 0.21 points (SE = 0.04,
p < 0.001) less supportive of integration than men in 2004, 0.13 points (SE = 0.04, p < 0.01)
less in 2009 and 0.14 points (SE = 0.04, p < 0.01) less in 2014, all other things being equal.
Once again, the differences between the years were not significant.
Education played a somewhat stronger role. There were three levels of education modelled, indicating the highest level of education attained. This is a university degree in the case
of high education, a high school certificate in the case of medium education and neither of the
above in the case of low education. There was a significant difference in support for European
integration among these educational groups in each year, with higher education consistently
associated with greater support. In 2004, an individual with medium education was predicted
to be 0.36 points (SE = 0.06, p < 0.001) more supportive of unification than an individual
with low education. An individual with high education was predicted to be a further 0.37
points (SE = 0.05, p < 0.001) more supportive. These relationships were not significantly
different in the other years, except that the predictive difference between low and medium
levels of education was 0.20 points (SE = 0.09, p = 0.02) smaller in 2009 and 2004. This
particular result is likely to be statistical noise, given that the error is relatively high compared
to the difference and that the other differences are not significant.
Unlike education, the importance of urban density did appear to change over time. In
2004, voters living in urban areas were the most supportive of European integration. An urban
voter was predicted to be 0.21 points (SE = 0.06, p < 0.001) more supportive than a voter
living in a town and 0.31 points (SE = 0.05, p < 0.001) more supportive than a rural voter. By
2009, this gap had closed slightly, so that an urban voter was only 0.12 points SE = 0.05, p =
0.02 points more supportive than a rural voter, a 0.19 point (SE = 0.07, p = 0.01) difference.
By 2014, the direction had changed, so that rural voters were now more 0.16 points (SE =
0.06, p < 0.01) more supportive of integration than urban voters, a 0.28 point (SE = 0.08,
p < 0.001) change from 2004.
Lastly, workforce status is a factor that would very much be expected to have some influence. In particular, it is expected that unemployed respondents would be harsher critics of
European institutions than the employed or those not in the workforce. In fact, the estimated
effect size of workforce status, all other things being equal, is very low and for the most part
not significant. In 2004, unemployed voters are not significantly different from either employed voters (∆ = 0.087, SE = 0.095, p = 0.36) or those not in the workforce (∆ = 0.076,
7.5. POPULAR SUPPORT FOR EU MEMBERSHIP
177
SE = 0.102, p = 0.46). Employed people are predicted to be 0.16 points (SE = 0.05, p < 0.01)
more supportive of further integration than those not in the workforce. Although these point
estimates do differ slightly in the other years, none of the differences are significant.
As this is a multilevel model, there are also estimates of the variances and covariances of
the random effect terms. The random intercept accounts for 45 percent of the country-level
variance and the random slopes for the time dummy variables account for a further 42 percent.
That is, the bulk of the country-level variance was related to variation in the underlying level
of support for European integration in each country and how that evolved over time. Only
13 percent of that variance was related to variation in the effect of prospective economic
assessment in different countries. There is a moderate negative correlation (−0.50) between
the random intercept and the slope for the time dummy variable indicating the year 2009. This
indicates that countries that had high levels of support for unification in 2004 had typically
lost some support by 2009 and those with low levels had gained some. Similarly, there is
a weak negative correlation (−0.24) between the effective level of support in 2009 and the
difference between 2009 and 2014, indicating a similar pattern between those two years. This
is consistent with a regression to the mean over time. Combined, the country-level random
effects account for 16 percent of the total residual variance in the model. This means that
most of the variation is actually between individuals rather than between countries.
In summary, support for further European integration was strongest in 2004, decaying
between 2004 and 2009 and particularly between 2009 and 2014. Voters holding optimistic
economic assessments were typically more supportive of integration than those holding pessimistic assessments, although the size of this effect was depressed in 2009 before recovering
in 2014. A voter’s left–right position had only a small effect on his or her level of support but
there was a slight tendency for right-wing voters to be more pro-integration than left-wing
voters in 2004, a tendency which had reversed by 2014. These results are consistent with this
chapter’s first three hypotheses, which predicted a positive relationship between economic
assessment and support for integration, becoming stronger after 2009, as well as an overall
decline in support for integration after 2009.
7.5
Popular support for EU membership
Another potential indicator of satisfaction or dissatisfaction with the European Union is whether
or not voters believe that EU membership is a good thing for their country. The same set of
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CHAPTER 7. ATTITUDES TOWARDS EUROPEAN INTEGRATION AND INSTITUTIONS
hypotheses that applied to attitudes towards European integration also apply to attitudes towards EU membership. That is, it is expected that EU membership was seen less positively
after 2009 and that there is a positive relationship between prospective economic assessment
and attitude towards EU membership, which became stronger after 2009. Model 7B was constructed in order to test these hypotheses. This variable only has three levels of measurement,
indicating a positive view, a negative view and a neutral view of EU membership. As a result, it
is preferable to use a model that assumes an ordinal scale rather then the stronger assumption
of an interval scale. To this end, a multilevel ordinal logit model has been used to model the
likelihood of individuals believing that EU membership is a good thing or a bad thing for their
country.
The selection of predictors modelled is the same as for the integration model. This reflects
the fact that both models seek to test the same hypotheses. Once again, the key predictors
are time, prospective economic assessment and left–right position, which is modelled quadratically. Age, gender, education, urban density and workforce status are included as controls.
Time is represented by two dummy variables, one for the year 2009 and one for 2014, with
2004 the base case. These dummy variables are interacted with each of the other variables so
as to allow for variation in the strength of those effects over time. As before, this is a multilevel model, with individuals nested within countries. As well as the random intercept, this
model includes random slopes for economic assessment, the time dummy variables and their
interactions.
This model was estimated and post-estimation simulation was used to predict the probabilities of an individual evaluating EU membership positively, negatively or neither in each
survey year. Figure 7.3 shows these predicted probabilities for an individual with a neutral
prospective economic assessment and who sits in the centre of the political spectrum. This
figure shows that the majority of voters evaluated the EU positively in all three years, ranging
from 55 percent [50%, 61%]7 in 2014 to 68 percent [61%, 74%] in 2009. Negative evaluations were comparatively rare, ranging from 7.6 percent [5.8%, 9.9%] in 2009 to 13 percent
[10%, 15%] in 2014. As the figure shows, EU membership was seen more positively in 2009,
during the initial crisis, than it was before or afterwards. Voters in 2009 were thirteen percent
more likely (odds ratio 1.39, CI 1.04–1.86) than in 2004 and eight percent less likely (OR 0.59,
CI 0.46–0.76) than in 2014 to see EU membership as good for their country. It appears from
7
Square brackets indicate 95% confidence intervals.
7.5. POPULAR SUPPORT FOR EU MEMBERSHIP
179
Figure 7.3: Evaluation of EU membership over time
0.7
predicted probability
0.6
0.5
evaluation
good
0.4
neither
bad
0.3
0.2
0.1
2004
2009
year
2014
Predicted probabilities of an individual holding a particular evaluation of EU membership in
each year. These possible evaluations are that EU membership is good for that individual’s
country, bad for the country, or neither good nor bad. These predictions are for an individual
holding a neutral economic assessment and a left–right position in the centre of the spectrum.
Source: EES
the figure as though assessments of EU membership were less positive in 2014 than in 2004
but this difference is not significant (OR 0.92, CI 0.79–1.04).8
Figure 7.4 shows the relationship between an individual’s prospective economic assessment and that individual’s probability of evaluating EU membership positively, assuming a
neutral assessment of the economy. From this point onwards, focus will be limited to the
predicted probability of a ‘good’ response. This is because the relationships between the probabilities of the various responses have been modelled as independent of the predictors,9 so
examining the other responses separately would not affect the inferences that could be drawn.
As the figure shows, there is a strong relationship between prospective economic assessment
and the likelihood of a positive evaluation of EU membership. An individual who was highly
optimistic about the economy was more than twice as likely (OR 5.82, CI 3.76–8.95) to evaluate EU membership positively in 2004 than one who was highly pessimistic about the economy.
8
As in the previous chapter, the percentage differences reported in the text are relative probabilities, with odds
ratios and confidence intervals given alongside in brackets.
9
In particular, the likelihood of the ‘bad’ response is given by odds(bad) = k/ odds(good) where k =
0.172 [0.168, 0.177] was estimated from the data. As the three possible responses are mutually exclusive, this
gives enough information to infer the likelihood of the ‘neither’ response: P(neither) = 1 − P(good) − P(bad).
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CHAPTER 7. ATTITUDES TOWARDS EUROPEAN INTEGRATION AND INSTITUTIONS
Figure 7.4: Positive evaluation of EU membership by prospective economic assessment
0.8
predicted probability
0.7
year
0.6
2004
2009
0.5
2014
0.4
0.3
-2
-1
0
1
prospective economic assessment
2
Predicted probability of an individual evaluating EU membership as good for his or her country.
Probabilities are shown according to the survey year and the individual’s prospective economic
assessment, ranging from −2 for a highly pessimistic assessment to +2 for a highly optimistic
assessment. These predictions are for an individual holding a left–right position in the centre
of the spectrum. Source: EES
This effect became significantly weaker (OR 0.60, CI 0.43–0.86) in 2009, when optimists were
only one and a half times as likely (OR 3.52, CI 2.67–4.61) as pessimists to see EU membership
positively. By 2014, this effect had strengthened once again (OR 3.09, CI 1.81–5.26), becoming a three-fold difference (OR 10.9, CI 7.51–15.6) between optimists and pessimists. The
difference between the 2004 and 2014 effect sizes is not quite significant (OR 1.87, CI 0.95–
3.66). This figure also shows that most of the divergence over time occurred among pessimistic
voters, with optimistic voters remaining quite consistent.
Figure 7.5 shows how the predicted probability of a positive evaluation of EU membership
varies according to the individual’s reported left–right position. It is clear from this figure
that those on the right typically see EU membership more positively than those on the left.
Another thing that this figure shows is that the size of this effect is greatest in 2009. Comparing this figure with Figure 7.4 also shows that left–right position has a smaller impact on
this evaluation than economic assessment does. As this relationship is modelled quadratically, it will be summarised using the same measures of directional tendency and curvature as
previously, although the underlying units in this case are on the logit scale. The directional
7.5. POPULAR SUPPORT FOR EU MEMBERSHIP
181
Figure 7.5: Positive evaluation of EU membership by left–right position
predicted probability
0.70
0.65
year
2004
0.60
2009
2014
0.55
0.50
0
2
4
6
left–right position
8
10
Predicted probability of an individual evaluating EU membership as good for his or her country. Probabilities are shown according to the survey year and the individual’s assessment of
their own left–right position, ranging from 0 (left) to 10 (right). These predictions are for an
individual holding a neutral prospective economic assessment. Source: EES
tendency was +0.72 (SE = 0.16, p < 0.001) in 2004, +1.26 (SE = 0.15, p < 0.001) in 2009
and +0.68 (SE = 0.15, p < 0.001) in 2014. This confirms that the probability of a positive
evaluation was greater among right-wing voters than left-wing voters in all years. The pattern
over time was that this imbalance increased (∆ = 0.54, SE = 0.22, p = 0.01) from 2004 to
2009 before decreasing again (∆ = 0.58, SE = 0.22, p < 0.01) by 2014 to very close to its
original level (∆ = 0.04, SE = 0.22, p = 0.86). As for curvature, there was a small negative
curvature (−0.010, SE = 0.004, p < 0.01) in 2009, indicating a slight tendency for those in
the centre to see EU membership more positively than those at the extremes. This was very
close to zero in both 2004 (+0.004, SE = 0.004, p = 0.25) and 2014 (−0.004, SE = 0.004,
p = 0.32), indicating that the relationship in those years was approximately linear.
Some of the control variables are much stronger predictors of an individual’s assessment
of EU membership than others. The effect of age, for example, on the probability of a positive
assessment is not significantly different from zero (OR 1.00, CI 0.98–1.03) in 2004, nor is the
effect significantly stronger in either of the other years. Men were approximately ten percent
more likely (OR 1.24, CI 1.17–1.32) than women to assess EU membership positively in 2004.
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CHAPTER 7. ATTITUDES TOWARDS EUROPEAN INTEGRATION AND INSTITUTIONS
This effect was not significantly different in 2009 but by 2014 it had weakened (OR 0.87,
CI 0.80–0.95) to a four percent increase in probability (OR 1.08, CI 1.02–1.15). In general,
higher levels of education were associated with a more positive assessment of EU membership.
In 2004, voters with a university education were 35 percent more likely (OR 2.16, CI 1.98–
2.37) to see EU membership positively than voters who had not completed high school. This
difference grew (OR 1.27, CI 1.12–1.44) between 2004 and 2009, to a 37 percent increase
in probability (OR 2.75, CI 2.52–3.00).10 There was no significant difference between 2009
and 2014 (OR 0.97, CI 0.86–1.11). Similarly, voters interviewed in areas of higher population
density are more likely to assess EU membership positively. In 2004, voters in cities were
eleven percent more likely (OR 1.29, CI 1.20–1.40) to do so than voters living in rural areas.
This effect was about the same in 2009 but by 2014 had weakened (OR 0.78, CI 0.69–0.87)
such that there was no longer a statistically significant difference between rural areas and
cities (OR 1.01, CI 0.93–1.09). There was also a small workforce status effect. In 2004, voters
who were unemployed were seven percent less likely (OR 0.83, CI 0.73–0.95) and voters
who were neither employed nor unemployed—frequently retirees—were four percent more
likely (OR 1.11, CI 1.03–1.19) to evaluate EU membership positively than those who were
employed. These effects were remarkably consistent over time, with no significant differences
between any pair of years.
As this is a multilevel model, some things can also be said about the variances and covariances of the random effects terms. There is once again a moderate negative correlation (−0.41) between the random intercept and the random slope for the 2009 time dummy
variable, which indicates that in those countries where assessments of EU memberships were
highly positive or highly negative in 2004 tended to be have more moderate assessments in
2009. Similarly, there is a strong negative correlation (−0.66) between the 2014 time dummy
variable and the effective 2009 level for each country. As this is consistent with a regression to
the mean effect, it suggests that the measured country-specific levels of support owe much to
chance. The remaining random slopes explain only sixteen percent of the total country-level
variance. Unfortunately, with an ordered logit model such as this, the concept of residual
variance is not well-defined, so it is difficult to talk about the total proportion of the variance
explained by the random effects.
In summary, EU membership is seen by most citizens as good, or at least neutral, for their
respective countries. Support for the idea that EU membership is a good thing peaked in 2009,
10
The reason that the considerably higher odds ratio only corresponds to a small increase in relative probability
is that the base probability is higher in 2009 than in 2004.
7.6. ATTRIBUTION OF RESPONSIBILITY FOR THE ECONOMY
183
during the initial phase of the Great Recession, before returning to roughly its 2004 levels by
2014. An optimistic economic assessment was a strong predictor of a positive evaluation of EU
membership in all years. Those holding a pessimistic economic assessment were most critical
of the EU in 2014 and least critical in 2009. Left–right position also had a modest influence,
with those on right generally being more positive in their assessment than those on the left,
especially in 2009. These results are consistent with the three hypotheses this model was
constructed to test, which were that support for EU membership fell between 2009 and 2014,
that support for EU membership was positively linked to economic assessment, and that the
strength of that relationship grew between 2009 and 2014.
7.6
Attribution of responsibility for the economy
The final hypothesis concerns the attribution of responsibility for the economy between the
European Union and the appropriate national government. This hypothesis states that the EU
was held more responsible in 2014 than it was in 2009, both in absolute terms and relative
to national governments. A model was constructed in order to test this hypothesis. The dependent variable in Model 7C is the level of responsibility a particular individual attributes
to a particular institution on an eleven-point scale and a dummy predictor variable indicates
whether the institution is the EU or the national government. In other respects this model
is similar to the earlier model measuring support for European integration. Like that model,
this one includes as predictors the individual’s prospective economic assessment, left–right
position, age, gender and level of education. It also includes a time dummy variable, which is
interacted with each of those other variables. Unlike the other model, this one only includes a
single time variable because there is no data available from 2004. The base year in this model
is 2009 and the time dummy variable indicates the year 2014. Furthermore, each other variable, including the interactions, is interacted with the institutional dummy variable. This was
done so as not to assume that any of those variables affect the amount of responsibility accorded to the different institutions in precisely the same way. Finally, like the other models
in this chapter, this is a multilevel model with individuals nested within countries. Random
slopes have been included for, as previously, time and prospective economic assessment and,
additionally, their interactions with the institution dummy variable.
According to this model, a typical centrist voter with a neutral prospective economic assessment would have held the government more responsible than the EU for the economy in
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CHAPTER 7. ATTITUDES TOWARDS EUROPEAN INTEGRATION AND INSTITUTIONS
Figure 7.6: Attribution of economic responsibility by prospective economic assessment
2009
2014
predicted responsibility
7.5
7.0
6.5
6.0
5.5
-2
-1
0
1
2
-2
-1
0
prospective economic assessment
institution
national government
1
2
European Union
Predicted level of responsibility attributed to the national government and to the European
Union in 2009 and 2014 according to the individual’s prospective economic assessment, which
ranges from −2 for a highly pessimistic assessment to +2 for a highly optimistic assessment.
These predictions are for an individual holding a left–right position in the centre of the spectrum. Source: EES
both years. The predicted responsibility scores that this voter would have assigned the government were 7.15 (SE = 0.13, p < 0.001) in 2009 and 7.41 (SE = 0.12, p < 0.001) in 2014.
The difference between the two years is not significant (∆ = 0.25, SE = 0.17, p = 0.13).
The predicted scores assigned to the EU were 5.58 (SE = 0.10, p < 0.001) in 2009 and
6.37 (SE = 0.13, p < 0.001) in 2014. This means that the responsibility attributed to the EU
increased by 0.79 points (SE = 0.10, p < 0.001) between the two years. In relative terms,
the responsibility attributed to the EU in 2009 was 1.58 points (SE = 0.16, p < 0.001) less
than that attributed to the national government, whereas by 2014 this difference was only
1.04 points (SE = 0.12, p < 0.001; ∆ = 0.53, SE = 0.17, p < 0.01). In other words, this class
of voter generally considered national governments to be more responsible for the economy
than the EU but this gap has closed somewhat over time as voters have started to attribute a
greater amount of responsibility to the EU.
7.6. ATTRIBUTION OF RESPONSIBILITY FOR THE ECONOMY
185
Figure 7.7: Attribution of economic responsibility by left–right position
2009
2014
predicted responsibility
7.5
7.0
6.5
6.0
5.5
0
2
4
institution
6
8
10
0
2
left–right position
national government
4
6
8
10
European Union
Predicted level of responsibility attributed to the national government and to the European
Union in 2009 and 2014 according to the individual’s assessment of their own left–right position, ranging from 0 (left) to 10 (right). These predictions are for an individual holding a
neutral prospective economic assessment. Source: EES
Figure 7.6 shows how these predicted responsibility scores vary for voters with differing
economic assessments. The first thing that stands out in this figure is that voters in 2014 were
generally more likely to hold the EU responsible for economic conditions than voters in 2004.
It is also clear that in 2009, individuals who were highly optimistic about the economy typically
assigned national governments less responsibility for the economy than those who were highly
pessimistic. This effect accounts for 0.63 points (SE = 0.24, p < 0.01) of difference. It appears
from the plot that there was a similar, albeit weaker, effect for the responsibility attributed to
the EU but this effect was not statistically significant (0.12 points, SE = 0.15, p = 0.43), nor
was there a significant effect in 2014 (0.14 points, SE = 0.25, p = 0.59). This effect had
also weakened (∆ = 0.79, SE = 0.31, p < 0.01) for governments so that it was no longer
significant (−0.16 points, SE = 0.32, p = 0.61) by 2014.
Figure 7.7 shows the relationship between a voter’s left–right position and that individual’s
predicted responsibility scores under this model, assuming in this case a neutral economic
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CHAPTER 7. ATTITUDES TOWARDS EUROPEAN INTEGRATION AND INSTITUTIONS
assessment. One thing that stands out in this figure, as in the last, is that all groups of voters
held the European Union more responsible for the economy than they did in 2009. It also
appears that right-wing voters in 2009 were more likely to hold governments accountable for
the condition of the economy and that otherwise voters on both the left and the right are
more inclined to assign higher responsibility scores than voters in the centre. The directional
tendency for government responsibility was +1.03 (SE = 0.17, p < 0.001) in 2009 but had
fallen (∆ = 0.90, SE = 0.25, p < 0.001) to almost zero (+0.13, SE = 0.18, p = 0.47)
in 2014. This confirms the impression that right-wing voters tended to hold governments
more responsible for the economy than left-wing voters but only in 2009. The directional
tendency for EU responsibility was positive but not quite significant at +0.32 (SE = 0.17,
p = 0.06) in 2009 and not significant in 2014 (+0.15, SE = 0.24, p < 0.01). The curvature
was positive in all cases, as the figure suggests. Specifically, for government responsibility, this
was +0.009 (SE = 0.004, p = 0.03) in 2009 and +0.010 (SE = 0.004, p = 0.01) in 2014
and for EU responsibility, +0.018 (SE = 0.004, p < 0.001) in 2009 and +0.020 (SE = 0.004,
p < 0.001) in 2014. These figures confirm that centrist voters were somewhat less inclined to
assign high responsibility scores than voters on the left and right. This may simply be a result
of higher political interest among those voters who identify as being further from the centre.
As the control variables are of less substantive interest, their analysis will be limited to their
effect on the relative responsibility attributed to national governments over the EU. In general,
older people are more likely to attribute economy responsibility to their national governments
than the EU, although the effect is very small, amounting to an extra 0.04 points (SE = 0.02,
p = 0.01) of difference in 2009 and a similar amount in 2014. Gender had a stronger effect,
with men’s relative responsibility scores 0.23 points (SE = 0.05, p < 0.001) greater than
women’s in 2009. By 2014 however, this effect had declined in magnitude (∆ = 0.15, SE =
0.07, p = 0.03) so that it was no longer significant. In 2009, higher levels of education were
associated with more responsibility assigned to the government over the EU, there being a
0.36 point (SE = 0.07, p < 0.001) difference between university educated voters and those
who did not complete high school. By 2014, there was no longer a significant difference
between these groups (∆ = −0.29, SE = 0.11, p < 0.01). Urban density did not have a
significant effect in 2009 but by 2014 a pattern had emerged in which those living in urban
areas attributed more responsibility to the EU relative to the national government than those
in rural areas. This amounted to a 0.18 point (SE = 0.07, p < 0.01) difference between
cities and rural areas. Lastly, there was also a mild workforce status effect in 2009, when
7.7. CONCLUSION
187
employed voters attributed more responsibility to the government relative to the EU than both
unemployed voters (∆ = 0.28, SE = 0.11, p < 0.01) and those not in the workforce (∆ = 0.14,
SE = 0.06, p = 0.02). These effects were not significant in 2014 but the differences between
the years were also not significant.
The random effects account for 26 percent of the total variance in this model, which is
considerably more than in the European integration model. This suggests that country-level
differences are more important influences on beliefs about who is responsible for the economy than on attitudes towards continuing integration. There is a strong negative correlation (−0.77) between the random intercept and the random slope for the institution dummy
variable. This indicates that, in 2009, countries where the government was seen as highly
responsible for the economy tended to see the EU as less responsible and vice versa. There
is also a strong negative correlation (−0.68) between the time random slope and the random
intercept and a similar correlation (−0.73) between the time random slope and the random
slope for the time–institution interaction. These are both consistent with regression to the
mean. The institution and time dummy variables along with their interactions account for 87
percent of the country-level variance.
These results support the fourth hypothesis, which states that the level of responsibility
for the economy attributed to the EU increased between 2009 and 2014 and that additionally the balance between the responsibility assigned to the EU and to national governments
shifted towards the EU over the same time period. The post-estimation predictions from this
model show that these patterns are indeed observable. It was somewhat surprising to see that
prospective economic assessment played only a minor role in the attribution of responsibility, really only being relevant for the EU and in 2009 and even then on a relatively modest
scale. The reason this is surprising is that this variable has been shown to be an important
predictor in the other models not just in this chapter but throughout this thesis, predicting
party preference and turnout likelihood as well as attitudes towards European unification EU
membership. Based on this model it appears that voters’ attribution of responsibility between
the institutions of government and the EU is, unlike their voting behaviour, relatively robust
to their assessment of the economy.
7.7
Conclusion
A recurring pattern has emerged in the preceding chapters, which is that voters’ initial response to the Great Recession was muted, with the strongest effects taking place well after
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CHAPTER 7. ATTITUDES TOWARDS EUROPEAN INTEGRATION AND INSTITUTIONS
the initial shock. This pattern suggests that European voters may be responding more to the
political reaction to the crisis—the austerity programmes implemented in its wake—than to
the recession itself. If this were indeed the case, then there ought to be evidence of a change
in voters’ attitudes towards the EU in the years since the recession, since it played a direct role
in introducing austerity into four countries and in indirect role in the remaining countries,
owing to the Stability Growth Pact, as discussed earlier in the chapter. By testing four hypotheses, this chapter has shown that there was such a change in attitudes. The first of these
was that voters who have an optimistic view of the economy are more likely to see both EU
membership and further European unification positively. This was supported by the data, with
both the membership and integration models showing a positive relationship with prospective economic assessment in all years. This is confirmation of the idea that voters’ attitudes
towards the EU are linked to their evaluation of economic conditions. This resembles the economic voting effect, except that it is not the only individual political parties that are being held
accountable but the European institutions themselves.
The second hypothesis was that this support for the EU fell between 2009 and 2014 and
the third hypothesis was that the effect of an optimistic economic assessment became stronger
over the same period. Both of these hypotheses were supported by both the membership
and integration models. Support for further integration fell between 2009 and 2014, having
previously fallen less severely between 2004 and 2009. Support for EU membership actually
rose by 2009 before falling again by 2014. The strength of the economic assessment effect in
both models fell from 2004 to 2009 before rising again by 2014. The changes between 2009
and 2014 are very different from those between 2004 and 2009. This implies that the changes
that took place in the later time period were not merely a continuation of those taking place in
the earlier period. This pattern supports the argument that the recession itself cannot entirely
explain recent European voter attitudes and behaviour. On the other hand, the economic link
in 2014 is even stronger than before and any explanation has to take account of this. These
trends are consistent with the idea that the austerity politics of the post-recession period has
had a strong impact on voters.
The final hypothesis was that the level of responsibility for the economy that voters attributed to the EU was greater in 2014 than in 2009. This was measured in two ways—both
in absolute terms and relative to that responsibility attributed to the national government.
The data supported the hypothesis under both measures. Although governments were held
more responsible than the EU in both years, this gap closed between the two years, owing to
7.7. CONCLUSION
189
an increase in the level of responsibility attributed to the EU in 2014. Unfortunately no data
for the relevant questions was available from 2004 so it is not possible to compare with that
year as in the other cases. It is interesting to note that prospective economic assessment had
little if any impact on the attribution of responsibility in 2014. That is, by that year, optimistic
and pessimistic voters were nearly in agreement on the question. Once again, these findings
support the argument that there was a shift in attitudes about the economy that took place
too late to be attributed purely to the recession. This finding also supports Hobolt and Tilley’s
(2014) argument that voters increasingly tend to blame the EU for problems it may not be
directly responsible for.
This chapter’s findings offer support to the argument that the austerity policies implemented in the immediate aftermath of the Great Recession played a larger role in affecting voters’
political behaviour and attitudes than the mere fact of the recession itself, which is one of the
central arguments of the thesis. The following and final chapter summarises the findings of
the entire thesis and discusses their implications in general and for economic voting theory in
particular.
Chapter 8
Conclusion: revising theories of economic voting
This thesis has been motivated by two key questions. First, what were voters’ electoral intentions in the wake of the Great Recession and how did these differ from their intentions at other
times? Secondly, how much of this response can be attributed to the austerity programmes
implemented in the wake of the recession, rather than the economic hardships themselves?
In summary, it was found that the economic voting response was depressed during the Great
Recession and still had not fully recovered by 2014. Voters’ economic dissatisfaction was also
expressed in other observable changes in attitudes and intentions but the most striking of these
did not take place until after 2009. By this point, the economies in most of the surveyed countries had recovered from the initial crisis but many of them had also implemented unpopular
austerity measures, suggesting that it was the political reaction to the crisis, rather than the
economic events themselves, that produced the strongest reaction from voters.
These questions were researched by using European Election Studies survey data to construct several models assessing the influence of prospective economic assessment on a voter’s
intended electoral behaviour. By contrasting responses from the 2004, 2009 and 2014 survey
waves, the thesis was able to compare these relationships at a time before the crisis, a time
when the initial recession was at its peak and a time well after the initial recession but when
many countries were still struggling with the economic consequences of the situation. In doing so, it was possible to make an argument about how these relationships evolved over the
course of the crisis. This thesis models the economic vote by using individuals’ prospective
sociotropic assessments of the economy to predict their support for the parties in their country. This means that a voter’s prospective economic perceptions are expected to influence his
or her level of support for each party—positively for for government parties and negatively
for opposition parties—and it is these levels of support which ultimately determine that individual’s vote choice. Party support is thus used as the dependent variable for the empirical
economic voting models discussed in this thesis.
191
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CHAPTER 8. CONCLUSION
A measurable economic vote was observed in all three years. This means that an optimistic economic assessment consistently improved a voter’s support for government parties and
reduced a voter’s support for opposition parties, while a pessimistic assessment had the opposite effect. The only clarity of responsibility effect involved the ideological cohesion of the
government, that is, the proportion of the government’s members of parliament who are on
the same side of the political spectrum as the prime minister. The economic vote tended to
be stronger in countries where the government was more ideologically cohesive. Other clarity
effects were tested for but not found, nor was there any evidence that the government’s time
in office affected the strength of the economic vote. The economy was also found to have an
influence on election turnout. This was a withdrawal effect, meaning that those who were
pessimistic about the economy were less likely to vote compared to those holding more optimistic views. Finally, support for both EU membership and further European integration was
higher among optimistic voters than pessimistic voters in all three years. In summary, people
who are optimistic about the economy are more likely to vote and more likely to support the
incumbent government as well as the European project, whereas pessimists are less likely to
vote, more likely to support opposition parties and more sceptical of the European project.
Several changes were observed between 2004 and 2009, during which period the Great
Recession began and, in most countries as well as the EU overall, peaked. During this period
of economic turmoil, the economic vote actually became weaker, not stronger. The mediating
effect of ideological cohesion was suppressed during this time, as was the withdrawal effect on
turnout. Support for EU membership and European integration also fell and the link between
prospective economic assessment and support for these also weakened. Voters started to prefer
parties further from the centre than they previously had. In economic terms, pessimistic voters
had tended to prefer left-leaning parties and optimistic voters right-leaning parties but this difference had become much smaller by 2009. In other words, every key link identified between
economic perceptions and voter attitudes or intentions became weaker during the Great Recession. The only stronger effect that was identified was the increase in support for parties
further from the centre.
The most striking changes were found between 2009 and 2014. This period followed the
Great Recession, although some countries did experience subsequent periods of recession, and
is largely characterised by the European debt crisis. During this time, austerity programmes
were implemented in many EU member states. In some countries, these were imposed by the
193
European Troika1 in return for access to bailout funds. Over this period, the economic vote
started to recover, although not to its pre-crisis levels. The clarity effect of ideological cohesion
was also partly restored. As for turnout, the withdrawal effect became even stronger than it
had been in 2004. On the other hand, support for the European project became much weaker
and the link between economic perceptions and this support also became stronger than it had
been before the crisis. Support increased for parties even further away from the centre than
previously and, on the social dimension, there was also an increase in support for traditionalist/authoritarian parties, especially among pessimistic voters. Whereas in previous years all
voters had preferred pro-integration parties, by 2014 there was a shift towards anti-integration
parties, particularly among pessimistic voters once again. Finally, between 2009 and 2014,
voters had become much more likely to regard the EU as responsible for economic issues than
they had previously, although still not to the same degree as their national governments.
These findings suggest that it was the political response to the crisis, in the form of austerity
programmes, rather than the recession itself, that produced the greatest change in voters’
attitudes and intentions. Other explanations were also considered but these do not fit the
findings so well. For example, one possibility is that voters wanted to see how the crisis would
play out before reacting strongly. There are two problems with this explanation, however.
First, this would imply that the twelve months that the EU had been in recession by the time
of the 2009 survey wave were not sufficient to produce a full economic voting response, in
spite of the evidence that a twelve month window is a good choice for economic voting studies
(Hellwig and Marinova 2015). Second, this idea cannot explain the increase in Euroscepticism
or the increasing popularity of the view that the EU is responsible for the economy. Similarly, it
is unlikely that voters were simply responding more forcefully after 2009 because there were
additional waves of recession. This alone could not explain the change in attitudes towards
the EU. Yet another possibility is that the increase in Euroscepticism is a coincidence, that is it
has an unrelated cause. There must be some link between this Euroscepticism and economic
issues, however, since it was predominantly pessimistic voters who became more Eurosceptic
after 2009 and 2014 was the first year in which optimistic and pessimistic voters had different
attitudes towards Eurosceptic parties. It is difficult to give an account of events that would
explain all of these findings and yet allow no role for the politics of austerity.
It must be conceded that some key findings of this thesis were foreshadowed elsewhere.
In the introduction to their edited volume on politics during the Great Recession, Bermeo and
1
The European Commission, European Central Bank and International Monetary Fund. The role of these
institutions in introducing austerity measures was discussed in Section 7.1.
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CHAPTER 8. CONCLUSION
Bartels (2014) commented that:
Voters did punish incumbents as predicted; but contrary to expectations, the combustible potential of the Great Recession was realized in only a few of the countries we studied. This observation constitutes the first of our volume’s two major
themes: in most countries, popular reactions to the Great Recession were surprisingly
muted and moderate. (3, emphasis in original)
They went on to state that:
Our analysis of these exceptional cases yields the second theme of our project:
dramatic political reactions to the Great Recession were associated less with the direct
economic repercussions of the crisis than with government initiatives to cope with
those repercussions. Radical reactions were less likely to be triggered directly by
declining growth or escalating unemployment than by the austerity and bailout
programs that policy makers adopted in response to crisis trends. (4, emphasis in
original)
In other words, the authors also found that the economic voting response to the Great Recession was surprisingly weak and that the strongest reactions appear to have been in response to
the austerity programmes implemented after the recession began. Despite not being the first
to make these observations, this thesis still makes an important contribution to this literature.
Bermeo and Bartels’s comments were based on very different evidence from that here. The
individual studies in their book were based on aggregate data and they did not use the EES
surveys but various other datasets. This thesis thus corroborates their evidence by showing
that similar conclusions can be reached using completely different data and methods.
This thesis further contributes to the economic voting literature in several other ways.
First, it provides a much-needed multinational account of individual-level behaviour during
the Great Recession. Although a number of studies of economic voting behaviour during this
period have emerged, they either study a single country or they use only aggregate data. Both
of these approaches have their place but they are also limited. Single country studies, while
useful for gaining a deep understanding of the situation in those countries, cannot necessarily
be generalised to other countries which might have different peculiar circumstances. For example, studies in Sweden (Lindvall, Martinsson and Oscarsson 2013; Martinsson 2013) found
a strengthened economic vote during the Great Recession, while studies in Portugal (Freire
195
and Santana-Pereira 2012) and Turkey (Çarkoğlu 2012) found a weakened economic vote.
By studying multiple countries together, this thesis has been able to show that it is the latter
result which predominates, at least in the EU. Similarly, aggregate studies are useful because
aggregate data is often readily available and they offer a means to compare countries but not
all changes in individual attitudes are necessarily manifested at the aggregate level. This thesis
fills this gap with an analysis that is both multinational and individual-level.
Second, this thesis also demonstrates that the novel method for measuring the economic
vote proposed by van der Brug, van der Eijk and Franklin (2007) can be replicated using newer
data. Whereas most economic voting studies use logistic regression models to predict the effect of changing economic conditions on party choice, they argued that that economic voting
could be more accurately and more reliably measured using party support as a dependent variable. According to them, this has several advantages, such as being more sensitive to shifts in
support that might not necessarily be expressed in a change in vote likelihood, a characteristic
which makes this method particularly appropriate for multiparty systems such as those prevalent throughout Europe. This thesis has adopted this method, with some key differences,
notably the use of multilevel modelling rather than regression with robust standard errors. By
showing that this method can be applied to different data yielding similar results, this thesis
has contributed further evidence in support of their argument that party support models can
correct the instability problems that have plagued party choice models.
Third, this thesis has implications for the clarity of responsibility literature. Powell and
Whitten (1993) were trying to solve the same problem with their clarity of responsibility theory as van der Brug, van der Eijk and Franklin (2007) were with their party support model
of economic voting, namely the instability problem of economic voting results. Of the many
clarity components tested in this thesis, only the ideological cohesion of the government was
shown to have any effect on the strength of the economic vote. Since these models are based
on the party support theory of economic voting, this supports van der Brug, van der Eijk and
Franklin’s argument that model misspecification may account for much of the instability of
economic voting results. It also suggests that clarity of responsibility effect may be at least
in part an artefact of this misspecification. Given the large extant literature on clarity of responsibility, these results are hardly definitive but it suggests an interesting area for further
research. A study could be designed to test whether the existence of an apparent clarity effect
depends on whether the economic vote is measured in terms of vote choice or party support.
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CHAPTER 8. CONCLUSION
The current study has also contributed to the turnout literature, particularly that part of
the literature examining the idea that, much like the economic voting effect, an individual’s
inclination to vote is affected in some way by his or her assessment of the economy. One of
the two competing theories is that, in the face of economic misfortune, people are mobilised
to vote so that they can show their dissatisfaction. An alternative theory is that people are
not mobilised but in fact too concerned with the increasingly difficult task of meeting their
daily needs to focus on more abstract concerns like politics and will as a result withdraw
from political involvement (Rosenstone 1982, 25–26). One argument for this hypothesis is
that mobilisation occurs when the economy is performing especially well or especially poorly,
owing to the increased salience of economic issues, and withdrawal occurs otherwise (Martins
and Veige 2013). The findings of the current research support the withdrawal argument,
although as the withdrawal effect was weakened during the recession, it also offers partial
support to the theory that withdrawal is the norm but that mobilisation also occurs during
extreme economic conditions.
These results also have broader implications for European politics. The finding that voters
were relatively cautious about condemning their governments for an international recession
originating beyond their borders is presumably good news for politicians, as it suggests that at
least some voters will allow them scope to react to events outside of their immediate control.
On the other hand, the strong reaction against the austerity programmes introduced after the
crisis also suggests that there are limits to the policy measures that their citizens are willing to
tolerate, even in response to such a crisis. That voters would object to pension and pay cuts,
tax hikes and the loss of government services is not on the face of it surprising but this idea
also appears to stand in contrast to traditional accounts of the economic vote, which argue
that voters are not concerned with policy instruments, only with outcomes (Fiorina 1981, 8–
9). This is only the case, however, if voters see austerity purely as a policy instrument. It is
very likely that an individual facing a cut to his or her own pension sees this as an undesirable
policy outcome, rather than a mere instrument. This would explain why economic voting and
anti-austerity sentiment can exist side by side.
The change in attitudes towards the EU and European integration should be of concern to
European politicians, as well as the increased support for Eurosceptic parties. These results
show that the consensus in favour of the existing European institutions is under threat. The
recent decision by British voters to leave the EU is a visible indicator of this but these results do
not suggest that this shift in attitudes is confined to the UK. Furthermore, the large increase in
197
the degree to which voters consider the EU to be responsible for the condition of the economy
does not correspond to a new centralisation of economic power at the European level. As
Hobolt and Tilley (2014) have observed, the disconnect between the powers of the European
institutions and what voters hold them responsible for is creating an accountability deficit.
Unless this is addressed, there is no reason to expect support for the status quo to return to its
previous levels.
There are several limitations in the thesis. One obvious limitation is that the findings are
not necessarily generalisable beyond the countries of Europe. These countries are predominantly industrialised parliamentary democracies, so many of the findings are likely to be valid
for similar countries like Canada or New Zealand; the situation in countries using a presidential
system, like the United States and most of South America, or in less industrialised countries,
may be different. It would be interesting to investigate the situation in these other countries
but this is made difficult by the lack of comparable survey data collected at the same time.
It was the convenient timing of the EES survey data that led to the choice to study Europe
in this thesis. Nonetheless, it may be possible to use surveys such as the Comparative Study
of Electoral Systems to perform similar comparative research across a different selection of
countries.
Two limitations arise from the constraints of the surveys used. First, as in any surveybased research, the sample is not perfectly random, the response rates are not as high as
would be preferred and there are missing responses. Second, the questions are not always
worded identically in each year and sometimes a particular question is not included at all in
a particular year’s survey. Even with a single year, there are sometimes variations among the
specific national surveys. This is another reason why it would be useful to replicate this study
using a different comparative survey project. Although these constraints are to some degree
unavoidable, it would increase the confidence in these findings if they were repeated using
different data.
Finally, an important priority for further research would be to look at voters’ attitudes
towards austerity. As has already been discussed, the results of this thesis are highly suggestive
that it was the austerity measures rather than the Great Recession itself that produced the
greatest change in voters’ attitudes and intentions. However, this was not an a priori hypothesis
but rather one that has emerged from the data, being suggested by the pattern in the findings of
Chapters 3–6. Indirect evidence was used to test this hypothesis in Chapter 7 and the evidence
found supports it but the surveys used in this thesis do not include the questions that would be
198
CHAPTER 8. CONCLUSION
needed to test it directly. As a result, it would be valuable to study attitudes towards austerity
directly, including the different individual policy measures that are collectively described as
austerity measures.
Appendix A
Countries and parties
This appendix lists all of the countries and parties involved in the analysis presented in this
thesis. The countries are listed in alphabetical order and under each country heading, the
parties for each year are listed in the order that survey respondents were asked about them in
the relevant European Election Study (EES) survey. For each party, it is noted whether it has
been treated as the prime minister’s party or as another cabinet party at that time. All other
parties have been treated as opposition parties.
199
200
APPENDIX A. COUNTRIES AND PARTIES
Austria
Belgium
2004
2004
Status
PM
cabinet
Party
Social Democratic Party of Austria
Austrian People’s Party
Freedom Party of Austria
The Greens—The Green Alternative
Communist Party of Austria
2009
Status
Party
PM
cabinet
Social Democratic Party of Austria
Austrian People’s Party
Freedom Party of Austria
Alliance for the Future of Austria
The Greens—The Green Alternative
Hans-Peter Martin’s List
Junge Liberale
Communist Party of Austria
2014
No party support questions were included in
this year’s survey.
2009
Status
Party
PM
cabinet
Christian Democratic and Flemish
Open Flemish Liberals and Democrats
Socialist Party Different
Flemish Interest
Green
New Flemish Alliance
List Dedecker
Social Liberal Party
Workers’ Party of Belgium
Humanist Democratic Centre
Reformist Movement
Socialist Party
National Front
Environmentalists
cabinet
cabinet
cabinet
2014
Status
Party
cabinet
PM
Austrian People’s Party
Social Democratic Party of Austria
NEOS—The New Austria and
Liberal Forum
The Greens—The Green Alternative
Freedom Party of Austria
Alliance for the Future of Austria
Status
cabinet
cabinet
cabinet
cabinet
PM
cabinet
Party
Workers’ Party of Belgium
Christian Democratic and Flemish
Socialist Party Different
Open Flemish Liberals and Democrats
New Flemish Alliance
Green
Flemish Interest
Humanist Democratic Centre
Socialist Party
Reformist Movement
Environmentalists
People’s Party
Workers’ Party of Belgium (Wallonia)
201
Cyprus
Czech Republic
2004
2004
Status
Party
Status
Party
cabinet
Progressive Party of Working
People
Democratic Rally
Democratic Party
Movement for Social Democracy
PM
cabinet
Czech Social Democratic Party
Christian and Democratic Union—
Czechoslovak People’s Party
Communist Party of Bohemia and
Moravia
Civic Democratic Party
Green Party
PM
cabinet
2009
Status
Party
PM
Progressive Party of Working
People
Democratic Rally
Democratic Party
Movement for Social Democracy
European Party
Ecological and Environmental
Movement
cabinet
cabinet
2009
Status
cabinet
PM
cabinet
Party
Czech Social Democratic Party
Christian and Democratic Union—
Czechoslovak People’s Party
Communist Party of Bohemia and
Moravia
Civic Democratic Party
Green Party
2014
2014
Status
Party
PM
Democratic Rally
Democratic Party
Movement for Social Democracy
Progressive Party of Working People
Ecological and Environmental
Movement
Citizens’ Alliance
Status
Party
cabinet
Christian and Democratic Union—
Czechoslovak People’s Party
Tradition Responsibility Prosperity
(TOP 09)
Czech Social Democratic Party
Civic Democratic Party
Communist Party of Bohemia and
Moravia
National Socialists—21st Century
Left
ANO 2011
Party of Free Citizens
PM
cabinet
202
APPENDIX A. COUNTRIES AND PARTIES
Denmark
Estonia
2004
2004
Status
cabinet
PM
Party
Social Democrats
Danish Social Liberal Party
Conservative People’s Party
Socialist People’s Party
Danish People’s Party
Venstre
Status
PM
cabinet
cabinet
2009
Status
cabinet
PM
Party
Estonian Centre Party
Res Publica Party
Estonian Reform Party
People’s Union of Estonia
Pro Patria Union
Social Democratic Party
Estonian United People’s Party
Estonian Christian Union
Estonian Social Democratic Labour
Party
Party
Social Democrats
Danish Social Liberal Party
Conservative People’s Party
Socialist People’s Party
Danish People’s Party
Venstre
Liberal Alliance
June Movement
People’s Movement against the EU
2009
Status
Party
PM
Estonian Reform Party
Estonian Centre Party
Pro Patria and Res Publica Union
Social Democratic Party
Estonian Greens
People’s Union of Estonia
cabinet
2014
2014
Status
Party
PM
Social Democrats
Venstre
Socialist People’s Party
Danish People’s Party
Danish Social Liberal Party
Liberal Alliance
Conservative People’s Party
People’s Movement against the EU
cabinet
Status
cabinet
PM
Party
Pro Patria and Res Publica Union
Social Democratic Party
Estonian Reform Party
Estonian Centre Party
Estonian Greens
203
Finland
France
2004
2004
Status
Party
cabinet
PM
Social Democratic Party of Finland
Centre Party
National Coalition Party
Left Alliance
Green League
Swedish People’s Party of Finland
Christian Democrats
True Finns
cabinet
Status
cabinet
PM
Party
Far left (Workers’ Struggle / New
Anticapitalist Party)
French Communist Party
Socialist Party
The Greens
Union for French Democracy /
other right parties
Union for a Popular Movement
National Front / Gathering for
France
2009
Status
PM
cabinet
cabinet
cabinet
Party
Social Democratic Party of Finland
Centre Party
National Coalition Party
Left Alliance
Green League
Swedish People’s Party of Finland
Christian Democrats
True Finns
2009
Status
PM
2014
Status
Party
PM
cabinet
cabinet
National Coalition Party
Christian Democrats
Social Democratic Party of Finland
Centre Party
Swedish People’s Party of Finland
Green League
Left Alliance
True Finns
cabinet
cabinet
Party
Far left (Workers’ Struggle / New
Anticapitalist Party)
French Communist Party
Socialist Party
The Greens
Democratic Movement
Union for a Popular Movement
National Front
Left Party
2014
Status
PM
Party
Union for a Popular Movement
Socialist Party
National Front
The Greens
Left Front
Union of Democrats and Independents / Democratic Movement
France Arise
New Anticapitalist Party
204
APPENDIX A. COUNTRIES AND PARTIES
Germany
Greece
2004
2004
Status
PM
cabinet
Party
Status
Party
Christian Democratic Union /
Christian Social Union
Social Democratic Party
Alliance 90 / The Greens
Party of Democratic Socialism
Free Democratic Party
The Republicans
PM
New Democracy
Panhellenic Socialist Movement
Communist Party of Greece
Coalition of Left
Popular Orthodox Rally
Democratic Social Movement
2009
2009
Status
Party
PM
Christian Democratic Union /
Christian Social Union
Social Democratic Party
Alliance 90 / The Greens
The Left
Free Democratic Party
cabinet
Status
Party
PM
New Democracy
Panhellenic Socialist Movement
Communist Party of Greece
Coalition of the Radical Left
Popular Orthodox Rally
Ecologist Greens
2014
2014
Status
Party
PM
Christian Democratic Union /
Christian Social Union
Social Democratic Party
Free Democratic Party
Alliance 90 / The Greens
The Left
Alternative for Germany
Pirate Party
cabinet
Status
Party
PM
New Democracy
Coalition of the Radical Left
Panhellenic Socialist Movement
Independent Greeks
Golden Dawn
Democratic Left
Communist Party of Greece
The River
cabinet
205
Hungary
Ireland
2004
2004
Status
PM
cabinet
Party
Status
Party
Fidesz—Hungarian Civic Alliance
Hungarian Democratic Forum
Hungarian Justice and Life Party
Hungarian Socialist Party
Hungarian Workers’ Party
Alliance of Free Democrats
PM
Fianna Fail
Fine Gael
Green Party
Labour Party
Progressive Democrats
Sinn Fein
2009
Status
PM
cabinet
2009
Party
Status
Party
Fidesz—Hungarian Civic Alliance
Jobbik
Hungarian Communist Workers’
Party
Hungarian Democratic Forum
Hungarian Socialist Party
Alliance of Free Democrats
Christian Democratic People’s Party
PM
Fianna Fail
Fine Gael
Green Party
Labour Party
Sinn Fein
Libertas
cabinet
2014
2014
Status
PM
Party
Jobbik
Politics Can Be Different
Fidesz / Christian Democratic
People’s Party
Hungarian Socialist Party
Together 2014 / Dialogue for Hungary
Democratic Coalition
Status
Party
PM
cabinet
Fine Gael
Labour Party
Fianna Fail
Green Party
Sinn Fein
Socialist Party
206
APPENDIX A. COUNTRIES AND PARTIES
Italy
Latvia
2004
2004
Status
PM
cabinet
cabinet
cabinet
Party
Communist Refoundation Party
Democrats of the Left
Democracy is Freedom—The Daisy
Party of Italian Communists
Federation of the Greens
Italian Democratic Socialists
Union of Democrats for Europe
Italy of Values
Forza Italia
National Alliance
Union of the Centre
North League
New Italian Socialist Party
Italian Radicals / Pannella-Bonino
List
2009
Status
Party
PM
cabinet
The People of Freedom
North League
Democratic Party
Italy of Values
Union of the Centre
Communist Refoundation Party /
Party of Italian Communists
Left and Freedom
The Right
2014
Status
Party
PM
Democratic Party
Forza Italia
North League
Five Star Movement
Union of the Centre
The Other Europe
New Centre-Right
Brothers of Italy
cabinet
cabinet
Status
cabinet
PM
cabinet
Party
New Era Party
For Human Rights in a United
Latvia
People’s Party
Union of Greens and Farmers
Latvia’s First party
For Fatherland and Freedom
Latvian Way
2009
Status
Party
cabinet
cabinet
PM
People’s Party
Union of Greens and Farmers
New Era Party
Harmony Centre
Latvia’s First Party / Latvian Way
For Fatherland and Freedom
For Human Rights in a United
Latvia
Civic Union
Society for Other Politics
cabinet
cabinet
2014
Status
Party
PM
Unity
Harmony
National Alliance
Union of Greens and Farmers
Reform Party
Latvian Russian Union
cabinet
cabinet
cabinet
207
Lithuania
Luxembourg
2004
2004
No party support questions were included in
No party support questions were included in
this year’s survey.
this year’s survey.
2009
2009
Status
Party
PM
Homeland Union—Lithuanian
Christian Democrats
Social Democratic Party of
Lithuania
National Resurrection Party
Order and Justice
Liberal Movement
Labour Party
Liberal and Centre Union
Election Action of Poles in
Lithuania
Lithuanian Popular Peasants’
Union
New Union (Social Liberals)
cabinet
cabinet
cabinet
Status
cabinet
PM
2014
cabinet
PM
cabinet
cabinet
cabinet
Party
Homeland Union—Lithuanian
Christian Democrats
Social Democratic Party of
Lithuania
Liberal Movement
Labour Party
Order and Justice
Election Action of Poles in
Lithuania
Lithuanian Popular Peasants’
Union
The Greens
Luxembourg Socialist Workers’
Party
Democratic Party
Christian Social People’s Party
Alternative Democratic Reform
Party
The Left
Communist Party of Luxembourg
Citizens’ List
2014
Status
Status
Party
PM
cabinet
Party
Christian Social People’s Party
Luxembourg Socialist Workers’
Party
Democratic Party
The Greens
The Left
Alternative Democratic Reform
Party
208
APPENDIX A. COUNTRIES AND PARTIES
Malta
The Netherlands
2004
2004
No party support questions were included in
Status
this year’s survey.
PM
cabinet
2009
cabinet
Status
Party
PM
Nationalist Party
Labour Party
Democratic Alternative
National Action
Party
Labour Party
Christian Democratic Appeal
People’s Party for Freedom and
Democracy
Democrats 66
GreenLeft
Pim Fortuyn List
ChristianUnion
Reformed Political Party
Socialist Party
2009
2014
Status
Party
PM
Labour Party
Nationalist Party
Democratic Alternative
Status
Party
cabinet
PM
Labour Party
Christian Democratic Appeal
People’s Party for Freedom and
Democracy
Democrats 66
GreenLeft
Party for the Animals
ChristianUnion
Reformed Political Party
Socialist Party
Party for Freedom
Proud of the Netherlands
cabinet
2014
Status
Party
PM
People’s Party for Freedom and
Democracy
Labour Party
Party for Freedom
Socialist Party
Christian Democratic Appeal
Democrats 66
ChristianUnion
GreenLeft
cabinet
209
Poland
Portugal
2004
2004
Status
cabinet
cabinet
PM
cabinet
Party
League of Polish Families
Polish People’s Party
Law and Justice
Civic Platform
Self-Defence of the Republic of
Poland
Social Democracy of Poland
Democratic Left Alliance
Labour United
Freedom Union
cabinet
PM
Status
Party
cabinet
Polish People’s Party
Libertas Poland
Coalition Agreement for the Future
Democratic Left Alliance
Civic Platform
Law and Justice
2014
Status
Party
PM
cabinet
Civic Platform
Polish People’s Party
Democratic Left Alliance
Law and Justice
Your Movement
Congress of the New Right
United Poland
Party
Left Bloc
Democratic and Social Centre—
People’s Party
Democratic Unitarian Coalition
New Democracy Party
Socialist Party
Social Democratic Party
2009
Status
2009
PM
Status
PM
Party
Left Bloc
Democratic and Social Centre—
People’s Party
Democratic Unitarian Coalition
Socialist Party
Social Democratic Party
2014
Status
Party
PM
cabinet
Social Democratic Party
Democratic and Social Centre—
People’s Party
Socialist Party
Democratic Unitarian Coalition
Left Bloc
Earth Party
210
APPENDIX A. COUNTRIES AND PARTIES
Slovakia
Slovenia
2004
2004
Status
PM
cabinet
cabinet
cabinet
Party
Status
Party
People’s Party—Movement for a
Democratic Slovakia
Direction—Social Democracy
Communist Party of Slovakia
Slovak Democratic and Christian
Union
Party of the Hungarian Coalition
Christian Democratic Movement
Alliance of the New Citizen
Slovak National Party
cabinet
Democratic Party of Pensioners of
Slovenia
Liberal Democracy of Slovenia
New Slovenia—Christian People’s
Party
Slovenian People’s Party
Youth Party of Slovenia
Slovenian National Party
Slovenian Democratic Party
United List of Social Democrats
Slovenia is Ours
PM
cabinet
2009
2009
Status
Party
cabinet
People’s Party—Movement for a
Democratic Slovakia
Direction—Social Democracy
Slovak Democratic and Christian
Union—Democratic Party
Party of the Hungarian Coalition
Christian Democratic Movement
Slovak National Party
Communist Party of Slovakia
Free Forum
PM
cabinet
Status
Party
cabinet
Democratic Party of Pensioners of
Slovenia
Liberal Democracy of Slovenia
Slovenian People’s Party
Slovenian National Party
Slovenian Democratic Party
Social Democrats
Zares—New Politics
New Slovenia—Christian People’s
Party
Youth Party of Slovenia
cabinet
PM
cabinet
2014
Status
Party
PM
Christian Democratic Movement
Slovak Democratic and Christian
Union—Democratic Party
Party of the Hungarian Coalition
Direction—Social Democracy
New Majority
Freedom and Solidarity
Ordinary People
Most-Híd
2014
Status
Party
PM
Positive Slovenia
Slovenian Democratic Party
Social Democrats
Civic List
Democratic Party of Pensioners of
Slovenia
New Slovenia—Christian People’s
Party
Liberal Democracy of Slovenia
Slovenian People’s Party
cabinet
cabinet
cabinet
211
Spain
Sweden
2004
2004
Status
Party
PM
People’s Party / Navarrese People’s
Union
Spanish Socialist Workers’ Party
United Left / Initiative for Catalonia
Greens
Status
PM
2009
Status
PM
Party
People’s Party
Spanish Socialist Workers’ Party
United Left / Initiative for Catalonia
Greens
Union
Convergence and Union
Republican Left of Catalonia
Basque National Party
Galician Nationalist Bloc
Canarian Coalition / Canarian
Nationalist Party
Yes to Navarre
Basque Solidarity
Navarrese People’s Union
Party
Left Party
Swedish Social Democratic Party
Centre Party
Liberal People’s Party
Moderate Party
Christian Democrats
Green Party
June List
2009
Status
cabinet
cabinet
PM
cabinet
Party
Left Party
Swedish Social Democratic Party
Centre Party
Liberal People’s Party
Moderate Party
Christian Democrats
Green Party
Swedish Democrats
Pirate Party
2014
2014
Status
Status
Party
PM
People’s Party
Spanish Socialist Workers’ Party
United Left / Initiative for Catalonia
Greens
Union
Coalition for Europe
Republican Left of Catalonia
Citizens—Party of the Citizenry
Podemos
PM
cabinet
cabinet
cabinet
Party
Swedish Social Democratic Party
Moderate Party
Green Party
Liberal People’s Party
Centre Party
Swedish Democrats
Christian Democrats
Left Party
212
APPENDIX A. COUNTRIES AND PARTIES
United Kingdom
2004
Status
Party
PM
Labour
Conservatives
Liberal Democrats
UK Independence Party
Scottish National Party
Plaid Cymru
2009
Status
Party
PM
Labour
Conservatives
Liberal Democrats
Scottish National Party
Plaid Cymru
UK Independence Party
British National Party
Green Party
2014
Status
Party
PM
Conservatives
Labour
Liberal Democrats
Green Party
UK Independence Party
Scottish National Party
Plaid Cymru
cabinet
Appendix B
Coefficient tables
Model 3A. Economic voting for PMs’ parties
Fixed effect
Coeff.
Intercept
Year 2009
Year 2014
Left–right distance
Left–right distance2
Party ID
Prospective assessment
Female
Age (decades)
High education
Low education
Urban area
Rural area
Unemployed
Not in workforce
Distance × party ID
Age × party ID
Prosp. assess. × year 2009
Prosp. assess. × year 2014
0.067
0.490
0.159
−0.428
0.023
4.706
0.323
0.054
−0.007
0.084
0.028
−0.018
0.065
−0.083
0.052
0.280
0.179
−0.051
−0.038
Country random effect
Intercept
Year 2009
Year 2014
Left–right distance
Party ID
Prospective assessment
Distance × party ID
Prosp. assess. × year 2009
Prosp. assess. × year 2014
SE
p
(0.095)
(0.183)
(0.130)
(0.019)
(0.001)
(0.129)
(0.053)
(0.021)
(0.008)
(0.025)
(0.031)
(0.026)
(0.027)
(0.043)
(0.025)
(0.029)
(0.015)
(0.048)
(0.065)
Var.
0.184
0.773
0.359
0.008
0.375
0.055
0.015
0.036
0.076
0.492
0.014
0.235
< 0.001
< 0.001
< 0.001
< 0.001
0.011
0.353
0.001
0.357
0.495
0.016
0.055
0.038
< 0.001
< 0.001
0.310
0.565
SD
(0.429)
(0.879)
(0.599)
(0.091)
(0.612)
(0.235)
(0.123)
(0.189)
(0.275)
Dependent variable is support for the prime minister’s party. Sample size is 51962 individuals
within 25 countries. Pseudo R2 is 0.541.
213
214
APPENDIX B. COEFFICIENT TABLES
Model 3B. Economic voting for all parties, no incumbency status
Fixed effect
Intercept
Year 2009
Year 2014
Left–right distance
Left–right distance2
Party ID
Prospective assessment
Female
Age (decades)
High education
Low education
Urban area
Rural area
Unemployed
Not in workforce
Distance × party ID
Age × party ID
Prosp. assess. × year 2009
Prosp. assess. × year 2014
Coeff.
−0.195
0.137
0.160
−0.345
0.021
5.192
−0.034
0.021
−0.003
0.009
0.013
0.017
0.020
0.002
0.000
0.212
0.131
0.015
0.002
Party random effect
Intercept
Left–right distance
Party ID
Prospective assessment
Female
Age (decades)
High education
Low education
Urban area
Rural area
Unemployed
Not in workforce
Distance × party ID
SE
p
(0.052)
(0.063)
(0.064)
(0.006)
(0.000)
(0.051)
(0.023)
(0.011)
(0.005)
(0.015)
(0.016)
(0.014)
(0.014)
(0.018)
(0.011)
(0.013)
(0.008)
(0.030)
(0.031)
Var.
0.555
0.018
0.936
0.065
0.034
0.010
0.065
0.052
0.043
0.044
0.035
0.020
0.037
< 0.001
0.030
0.013
< 0.001
< 0.001
< 0.001
0.135
0.059
0.586
0.555
0.413
0.207
0.146
0.890
0.989
< 0.001
< 0.001
0.615
0.953
SD
(0.745)
(0.135)
(0.968)
(0.255)
(0.185)
(0.100)
(0.255)
(0.228)
(0.208)
(0.210)
(0.187)
(0.142)
(0.192)
Dependent variable is individual’s support for party. Sample size is 352050 measurements
within 497 parties. Pseudo R2 is 0.508.
215
Model 3C. Economic voting for all parties, 2-way incumbency status
Fixed effect
Coeff.
Intercept
Year 2009
Year 2014
Cabinet party
Left–right distance
Left–right distance2
Party ID
Prospective assessment
Female
Age (decades)
High education
Low education
Urban area
Rural area
Unemployed
Not in workforce
Distance × party ID
Age × party ID
Prosp. assess. × year 2009
Prosp. assess. × year 2014
Cabinet × year 2009
Cabinet × year 2014
Prosp. assess. × cabinet
Prosp. assess. × cabinet × year 2009
Prosp. assess. × cabinet × year 2014
Party random effect
Intercept
Left–right distance
Party ID
Prospective assessment
Female
Age (decades)
High education
Low education
Urban area
Rural area
Unemployed
Not in workforce
Distance × party ID
Country random effect
Intercept
Prosp. assess. × cabinet
SE
−0.209
0.129
0.153
0.043
−0.345
0.021
5.186
−0.036
0.021
−0.003
0.009
0.014
0.017
0.020
0.002
0.000
0.213
0.130
0.031
0.007
0.167
0.052
0.431
−0.136
−0.125
(0.065)
(0.062)
(0.063)
(0.094)
(0.006)
(0.000)
(0.051)
(0.017)
(0.011)
(0.005)
(0.015)
(0.016)
(0.014)
(0.014)
(0.018)
(0.011)
(0.013)
(0.008)
(0.023)
(0.024)
(0.125)
(0.127)
(0.050)
(0.049)
(0.051)
Var.
0.560
0.018
0.920
0.033
0.034
0.010
0.065
0.053
0.043
0.044
0.035
0.020
0.037
0.002
0.038
0.015
0.646
< 0.001
< 0.001
< 0.001
0.038
0.061
0.591
0.538
0.372
0.209
0.144
0.909
0.984
< 0.001
< 0.001
0.173
0.775
0.185
0.685
< 0.001
0.006
0.014
SD
(0.749)
(0.135)
(0.959)
(0.183)
(0.185)
(0.101)
(0.256)
(0.230)
(0.208)
(0.210)
(0.187)
(0.142)
(0.192)
Var.
0.037
0.028
p
SD
(0.193)
(0.167)
Dependent variable is individual’s support for party. Sample size is 352050 measurements
within 497 parties within 25 countries. Pseudo R2 is 0.508.
216
APPENDIX B. COEFFICIENT TABLES
Model 3D. Economic voting for all parties, 3-way incumbency status
Fixed effect
Intercept
Year 2009
Year 2014
Cabinet party
Prime minister’s party
Left–right distance
Left–right distance2
Party ID
Prospective assessment
Female
Age (decades)
High education
Low education
Urban area
Rural area
Unemployed
Not in workforce
Distance × party ID
Age × party ID
Prosp. assess. × year 2009
Prosp. assess. × year 2014
Cabinet × year 2009
Cabinet × year 2014
PM × year 2009
PM × year 2014
Prosp. assess. × cabinet
Prosp. assess. × PM
Prosp. assess. × cabinet × year 2009
Prosp. assess. × cabinet × year 2014
Prosp. assess. × PM × year 2009
Prosp. assess. × PM × year 2014
Coeff.
−0.207
0.125
0.151
0.116
−0.164
−0.345
0.021
5.187
−0.034
0.021
−0.003
0.009
0.014
0.017
0.020
0.002
0.000
0.214
0.130
0.027
0.004
−0.036
−0.042
0.433
0.201
0.384
0.108
−0.161
−0.157
0.044
0.065
SE
(0.066)
(0.062)
(0.063)
(0.116)
(0.148)
(0.006)
(0.000)
(0.051)
(0.017)
(0.011)
(0.005)
(0.015)
(0.016)
(0.014)
(0.014)
(0.018)
(0.011)
(0.013)
(0.008)
(0.022)
(0.023)
(0.156)
(0.158)
(0.200)
(0.202)
(0.054)
(0.058)
(0.059)
(0.061)
(0.078)
(0.080)
p
0.003
0.044
0.016
0.320
0.269
< 0.001
< 0.001
< 0.001
0.044
0.062
0.591
0.535
0.372
0.207
0.144
0.911
0.982
< 0.001
< 0.001
0.223
0.872
0.816
0.789
0.031
0.321
< 0.001
0.061
0.007
0.010
0.569
0.416
217
Party random effect
Intercept
Left–right distance
Party ID
Prospective assessment
Female
Age (decades)
High education
Low education
Urban area
Rural area
Unemployed
Not in workforce
Distance × party ID
Var.
0.560
0.018
0.921
0.032
0.034
0.010
0.065
0.053
0.043
0.044
0.035
0.020
0.037
Country random effect
Intercept
Prosp. assess. × cabinet
Prosp. assess. × PM
Var.
0.038
0.022
0.000
SD
(0.748)
(0.135)
(0.959)
(0.180)
(0.184)
(0.101)
(0.256)
(0.229)
(0.208)
(0.210)
(0.187)
(0.142)
(0.192)
SD
(0.195)
(0.150)
(0.009)
Dependent variable is individual’s support for party. Note that as prime ministers’ parties are
also cabinet parties, the prime minister’s party term should be interpreted relative to other
cabinet parties rather than opposition parties. Sample size is 352050 measurements within
497 parties within 25 countries. Pseudo R2 is 0.508.
218
APPENDIX B. COEFFICIENT TABLES
Model 3E. Mean party preference
Fixed effect
Intercept
Year 2009
Year 2014
Prospective assessment
Female
Age (decades)
High education
Low education
Urban area
Rural area
Unemployed
Not in workforce
Prosp. assess. × year 2009
Prosp. assess. × year 2014
Coeff.
3.594
−0.346
−0.434
0.129
0.096
−0.089
0.035
0.003
−0.016
0.006
−0.042
0.028
−0.052
0.070
Country random effect
Intercept
Year 2009
Year 2014
Prospective assessment
Prosp. assess. × year 2009
Prosp. assess. × year 2014
SE
p
(0.110)
(0.146)
(0.121)
(0.025)
(0.013)
(0.004)
(0.015)
(0.018)
(0.016)
(0.016)
(0.025)
(0.015)
(0.022)
(0.034)
Var.
0.262
0.482
0.316
0.011
0.004
0.020
< 0.001
0.029
0.003
< 0.001
< 0.001
< 0.001
0.021
0.875
0.323
0.694
0.095
0.063
0.027
0.050
SD
(0.511)
(0.694)
(0.562)
(0.103)
(0.066)
(0.140)
Dependent variable is the individual’s mean party preference. Sample size is 63286 individuals
within 25 countries. Pseudo R2 is 0.116.
219
Model 4A. Clarity of responsibility (PMs’ parties)
Fixed effect
Intercept
Year 2009
Year 2014
Left–right distance
Left–right distance2
Party ID
Prospective assessment
Time in office (PM)
Government clarity
Institutional clarity
Female
Age (decades)
High education
Low education
Urban area
Rural area
Unemployed
Not in workforce
Distance × party ID
Age × party ID
Prosp. assess. × year 2009
Prosp. assess. × year 2014
Prosp. assess. × time in office
Prosp. assess. × govt clarity
Prosp. assess. × inst. clarity
Coeff.
0.543
0.329
0.039
−0.438
0.023
4.680
0.131
0.024
−0.893
0.054
0.047
−0.005
0.087
0.035
−0.018
0.068
−0.077
0.056
0.266
0.178
−0.083
−0.066
0.005
0.186
0.144
Country random effect
Intercept
Year 2009
Year 2014
Party ID
Prospective assessment
Var.
0.195
0.631
0.353
0.313
0.025
SE
(0.244)
(0.173)
(0.133)
(0.005)
(0.001)
(0.118)
(0.091)
(0.016)
(0.290)
(0.261)
(0.021)
(0.008)
(0.025)
(0.031)
(0.026)
(0.027)
(0.044)
(0.025)
(0.015)
(0.016)
(0.029)
(0.031)
(0.004)
(0.089)
(0.121)
p
0.033
0.071
0.773
< 0.001
< 0.001
< 0.001
0.161
0.130
0.004
0.837
0.027
0.525
0.001
0.260
0.503
0.012
0.079
0.028
< 0.001
< 0.001
0.004
0.033
0.168
0.037
0.245
SD
(0.442)
(0.795)
(0.594)
(0.559)
(0.159)
Dependent variable is support for the prime minister’s party. Sample size is 51962 individuals
within 25 countries. Pseudo R2 is 0.534.
220
APPENDIX B. COEFFICIENT TABLES
Model 4B. Components of government clarity (PMs’ parties)
Fixed effect
Coeff.
Intercept
Year 2009
Year 2014
Left–right distance
Left–right distance2
Party ID
Prospective assessment
Time in office (PM)
Single-party government
Absence of cohabitation
Ideological cohesion
Dominance of main party
Institutional clarity
Female
Age (decades)
High education
Low education
Urban area
Rural area
Unemployed
Not in workforce
Distance × party ID
Age × party ID
Prosp. assess. × year 2009
Prosp. assess. × year 2014
Prosp. assess. × time in office
Prosp. assess. × single party
Prosp. assess. × no cohabitation
Prosp. assess. × cohesion
Prosp. assess. × dominance
Prosp. assess. × inst. clarity
1.917
0.311
−0.056
−0.437
0.023
4.674
−0.131
0.006
0.213
−0.267
−1.580
−0.142
−0.146
0.047
−0.005
0.086
0.034
−0.018
0.067
−0.078
0.056
0.266
0.178
−0.071
−0.037
0.007
−0.053
−0.026
0.388
0.035
0.190
Country random effect
Var.
Intercept
Year 2009
Year 2014
Party ID
Prospective assessment
0.175
0.750
0.387
0.312
0.024
SE
(0.414)
(0.186)
(0.140)
(0.005)
(0.001)
(0.118)
(0.137)
(0.015)
(0.193)
(0.213)
(0.323)
(0.336)
(0.257)
(0.021)
(0.008)
(0.025)
(0.031)
(0.026)
(0.027)
(0.044)
(0.025)
(0.015)
(0.016)
(0.030)
(0.032)
(0.004)
(0.061)
(0.068)
(0.086)
(0.113)
(0.117)
p
< 0.001
0.110
0.696
< 0.001
< 0.001
< 0.001
0.343
0.690
0.283
0.237
< 0.001
0.675
0.576
0.028
0.502
0.001
0.267
0.501
0.013
0.073
0.027
< 0.001
< 0.001
0.016
0.250
0.069
0.381
0.696
< 0.001
0.757
0.120
SD
(0.418)
(0.866)
(0.622)
(0.558)
(0.155)
Dependent variable is support for the prime minister’s party. Sample size is 51962 individuals
within 25 countries. Pseudo R2 is 0.534.
221
Model 4C. Ideological cohesion (PMs’ parties)
Fixed effect
Intercept
Year 2009
Year 2014
Left–right distance
Left–right distance2
Party ID
Prospective assessment
Time in office (PM)
Ideological cohesion
Institutional clarity
Female
Age (decades)
High education
Low education
Urban area
Rural area
Unemployed
Not in workforce
Distance × party ID
Age × party ID
Prosp. assess. × year 2009
Prosp. assess. × year 2014
Prosp. assess. × time in office
Prosp. assess. × cohesion
Prosp. assess. × inst. clarity
Coeff.
1.395
0.339
−0.061
−0.437
0.023
4.676
−0.109
0.008
−1.391
−0.080
0.047
−0.005
0.087
0.036
−0.018
0.067
−0.077
0.056
0.266
0.178
−0.071
−0.034
0.007
0.375
0.152
Country random effect
Intercept
Year 2009
Year 2014
Party ID
Prospective assessment
Var.
0.160
0.674
0.354
0.311
0.023
SE
(0.315)
(0.175)
(0.135)
(0.005)
(0.001)
(0.118)
(0.103)
(0.014)
(0.297)
(0.235)
(0.021)
(0.008)
(0.025)
(0.031)
(0.026)
(0.027)
(0.044)
(0.025)
(0.015)
(0.016)
(0.028)
(0.032)
(0.004)
(0.082)
(0.114)
p
< 0.001
0.065
0.654
< 0.001
< 0.001
< 0.001
0.295
0.559
< 0.001
0.738
0.028
0.498
0.001
0.245
0.504
0.013
0.076
0.027
< 0.001
< 0.001
0.012
0.284
0.069
< 0.001
0.195
SD
(0.400)
(0.821)
(0.595)
(0.558)
(0.152)
Dependent variable is support for the prime minister’s party. Sample size is 51962 individuals
within 25 countries. Pseudo R2 is 0.534.
222
APPENDIX B. COEFFICIENT TABLES
Model 4D. Ideological cohesion over time (PMs’ parties)
Fixed effect
Coeff.
Intercept
Year 2009
Year 2014
Left–right distance
Left–right distance2
Party ID
Prospective assessment
Time in office (PM)
Ideological cohesion
Institutional clarity
Female
Age (decades)
High education
Low education
Urban area
Rural area
Unemployed
Not in workforce
Distance × party ID
Age × party ID
Prosp. assess. × year 2009
Prosp. assess. × year 2014
Time in office × year 2009
Time in office × year 2014
Cohesion × year 2009
Cohesion × year 2014
Inst. clarity × year 2009
Inst. clarity × year 2014
Prosp. assess. × time in office
Prosp. assess. × cohesion
Prosp. assess. × inst. clarity
Prosp. assess. × time in office × year 2009
Prosp. assess. × time in office × year 2014
Prosp. assess. × cohesion × year 2009
Prosp. assess. × cohesion × year 2014
Prosp. assess. × inst. clarity × year 2009
Prosp. assess. × inst. clarity × year 2014
Country random effect
Intercept
Year 2009
Year 2014
Party ID
Prospective assessment
1.702
0.367
−0.777
−0.437
0.023
4.674
−0.447
−0.034
−1.288
−0.531
0.046
−0.005
0.087
0.036
−0.018
0.066
−0.079
0.055
0.266
0.178
0.502
−0.136
0.041
0.078
−0.685
−0.033
0.704
0.825
0.019
0.824
−0.061
−0.016
0.030
−0.681
−0.340
0.189
0.568
Var.
0.161
0.745
0.396
0.314
0.028
SE
(0.637)
(0.871)
(0.764)
(0.005)
(0.001)
(0.119)
(0.220)
(0.031)
(0.613)
(0.401)
(0.021)
(0.008)
(0.025)
(0.031)
(0.026)
(0.027)
(0.044)
(0.025)
(0.015)
(0.016)
(0.225)
(0.228)
(0.040)
(0.048)
(0.875)
(0.716)
(0.825)
(0.557)
(0.010)
(0.205)
(0.154)
(0.011)
(0.015)
(0.238)
(0.220)
(0.142)
(0.136)
p
0.016
0.676
0.318
< 0.001
< 0.001
< 0.001
0.043
0.293
0.053
0.201
0.031
0.485
0.001
0.244
0.488
0.015
0.071
0.030
< 0.001
< 0.001
0.026
0.549
0.309
0.112
0.440
0.964
0.402
0.154
0.059
< 0.001
0.695
0.132
0.036
0.004
0.123
0.183
< 0.001
SD
(0.402)
(0.863)
(0.630)
(0.560)
(0.166)
Dependent variable is support for the prime minister’s party. Sample size is 51962 individuals
within 25 countries. Pseudo R2 is 0.534.
223
Model 4E. Ideological cohesion over time (all parties)
Fixed effect
Intercept
Year 2009
Year 2014
Cabinet party
Prime minister’s party
Left–right distance
Left–right distance2
Party ID
Prospective assessment
Time in office (cabinet)
Ideological cohesion
Institutional clarity
Female
Age (decades)
High education
Low education
Urban area
Rural area
Unemployed
Not in workforce
Distance × party ID
Age × party ID
Prosp. assess. × year 2009
Prosp. assess. × year 2014
Cabinet × year 2009
Cabinet × year 2014
Time in office × year 2009
Time in office × year 2014
Cohesion × year 2009
Cohesion × year 2014
Inst. clarity × year 2009
Inst. clarity × year 2014
Cohesion × cabinet
Inst. clarity × cabinet
Prosp. assess. × cabinet
Prosp. assess. × PM
Prosp. assess. × time in office
Prosp. assess. × cohesion
Prosp. assess. × inst. clarity
Prosp. assess. × cabinet × year 2009
Prosp. assess. × cabinet × year 2014
Cohesion × cabinet × year 2009
Cohesion × cabinet × year 2014
Inst. clarity × cabinet × year 2009
Inst. clarity × cabinet × year 2014
Prosp. assess. × cohesion × cabinet
Prosp. assess. × inst. clarity × cabinet
Prosp. assess. × time in office × year 2009
Prosp. assess. × time in office × year 2014
Prosp. assess. × cohesion × year 2009
Prosp. assess. × cohesion × year 2014
Prosp. assess. × inst. clarity × year 2009
Prosp. assess. × inst. clarity × year 2014
Prosp. assess. × cohesion × cabinet × year 2009
Prosp. assess. × cohesion × cabinet × year 2014
Prosp. assess. × inst. clarity × cabinet × year 2009
Prosp. assess. × inst. clarity × cabinet × year 2014
Coeff.
1.062
−1.336
−0.751
−0.178
0.098
−0.345
0.021
5.188
−0.083
−0.018
−1.077
−0.398
0.021
−0.003
0.009
0.014
0.017
0.021
0.001
0.000
0.214
0.130
0.071
−0.001
0.963
1.022
0.012
−0.001
1.294
0.844
0.430
0.119
0.207
0.061
−0.369
0.154
−0.003
0.021
0.058
0.430
0.096
−1.176
−0.890
0.357
−0.411
1.031
−0.358
0.000
0.012
−0.014
0.036
−0.059
−0.068
−0.924
−0.555
0.488
0.544
SE
(0.436)
(0.474)
(0.463)
(0.771)
(0.083)
(0.006)
(0.000)
(0.051)
(0.145)
(0.017)
(0.424)
(0.265)
(0.011)
(0.005)
(0.015)
(0.016)
(0.013)
(0.014)
(0.018)
(0.011)
(0.013)
(0.008)
(0.162)
(0.164)
(0.863)
(0.860)
(0.020)
(0.020)
(0.500)
(0.459)
(0.286)
(0.281)
(0.752)
(0.419)
(0.302)
(0.033)
(0.007)
(0.143)
(0.078)
(0.339)
(0.340)
(0.889)
(0.852)
(0.563)
(0.555)
(0.296)
(0.161)
(0.008)
(0.008)
(0.167)
(0.163)
(0.103)
(0.107)
(0.351)
(0.338)
(0.219)
(0.220)
p
0.016
0.005
0.106
0.817
0.242
< 0.001
< 0.001
< 0.001
0.569
0.293
0.011
0.140
0.062
0.588
0.540
0.375
0.204
0.139
0.940
0.975
< 0.001
< 0.001
0.662
0.996
0.265
0.235
0.534
0.947
0.010
0.067
0.133
0.672
0.783
0.884
0.223
< 0.001
0.623
0.886
0.453
0.205
0.778
0.186
0.297
0.526
0.459
0.001
0.027
0.950
0.132
0.934
0.824
0.568
0.523
0.009
0.102
0.026
0.014
224
APPENDIX B. COEFFICIENT TABLES
Party random effect
Intercept
Left–right distance
Party ID
Prospective assessment
Female
Age (decades)
High education
Low education
Urban area
Rural area
Unemployed
Not in workforce
Distance × party ID
Var.
0.556
0.018
0.926
0.035
0.034
0.010
0.065
0.053
0.043
0.044
0.035
0.020
0.037
Country random effect
Intercept
Var.
0.034
SD
(0.746)
(0.135)
(0.962)
(0.187)
(0.185)
(0.101)
(0.256)
(0.230)
(0.207)
(0.210)
(0.188)
(0.142)
(0.191)
SD
(0.185)
Dependent variable is individual’s support for party. Sample size is 352050 individuals within
497 parties within 25 countries. Pseudo R2 is 0.508.
225
Model 5A. Party’s European integration position
Fixed effect
Coeff.
Intercept
Year 2009
Year 2014
Cabinet party
Prime minister’s party
Party European integration position
Left–right distance
Left–right distance2
Party ID
Prospective assessment
Female
Age (decades)
High education
Low education
Urban area
Rural area
Unemployed
Not in workforce
Distance × party ID
Age × party ID
Props. assess. × year 2009
Props. assess. × year 2014
Cabinet × year 2009
Cabinet × year 2014
Integration pos. × year 2009
Integration pos. × year 2014
Prosp. assess. × cabinet
Prosp. assess. × PM
Prosp. assess. × integration pos.
Prosp. assess. × cabinet × year 2009
Prosp. assess. × cabinet × year 2014
Prosp. assess. × integration pos. × year 2009
Prosp. assess. × integration pos. × year 2014
Party random effect
Intercept
Left–right distance
Party ID
Prospective assessment
Female
Age (decades)
High education
Low education
Urban area
Rural area
Unemployed
Not in workforce
Country random effect
Party European integration position
−0.100
0.107
0.101
−0.172
0.040
0.183
−0.354
0.022
5.222
−0.026
0.026
−0.003
0.021
0.009
0.010
0.013
−0.004
0.001
0.287
0.132
0.009
−0.014
0.244
0.266
−0.058
−0.161
0.381
0.136
0.017
−0.191
−0.217
−0.002
0.045
Var.
0.391
0.020
1.066
0.036
0.037
0.011
0.065
0.054
0.043
0.047
0.035
0.020
Var.
0.005
SE
(0.050)
(0.064)
(0.064)
(0.112)
(0.090)
(0.034)
(0.007)
(0.001)
(0.056)
(0.018)
(0.012)
(0.006)
(0.016)
(0.017)
(0.014)
(0.015)
(0.019)
(0.012)
(0.008)
(0.008)
(0.025)
(0.026)
(0.145)
(0.147)
(0.043)
(0.041)
(0.044)
(0.036)
(0.012)
(0.057)
(0.058)
(0.016)
(0.016)
p
0.046
0.094
0.117
0.125
0.658
< 0.001
< 0.001
< 0.001
< 0.001
0.155
0.036
0.614
0.184
0.581
0.475
0.376
0.832
0.910
< 0.001
< 0.001
0.708
0.576
0.093
0.072
0.174
< 0.001
< 0.001
< 0.001
0.140
0.001
< 0.001
0.918
0.006
SD
(0.626)
(0.141)
(1.033)
(0.189)
(0.193)
(0.103)
(0.255)
(0.233)
(0.208)
(0.217)
(0.187)
(0.143)
SD
(0.067)
Dependent variable is individual’s support for party. Sample size is 314782 individuals within
428 parties within 22 countries. Pseudo R2 is 0.504.
226
APPENDIX B. COEFFICIENT TABLES
Model 5B. Party’s left–right position
Fixed effect
Intercept
Year 2009
Year 2014
Cabinet party
Prime minister’s party
Party left–right position
Party left–right position2
Left–right distance
Left–right distance2
Party ID
Prospective assessment
Female
Age (decades)
High education
Low education
Urban area
Rural area
Unemployed
Not in workforce
Distance × party ID
Age × party ID
Props. assess. × year 2009
Props. assess. × year 2014
Cabinet × year 2009
Cabinet × year 2014
Left–right pos. × year 2009
Left–right pos. × year 2014
Left–right pos.2 × year 2009
Left–right pos.2 × year 2014
Prosp. assess. × cabinet
Prosp. assess. × PM
Prosp. assess. × left–right position
Prosp. assess. × left–right position2
Prosp. assess. × cabinet × year 2009
Prosp. assess. × cabinet × year 2014
Prosp. assess. × left–right pos. × year 2009
Prosp. assess. × left–right pos. × year 2014
Prosp. assess. × left–right pos.2 × year 2009
Prosp. assess. × left–right pos.2 × year 2014
Coeff.
0.094
0.106
−0.091
−0.056
0.026
−0.021
−0.037
−0.354
0.022
5.228
0.005
0.026
−0.003
0.021
0.009
0.010
0.014
−0.004
0.002
0.287
0.131
−0.007
0.008
0.192
0.200
−0.012
0.010
0.003
0.039
0.346
0.142
0.032
−0.006
−0.155
−0.129
−0.031
−0.030
0.004
−0.003
SE
(0.070)
(0.093)
(0.095)
(0.114)
(0.092)
(0.022)
(0.009)
(0.007)
(0.001)
(0.056)
(0.026)
(0.012)
(0.006)
(0.016)
(0.017)
(0.014)
(0.015)
(0.019)
(0.012)
(0.008)
(0.008)
(0.036)
(0.037)
(0.147)
(0.147)
(0.029)
(0.029)
(0.012)
(0.012)
(0.044)
(0.036)
(0.008)
(0.004)
(0.057)
(0.058)
(0.011)
(0.011)
(0.005)
(0.005)
p
0.180
0.256
0.337
0.627
0.774
0.335
< 0.001
< 0.001
< 0.001
< 0.001
0.847
0.035
0.608
0.187
0.590
0.471
0.371
0.825
0.899
< 0.001
< 0.001
0.837
0.839
0.192
0.175
0.683
0.739
0.793
0.002
< 0.001
< 0.001
< 0.001
0.069
0.007
0.026
0.005
0.007
0.400
0.509
227
Party random effect
Intercept
Left–right distance
Party ID
Prospective assessment
Female
Age (decades)
High education
Low education
Urban area
Rural area
Unemployed
Not in workforce
Var.
0.423
0.020
1.057
0.036
0.037
0.011
0.065
0.054
0.043
0.047
0.035
0.020
SD
(0.650)
(0.141)
(1.028)
(0.191)
(0.192)
(0.103)
(0.256)
(0.233)
(0.208)
(0.217)
(0.187)
(0.143)
Dependent variable is individual’s support for party. Sample size is 314782 individuals within
428 parties. Pseudo R2 is 0.504.
228
APPENDIX B. COEFFICIENT TABLES
Model 5C. Party’s social and economic positions
Fixed effect
Intercept
Year 2009
Year 2014
Cabinet party
Prime minister’s party
Party economic position
Party economic position2
Party social position
Party social position2
Left–right distance
Left–right distance2
Party ID
Prospective assessment
Female
Age (decades)
High education
Low education
Urban area
Rural area
Unemployed
Not in workforce
Distance × party ID
Age × party ID
Props. assess. × year 2009
Props. assess. × year 2014
Cabinet × year 2009
Cabinet × year 2014
Economic pos. × year 2009
Economic pos. × year 2014
Economic pos.2 × year 2009
Economic pos.2 × year 2014
Social pos. × year 2009
Social pos. × year 2014
Social pos.2 × year 2009
Social pos.2 × year 2014
Prosp. assess. × cabinet
Prosp. assess. × PM
Prosp. assess. × economic position
Prosp. assess. × economic position2
Prosp. assess. × social position
Prosp. assess. × social position2
Prosp. assess. × cabinet × year 2009
Prosp. assess. × cabinet × year 2014
Prosp. assess. × economic pos. × year 2009
Prosp. assess. × economic pos. × year 2014
Prosp. assess. × economic pos.2 × year 2009
Prosp. assess. × economic pos.2 × year 2014
Prosp. assess. × social pos. × year 2009
Prosp. assess. × social pos. × year 2014
Prosp. assess. × social pos.2 × year 2009
Prosp. assess. × social pos.2 × year 2014
Coeff.
0.139
0.073
−0.093
−0.031
0.027
0.008
−0.024
−0.042
−0.019
−0.354
0.022
5.227
−0.067
0.026
−0.003
0.021
0.009
0.010
0.014
−0.004
0.001
0.287
0.132
0.042
0.055
0.193
0.176
−0.018
−0.033
−0.008
0.027
−0.007
0.048
0.011
0.012
0.312
0.161
0.060
0.002
−0.016
0.005
−0.133
−0.100
−0.029
−0.033
0.000
−0.007
−0.007
−0.012
−0.005
−0.007
SE
(0.090)
(0.120)
(0.124)
(0.119)
(0.094)
(0.025)
(0.010)
(0.021)
(0.009)
(0.007)
(0.001)
(0.056)
(0.032)
(0.012)
(0.006)
(0.016)
(0.017)
(0.014)
(0.015)
(0.019)
(0.012)
(0.008)
(0.008)
(0.043)
(0.046)
(0.153)
(0.151)
(0.034)
(0.033)
(0.014)
(0.013)
(0.029)
(0.029)
(0.011)
(0.012)
(0.043)
(0.035)
(0.009)
(0.004)
(0.008)
(0.003)
(0.055)
(0.056)
(0.012)
(0.012)
(0.005)
(0.005)
(0.010)
(0.010)
(0.004)
(0.004)
p
0.125
0.543
0.454
0.796
0.774
0.744
0.020
0.050
0.031
< 0.001
< 0.001
< 0.001
0.038
0.035
0.603
0.184
0.590
0.470
0.371
0.833
0.909
< 0.001
< 0.001
0.331
0.235
0.207
0.244
0.597
0.324
0.573
0.046
0.797
0.093
0.325
0.316
< 0.001
< 0.001
< 0.001
0.681
0.032
0.073
0.016
0.074
0.019
0.008
0.998
0.185
0.533
0.244
0.210
0.099
229
Party random effect
Intercept
Left–right distance
Party ID
Prospective assessment
Female
Age (decades)
High education
Low education
Urban area
Rural area
Unemployed
Not in workforce
Var.
0.420
0.020
1.059
0.032
0.037
0.011
0.065
0.054
0.043
0.047
0.035
0.020
SD
(0.648)
(0.141)
(1.029)
(0.179)
(0.192)
(0.103)
(0.255)
(0.233)
(0.207)
(0.217)
(0.187)
(0.143)
Dependent variable is individual’s support for party. Sample size is 314782 individuals within
428 parties. Pseudo R2 is 0.504.
230
APPENDIX B. COEFFICIENT TABLES
Model 6A. Turnout intention
Fixed effect
Intercept
Year 2009
Year 2014
Government identifier
Opposition identifier
Prospective assessment
Voted last time
Female
Age (decades)
High education
Low education
Urban area
Rural area
Unemployed
Not in workforce
Government ID × year 2009
Government ID × year 2014
Opposition ID × year 2009
Opposition ID × year 2014
Age × government ID
Age × opposition ID
Prosp. assess. × year 2009
Prosp. assess. × year 2014
Voted last time × year 2009
Voted last time × year 2014
Coeff.
0.314
−0.094
−0.063
0.245
0.287
0.015
0.358
−0.017
−0.020
0.003
0.014
0.003
−0.002
0.003
0.028
0.071
0.046
0.068
0.030
0.010
0.006
−0.007
0.010
−0.007
0.041
Country random effect
Var.
Intercept
Government identifier
Opposition identifier
0.006
0.006
0.005
SE
(0.017)
(0.008)
(0.007)
(0.017)
(0.016)
(0.003)
(0.006)
(0.003)
(0.001)
(0.003)
(0.004)
(0.004)
(0.004)
(0.006)
(0.004)
(0.010)
(0.010)
(0.009)
(0.009)
(0.002)
(0.002)
(0.004)
(0.004)
(0.009)
(0.009)
p
< 0.001
< 0.001
< 0.001
< 0.001
< 0.001
< 0.001
< 0.001
< 0.001
< 0.001
0.451
0.001
0.362
0.581
0.659
< 0.001
< 0.001
< 0.001
< 0.001
0.001
< 0.001
0.002
0.053
0.012
0.435
< 0.001
SD
(0.076)
(0.074)
(0.070)
This is a multilevel logistic regression model. The dependent variable is one for individuals
who indicated an intention to vote and zero for individuals who indicated they would not vote
as well as individuals who refused to answer the question. Sample size is 63483 individuals
within 22 countries. Pseudo R2 is 0.361.
231
Model 7A. Desirability of continued European unification
Fixed effect
Intercept
Year 2009
Year 2014
Prospective assessment
Female
Age (decades)
High education
Low education
Urban area
Rural area
Unemployed
Not in workforce
Left–right position
Left–right position2
Prosp. assess. × year 2009
Prosp. assess. × year 2014
Left–right pos. × year 2009
Left–right pos. × year 2014
Left–right pos.2 × year 2009
Left–right pos.2 × year 2014
Age × year 2009
Age × year 2014
Female × year 2009
Female × year 2014
Low education × year 2009
Low education × year 2014
High education × year 2009
High education × year 2014
Rural area × year 2009
Rural area × year 2014
Urban area × year 2009
Urban area × year 2014
Unemployed × year 2009
Unemployed × year 2014
Not in workforce × year 2009
Not in workforce × year 2014
Coeff.
5.506
−0.381
−1.082
0.372
−0.206
−0.060
0.369
−0.355
0.206
−0.106
0.088
0.162
0.020
0.004
−0.145
0.101
−0.013
−0.048
0.004
−0.005
−0.011
−0.016
0.079
0.069
0.199
0.145
0.071
0.053
0.002
0.305
−0.188
−0.168
−0.170
−0.182
−0.022
−0.137
Country random effect
Intercept
Year 2009
Year 2014
Prospective assessment
Prosp. assess. × year 2009
Prosp. assess. × year 2014
SE
p
(0.173)
(0.126)
(0.131)
(0.056)
(0.044)
(0.016)
(0.052)
(0.062)
(0.054)
(0.053)
(0.096)
(0.053)
(0.009)
(0.003)
(0.047)
(0.072)
(0.012)
(0.012)
(0.004)
(0.004)
(0.021)
(0.021)
(0.061)
(0.061)
(0.087)
(0.090)
(0.073)
(0.071)
(0.076)
(0.077)
(0.075)
(0.075)
(0.131)
(0.124)
(0.072)
(0.073)
Var.
0.688
0.306
0.336
0.061
0.028
0.094
< 0.001
0.006
< 0.001
< 0.001
< 0.001
< 0.001
< 0.001
< 0.001
< 0.001
0.046
0.362
0.002
0.025
0.107
0.007
0.179
0.281
< 0.001
0.272
0.169
0.586
0.437
0.191
0.256
0.022
0.107
0.325
0.460
0.980
< 0.001
0.013
0.025
0.194
0.144
0.759
0.061
SD
(0.830)
(0.553)
(0.579)
(0.248)
(0.167)
(0.307)
Dependent variable is individual’s support for further European integration. Sample size is
54806 individuals within 25 countries. Pseudo R2 is 0.107.
232
APPENDIX B. COEFFICIENT TABLES
Model 7B. Evaluation of EU membership
Fixed effect
Intercept (‘bad’ response)
Intercept (‘bad’ or ‘neutral’ response)
Year 2009
Year 2014
Prospective assessment
Female
Age (decades)
High education
Low education
Urban area
Rural area
Unemployed
Not in workforce
Left–right position
Left–right position2
Prosp. assess. × year 2009
Prosp. assess. × year 2014
Left–right pos. × year 2009
Left–right pos. × year 2014
Left–right pos.2 × year 2009
Left–right pos.2 × year 2014
Age × year 2009
Age × year 2014
Female × year 2009
Female × year 2014
Low education × year 2009
Low education × year 2014
High education × year 2009
High education × year 2014
Rural area × year 2009
Rural area × year 2014
Urban area × year 2009
Urban area × year 2014
Unemployed × year 2009
Unemployed × year 2014
Not in workforce × year 2009
Not in workforce × year 2014
Coeff.
−2.152
−0.392
0.440
−0.119
0.440
−0.219
0.005
0.440
−0.331
0.150
−0.108
−0.182
0.101
0.030
0.002
−0.126
0.157
0.018
−0.004
−0.007
−0.004
0.022
0.005
0.018
0.139
−0.093
−0.080
0.148
0.136
0.108
0.124
0.047
−0.130
−0.057
−0.084
−0.018
−0.014
SE
(0.150)
(0.149)
(0.137)
(0.149)
(0.056)
(0.031)
(0.011)
(0.038)
(0.043)
(0.039)
(0.037)
(0.066)
(0.038)
(0.006)
(0.002)
(0.044)
(0.086)
(0.009)
(0.009)
(0.003)
(0.003)
(0.015)
(0.015)
(0.045)
(0.043)
(0.062)
(0.062)
(0.055)
(0.052)
(0.055)
(0.054)
(0.056)
(0.054)
(0.092)
(0.085)
(0.053)
(0.052)
p
< 0.001
0.009
0.001
0.423
< 0.001
< 0.001
0.662
< 0.001
< 0.001
< 0.001
0.004
0.006
0.008
< 0.001
0.251
0.005
0.070
0.045
0.671
0.005
0.129
0.153
0.757
0.693
0.001
0.133
0.192
0.007
0.009
0.051
0.023
0.403
0.016
0.531
0.322
0.741
0.787
233
Country random effect
Intercept
Year 2009
Year 2014
Prospective assessment
Prosp. assess. × year 2009
Prosp. assess. × year 2014
Var.
0.458
0.349
0.424
0.057
0.023
0.155
SD
(0.677)
(0.591)
(0.651)
(0.238)
(0.153)
(0.394)
This is a multilevel ordered logit model. The dependent variable is the individual’s evaluation of EU membership as good, bad or neutral. Sample size is 56803 individuals within
25 countries. No Pseudo R2 has been computed as the residual variance is unknown. The
p-values given here are based on Wald tests, as the software used to estimate this model does
not produce Satterthwaite estimates of the degrees of freedom as for the other models.
234
APPENDIX B. COEFFICIENT TABLES
Model 7C. Allocation of responsibility for the economy
Fixed effect
Intercept
Institution is EU
Year 2014
Prospective assessment
Female
Age (decades)
High education
Low education
Urban area
Rural area
Unemployed
Not in workforce
Left–right position
Left–right position2
Prosp. assess. × year 2014
Left–right pos. × year 2014
Left–right pos.2 × year 2014
Age × year 2014
Female × year 2014
Low education × year 2014
High education × year 2014
Rural area × year 2014
Urban area × year 2014
Unemployed × year 2014
Not in workforce × year 2014
EU × year 2014
Prosp. assess. × EU
Left–right pos. × EU
Left–right pos.2 × EU
Age × EU
Female × EU
Low education × EU
High education × EU
Rural area × EU
Urban area × EU
Unemployed × EU
Not in workforce × EU
Prosp. assess. × EU × year 2014
Left–right pos. × EU × year 2014
Left–right pos.2 × EU × year 2014
Age × EU × year 2014
Female × EU × year 2014
Low education × EU × year 2014
High education × EU × year 2014
Rural area × EU × year 2014
Urban area × EU × year 2014
Unemployed × EU × year 2014
Not in workforce × EU × year 2014
Coeff.
0.319
−1.478
0.430
−0.159
0.185
−0.050
−0.010
−0.095
0.038
0.099
−0.058
−0.186
0.043
0.004
0.200
−0.036
0.001
0.051
−0.112
0.139
0.289
0.021
−0.037
0.081
0.170
0.419
0.129
−0.026
0.004
−0.042
0.229
0.169
−0.188
−0.021
−0.069
0.282
0.141
−0.204
0.030
0.000
0.003
−0.155
−0.250
0.033
−0.023
0.208
−0.204
−0.129
SE
(0.118)
(0.147)
(0.158)
(0.059)
(0.036)
(0.012)
(0.043)
(0.051)
(0.045)
(0.047)
(0.075)
(0.042)
(0.007)
(0.002)
(0.077)
(0.010)
(0.003)
(0.017)
(0.050)
(0.075)
(0.060)
(0.066)
(0.063)
(0.100)
(0.060)
(0.143)
(0.041)
(0.010)
(0.003)
(0.017)
(0.051)
(0.073)
(0.061)
(0.066)
(0.064)
(0.107)
(0.059)
(0.058)
(0.014)
(0.004)
(0.024)
(0.072)
(0.106)
(0.084)
(0.094)
(0.089)
(0.142)
(0.085)
p
0.013
< 0.001
0.012
0.014
< 0.001
< 0.001
0.811
0.062
0.394
0.034
0.443
< 0.001
< 0.001
0.029
0.016
< 0.001
0.772
0.003
0.027
0.065
< 0.001
0.748
0.559
0.417
0.004
0.007
0.004
0.007
0.124
0.016
< 0.001
0.020
0.002
0.750
0.281
0.008
0.017
0.002
0.034
0.908
0.913
0.031
0.019
0.693
0.805
0.019
0.151
0.128
235
Country random effect
Intercept
Institution is EU
Year 2014
Prospective assessment
Prosp. assess. × year 2014
EU × year 2014
Prosp. assess. × EU
Prosp. assess. × EU × year 2014
Var.
0.321
0.490
0.568
0.077
0.125
0.436
0.027
0.046
SD
(0.567)
(0.700)
(0.754)
(0.278)
(0.353)
(0.660)
(0.163)
(0.214)
Dependent variable is the degree to which the individual holds the institution responsble for
the condition of the economy. Sample size is 76658 individuals within 24 countries. Pseudo
R2 is 0.135.
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