The Impact of the Party System on

Electoral Volatility

A Cross-Country Analysis of Inter-Election Switching

Yves Dejaeghere1 & Ruth Dassonneville

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1Center of Citizenship and Democracy, University of Leuven

² FWO – Research Foundation Flanders

Paper prepared for presentation at the EPOP 2012 conference

7th

-9th

September, Oxford

1

Abstract

In the literature on electoral volatility and party defection, structural elements have been put forward

as crucial variables. Especially the party system is suggested to be of importance for understanding

differences in levels of volatility between countries. First of all, the electoral system has been shown to

have an impact on levels of volatility. In majoritarian and highly disproportional systems, electoral

volatility proved to be more pronounced. With regard to the party system two dimensions can be

distinguished. First, the number of parties within an electoral system is expected to be related to levels

of electoral volatility. It is argued that the more options voters have, the more they will be inclined to

switch. Second, the extent to which a party system is polarized matters as well. The more polarized a

party system is, the larger ideological distances between parties are. Therefore, switching parties

implies a more pronounced ideological shift for a voter and should therefore become less probable.

Although these variables have been empirically studied separately, there has not yet been a large

comparative investigation including them in one analysis with cross-national data on the individual

level. Using the second and third module of the CSES project this paper investigated volatility for

25531 respondents in 32 elections between 2000 and 2010. Using multilevel models that include

country level variables while controlling for important individual level characteristics we have an

optimal control of the simultaneous effect of these separate variables.

Our results show that the effect of individual-level variables such as education and a persons’

satisfaction with democracy remain strong predictors of electoral volatility even in a cross-national

analysis. Of the variables on the contextual level proportionality and the number of parties seem to

have an effect on switching parties between elections. Volatility is higher in more proportional

systems. Furthermore, it seems that the sheer number of parties increases the propensity to change a

voters choice regardless of their polarization. This last finding is a refutation of a longstanding claim

in the literature that it is the distance between the parties rather than the number that influences

volatility. Because we tested three different measures for polarization separately that can be found in

the literature, this can be considered a robust finding. We furthermore find a cross-level interaction

effect that shows that satisfaction with the way democracy works in a country leads to different odds

of switching votes depending on the effective number of parties in that election.

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1. Introduction

In his seminal 1979 paper Morgens Pedersen investigated the effect the format of a party system has

on electoral volatility. For Pedersen (1979: 14), it seemed there was a clear linkage ‘between the

number of options open to the individual voter, and the voters’ propensities to transfer votes between

parties.’. The main limitation of his analysis was the use of aggregate data whereas the effect of the

party systems worked through individual level dynamics. He stated himself (1979: 16) that his

hypotheses ‘in principle lend themselves to a test on the basis of individual level data.’, which he

didn’t have. While in the last thirty years this article has been cited in abundance there has as yet never

been a test of the claims made in it with the data they are intended for.

Pedersen assumed it is not specifically the number of parties in itself that cause more volatility, but the

fact that a voter perceives less average distance between parties when there are more of them. If

parties are ideologically close to each other, voters will be more likely to consider leaving the party

they voted for previously. This implies that Pedersen’s hypothesis also involved the polarization of the

party system. Pedersen himself, however, considered the connection between the number of parties

and polarization as a truism, as did many authors in his time. This was challenged in later empirical

research and the number of parties and polarization are treated as distinct elements of a party system in

current research and in this paper. We will assess whether the link Pedersen observed between

volatility at an aggregate level and the characteristics of the party system is indeed one driven by

individual level dynamics and caused by polarization in the party system rather than by the number of

parties as such. To do this we will use the data from the Comparative Study of Electoral Systems

(CSES). We start the paper with an overview of the literature on volatility, in which we have attention

for both the contextual and individual causes of volatility. Next we present the data used for the

analyses and elaborate on the method used. After describing the results we end with some concluding

remarks and suggestions for further research.

2. Theory and Hypotheses

In liberal democracies, political parties are the organizational expression of ideological diversity in the

political sphere (Ware, 1996). Elections are the arena where parties, on the basis of these ideologies,

try to win over as many voters as possible. They are expected to place themselves at the most

advantageous position in the ideological space in order to maximize their potential voters (Downs,

1957). Downs compares the ideological space with a consumer market, but unlike what holds there,

political parties cannot be considered interchangeable goods. As the seminal work of Lipset & Rokkan

a decade later (1967) pointed out, several cleavages exist in western societies. These deeply-rooted

cleavages arose because of historical reasons and dominate the political sphere through the presence

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political parties. The cleavage structure causes voters to be strongly aligned to particular parties and

parties therefore did not (and do not) go into battle over the total electorate at each election (as

companies towards consumers). They can count on a specific slice of the electorate that is bound to

them for ideological reasons. Although some change could occur at the fringes of an electoral space, a

large section of society was said to be ‘frozen’ into these cleavages (Mair, 2001). But as Blondel

(1968) pointed out a year after Lipset & Rokkan, some fluctuation is absolutely necessary to keep

democratic systems structurally balanced. If the party system is frozen and the same parties remain a

majority, this will undermine the belief in the usefulness of elections to achieve ones goal in society.

Blondel therefore sees a certain degree of electoral volatility between parties as benign to democracy

and especially so in majoritarian systems: “The distortions of two-party systems are potentially

serious, if the divine hand does not act to make the pendulum swing.” (1968: 198).

Pedersen was the first to try to investigate the issue of volatility in a large comparative manner. Since

the publication of Pedersen’s study, several scholars have drawn attention to a further trend of

increasing electoral volatility (Crewe & Denver, 1985; Dalton, McAllister & Wattenberg, 2002; Rose

& Urwin, 1970). This alleged trend has been seen as an indication that the old ties between voter and

parties have waned and that a trend of ‘dealignment’ has occurred. This process of dealignment is

thought of as causing voters to shift their party preferences to other than traditional parties as well. As

a consequence, new political parties enter the electoral arena and the number of electoral parties

increases (Dalton, McAllister & Wattenberg, 2002).

The literature gives an important role to the party system in explaining volatility (cfr. infra). It not only

points at the relationship between the number of parties and volatility, but also at the effect of the

dispersion of the parties in an ideological issue space. As has been stated in the introduction, Pedersen

(1979) first captured that link and using data from 103 elections from the period 1945-1975 he found a

clear effect of the number of parties on the level of electoral volatility. Although the causal mechanism

Pedersen put forward is clearly situated at the individual level, he could only find indications for it at

an aggregate level making him end his paper by saying that that the findings were not conclusive but

strong enough to warrant further study. While arguments stating that how voters perceive the

ideological space and electoral competition drives volatility are manifold, so far no one has

systematically tested the impact of these contextual variables at the individual level. In the current

paper, we address this gap in the literature by means of a specific focus on contextual and individual

causes of volatility and interactions between both.

The literature points at three important features of the party system in relation to electoral volatility.

Besides the number of parties and the ideological polarization of these parties, the electoral system as

well is regarded an important factor for explaining differences in levels of volatility between countries.

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As a first contextual variable the electoral system is expected to affect the degree of vote switching in

a particular country, for which three explanations can be given. First, an important explanation is

provided by the fact that strategic voting fosters vote switching. Since different electoral systems are

linked with varying levels of strategic voting, volatility should be related to the type of electoral

system as well. Strategic voting from a seat-maximizing or government formation maximizing

concern, implies switching according to strategic considerations and therefore volatility (Bischoff,

2012). Second, electoral systems in which clarity of responsibility is high, are characterized by the

presence of mechanisms of economic voting (Powell & Whitten, 1993). In these systems, voters are

likely to reward and punish the incumbent government for the state of the economy on election day

(Key, 1966; Lewis-Beck & Stegmaier, 2000). This voting mechanism as well can be expected to boost

vote switching. Third, electoral systems have been found to have a considerable impact on the degree

to which voters develop strong party attachments and as such affect the potential of vote switching.

Electoral systems where a limited number of parties compete for the median voter are characterized by

weaker party attachments compared to electoral systems that allow for a large variety of ideological

perspectives to be represented in parliament (Bowler, Lanoue & Savoie, 1994; Karp & Banducci,

2008).

Describing different electoral systems is usually done by means of a general typology of families of

electoral systems distinguishing between majoritarian, plurality, proportional and mixed systems

(Farrell, 2001). There are only very few countries with either majoritarian or plurality systems. Since

these types to a large extent produce the same dynamics, both groups are often considered jointly

(Karp & Banducci, 2008). In a majoritarian as well as in a plurality system, the electoral system tends

to be divided into two main blocks (Farrell, 2001). As such these electoral systems can be considered

as a catalyst for strategic voting (Bartolini & Mair, 1990; Bischoff, 2012). These systems are often

characterized by high clarity of responsibility as well (Powell & Whitten, 1993). Additionally, given

the fact that ideological extreme parties rarely win seats in majoritarian or plurality systems, these

types of electoral systems are not expected to be conducive to the formation of party attachments

either (Bowler et al., 1994). Given these dynamics, majoritarian and plurality systems can be expected

to be characterized by higher levels of volatility as opposed to systems with proportional or mixed

forms of representation.

The classification in two broad groups of electoral systems, majoritarian and plurality on the one hand

and proportional and mixed systems on the other hand, is rather crude. Therefore, as Bartolini and

Mair (1990) suggest, a more fine-grained picture of how electoral systems affect volatility can be

obtained by looking at a continuous measure of proportionality. Such a measure captures how votes

are translated into seats. Given differing district magnitudes and electoral thresholds across countries,

this translation can vary from very proportional to very disproportional (Gallagher, 1991).

5

Majoritarian and plurality systems are characterized by high disproportionality levels, given the fact

that they advantage larger parties.

The second and third context variables that will be investigated are the size and polarization of the

party system. A confusing element concerning these variables is that more electoral parties in an

electoral context was for a long time seen in party systems theory as being synonymous with a higher

likelihood at polarization. Most famously, Sartori (1976, 131-201) put a cut-off point at around five

relevant parties to distinguish between ‘polarized’ and ‘moderated’ pluralism (although the typology

came with several other criteria). Just as it was to Pedersen, it seemed self-evident to Sartori that “if so

many parties are to be perceived and justified in their separateness, they cannot afford a pragmatic

lack of distinctiveness.” (Sartori, 1976: 138). In previous empirical research therefore often one

variable was used to cover for both concepts. Investigating this relation, Crepaz (1990) initially found

evidence for a strong relation between polarization and the number of parties, but his measure of

polarization only took into account the distance between the most extreme parties and it therefore

ignored all the others parties’ positions or their electoral strength. With more refined measures it has

recently been shown that the number of parties in itself is not really indicative of a more extreme

positioning of parties (Dalton, 2008; Ezrow, 2008). Denmark for example has had up to five different

options that can be considered statistically separate on an ideological left-right dimension over a long

term period, whereas Italy has had more parties, but they are basically grouped into two clusters where

there is little ideological differentiation within the cluster (Budge & McDonald, 2006). In the elections

that will be used for this paper we also find no significant correlation between ENEP and our measures

of polarisation.i Therefore, we analyse the number of parties and polarisation as two separate

contextual variables affecting electoral volatility.

Considering the number of parties in a party-system, we would expect there to be a linear

relationship between the number of parties and electoral volatility. More parties increase the

opportunity for voters to switch, since there are more options to choose from. Additionally, with the

number of parties, the likelihood increases that a voter has an alternative to his previous choice that is

worthy of her vote. As Blais and Gschwend (2010) contend, this is the minimal condition to switch

from one’s preferred party to begin with. This expectation might run counter the hypothesized link

between majoritarian electoral systems and high volatility, since the ENEP is mostly low in

majoritarian/plurality systems (Gallagher, 1991; Lijphart, 1999).

Third, the degree of polarization in an electoral context is assumed to strongly affect volatility. The

theoretical expectation is that a higher polarization will lead to less vote-switching. The logic is

derived from the idea that Downs (1957) defended in his Economic Theory of Democracy: people will

pick out the party that is closest to them in the issue space, and this is most often perceived as a one-

dimensional left-right issue space (see also Clarke, Sanders, Stewart & Whiteley, 2004; Bartolini &

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Mair, 1990). If polarization is high, the ideological spacing between parties will be higher and hence

the distance to the next party will increase, making a switch less likely and vice versa (Hazan, 1997).

In the words of Sartori (1976: 138) on polarized systems: “it is tantamount to saying that cleavages

are likely to be very deep [...] Briefly put, we have polarization when we have ideological distance (in

contradiction to ideological proximity).”. There is indirect evidence supporting this idea. The

correlation between a voter’s placement on a left-right scale and the position of the party they vote for

is significantly higher in elections with a higher polarization (Dalton, 2008). This indicates that when

voters are offered a more diverse set of options, they will find a party that is closer to their own

position and therefore we would assume they will have less reason to switch than in less polarized

systems. Similarly the relation between self-placement on a left-right scale and party-affiliation was

found to be higher in polarized party-systems (Freire, 2008). This is also mirrored by research

showing that countries with more independent voters are less polarized (Curini & Hino, 2012). Other

scholars have demonstrated that systems with higher polarization increase turnout as they offer more

distinct choices to voters and this would motivate a section of the electorate that would otherwise

abstain (Dalton, 2008; Steiner & Martin, 2012; Kriesi, Grande, Lachat et al. 2006; Marshall & Fisher,

2010).

A second element in strongly polarized systems that reduces volatility might also be found in the

‘supply’ side of the electoral spectrum. In highly polarized systems parties are more likely to be

organized around specific constituencies related to cleavages that are at the basis of this polarization

(Mair, 1995). This might refrain them from going into competition for certain parts of the electorate

that are considered “unattainable” (Bardi & Mair, 2008). Ezrow (2007) also finds that parties’ policy

dispersion will mainly follow movements in dispersion among the electorate in disproportional

systems and less so in proportional systems, equally indicating that especially small parties are less

bound to go vote-seeking among the general electorate in these systems and opt for a focus on a more

faithful constituency. These parties therefore don’t try to convince voters to switch parties. Hazan

(1997: 164) in his analysis of center-parties ties polarization directly to the strategic behavior of non-

center parties as he states that “polarization is the byproduct of the moderate parties’ choice of

centrifugal over centripetal tactics to attack the center.”. Conversely, as Mair (1995) contends, if

parties lose their distinct profiles compared to each other, they will end up fishing in the same pool of

voters, and this makes it more likely that these voters will switch for more valence based differences

such as a more attractive leadership (Deegan-Krause & Zsolt, 2010). The process of dealignment

observed across the Western industrial world might thus fundamentally change parties’ behavior in

highly polarized systems. While some decades ago parties could focus on their own constituencies, the

waning of the cleavage structure urges parties to appeal to all voters.

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Bartolini & Mair (1990) found a weak negative effect of ideological distance between parties on

volatility, but they used aggregate data which only allows a rudimentary estimation of the real net

volatility. As mentioned above, Pedersen found a relationship between the number of electoral parties

and volatility, but he did not test polarization separately or on individual level data. The reasoning of

theories about the effects of the number of parties, polarization and the electoral system, however, is

clearly at the individual level. Therefore testing these contextual theories by means of individual data

is a prerequisite for understanding these mechanisms as well.

Following this review of the theory, the three contextual hypotheses that will be investigated in this

paper are therefore:

Hyp1: Volatility will be higher in highly disproportional electoral systems

Hyp2: An increasing number of parties will lead to higher volatility

Hyp3: Volatility will be suppressed by polarization in the party system

Contextual theories explaining electoral volatility have not yet been tested at the individual level.

Additionally, despite a long tradition of research, we know relatively little about the individual

characteristics of volatile voters (Kuhn, 2009). The first research on what causes voters to switch party

preferences resulted in a rather gloomy picture of volatile voters: ‘Stability in vote is characteristic of

those interested in politics and instability of those not particularly interested’ (Berelson, Lazarsfeld &

McPhee, 1963: 20). Still focusing on elements of political sophistication, Russell Dalton’s theory of

cognitive mobilization provides a more optimistic account of what characterizes volatile voters.

Dalton describes a process of dealignment in the electorate, which he considers to be caused by rising

levels of education and the considerable growth of political information available to voters. No longer

in need of the cognitive shortcuts party alignments provided, these new ‘apartisans’ are potentially

more volatile (Dalton, 1984; Dalton et al. 2000; Dalton, 2007). The implication that high levels of

political sophistication are linked to more vote switching have not remained uncontested. Aiming to

settle the debate, recent findings suggest that a curvilinear pattern can be observed, with the middle

groups being most volatile (Lachat, 2007; Kuhn, 2009).

A second approach focuses on political attitudes to understand what makes people switch votes.

Scholars seem to agree that disaffection with politics is associated to volatility. With his ‘frustrated

floating voter hypothesis’, Zelle (1995) drew attention to the fact that volatile voters are less satisfied

with the political system and less trusting in politics. Recent findings seem to confirm this pattern of

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disaffection leading to volatility for a number of different countries (Dalton & Weldon, 2005;

Dassonneville, 2012; Söderlund, 2008).

We posit two individual level hypotheses that will be investigated.

Hyp4: Volatility will be highest among voters with a middle level of political sophistication

Hyp5: Political disaffection increases the probability of volatility

Although research on the individual level characteristics of volatile voters has been done for a number

of countries by now, so far no one has systematically investigated how contextual factors affect these

patterns at the individual level. The research of Granberg and Holmberg (1991) on campaign volatility

suggests that differences therein might be substantive. Finding that volatile voters were mostly

uninformed in the United States but highly interested in Sweden, they argued that this contrast was

due to substantial differences in the electoral systems of both countries. While the majoritarian system

in the United States fosters a focus on candidates, the Swedish electoral system gives a considerable

weight to political parties as actors. This difference, then, explains why in both countries different

groups are influenced most during an election campaign. Clearly, investigating the interaction between

individual and contextual variables can shed light on why different mechanisms explaining volatility

can be observed by different scholars and in different countries.

According to the logic of Granberg and Holmberg (1991), we should expect an interaction effect

between political sophistication and the electoral system. Political sophistication is expected to

decrease the probability of vote switching in majoritarian or plurality systems but to increase the

probability of volatility in proportional electoral systems. Furthermore, given the fact that it requires

some level of political sophistication for a voter to know how parties position themselves in the

ideological space, we also expect the effect of polarization to be more pronounced among the higher

politically sophisticated. Additionally we could question whether the mechanism of disaffection

causing volatility holds in different electoral systems as well.

3. Data & Methods

3.1. Data

We will test the hypotheses outlined above using the Comparative Study of Electoral Systems (CSES,

2007, 2012). The CSES is a cross-national election survey that is administered in a standardized way

after elections in over fifty countries. This makes it a unique data-source to study effects of individual

and contextual elements on voting behavior and the interaction between both. Starting with the second

CSES module a recall question was administered to gauge for voting behavior in a previous election.

This allow us to compute the total amount of vote-change between elections.ii Unfortunately this

question is not added in all the CSES-surveys, and so we will use a subset of election studies in the

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CSES that asked this question. As we are only interested in seeing which variables differentiate people

that stay faithful to their party compared to those that switch, we only keep those respondents for

every election study that voted in both elections, a procedure which strongly reduced our sample.iii

This decision also means that the volatility calculated here differs in a fundamental way from that

calculated on aggregate data using the Pedersen index. The Pedersen index looks at the difference in

percentage score of the parties without taking into account who voted in previous elections. This

means a stark shift in the Pedersen index could be caused by a group of voters showing up that was

absent in previous elections and voting for one particular party or candidateiv. Our method of coding

volatility obviously does not capture this category of voters. Moreover, we restricted our sample to

European en other western-industrialized countries as the literature we test here relates to these

countries. We therefore excluded four election studies from the sample (Hong Kong, The Philippines,

Thailand and Uruguay)v. See Appendix A for all information on the dataset.

As Pedersen aptly noted on researching volatility between elections: “A serious problem for the

analysis springs from the occurrence of party splinterings and party mergers of various kinds.” This

problem has not gotten better since 1979 and the issue occurs both for aggregate measures of volatility

as well as for coding individual volatility. If a party splits or merges between two elections, a voter has

really no other option but to ‘change’. But in many cases, the new party will be very similar to the old

one and so it is debatable if the person is really to be counted in the group of volatile respondents. We

used a specific procedure to decide on these matters and who to label a respondent as a ‘party-

switcher’. For all elections in this paper we looked up the electoral report in Election Studies and if

extra documentation was needed we also included the country year report in European Journal of

Political Research. For some recent elections the OECD election report was consulted. Based on this

documentation we decided whether a party could be labeled as ‘new’ or a compilation/splintering of

former parties. The most straightforward cases are those elections where no significant change

occurred in the party landscape, such as Norway in 2001 or the United Kingdom in 2005. Any

respondent that voted for a different party in these elections is labeled 1 (‘switcher’). But the CSES

also comprised a good number of countries where there have been serious changes in the party system.

A good example is the dramatic Israeli election of 2006 where Ariel Sharon as acting prime minister

left Likud in 2005 and formed Kadima to challenge his old party. In these cases we looked whether a

party was made up out of former parties joining or making an electoral coalition under one name, and

the former voters of these parties are labeled as stable. If a party is labeled ‘new’ (As is Kadima

because Likud still ran in the same 2006 election) we label their voters as ‘switcher’. Using this

information we drafted a code for every country that had some change in its party system to label a

respondent as a switcher or not. Appendix B reports coding for all the elections in our paper with

references to the election specific sources. A second element here is that several of the CSES election

studies had a category for ‘other party’ or ‘independent candidate’. We decided to keep any

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respondent that went from one of these categories to a named party and vice-versa. We coded the

remainder (going from ‘other’ to ‘other’) as missing as we did not know whether they voted for the

same party or not as this is a generic category. This was only the case for less than 100 respondents

over the whole dataset.

3.2. Measurements

3.2.1. Institutional variables

Investigating the direct effect of the electoral system on electoral volatility, we make use of a measure

of disproportionalism. As such, making use of a continuous measure (Bartolini & Mair, 1990), we

have a more detailed image on the dynamics of volatility within different electoral systems than when

merely comparing majoritarian and plurality systems on the one hand and proportional and mixed

systems on the other hand. We make use of Gallagher’s least squares index, measuring

disproportionality between votes and distributed seats (Gallagher, 1991).

For the measurement of the number of parties we make use the ‘effective number of electoral parties’

index as composed by Laakso & Taagepera (1979). The formula being (for n parties with p as a

proportion of the vote)

For a measure of polarization there are more complexities that come into play and whereas the

Laakso-Taagepera-index has had few contenders in over 30 years, many different polarization

measures are used simultaneously in the literature. Three different types exist and to see how robust

the findings of our analyses are, we will perform them with an index from each of these types

separately. To compose a polarization measure one needs several pieces of information and all can be

composed differently using different sources. A minimal piece of information is the positioning of the

parties on one or more relevant dimensions. The left-right continuum is the most universally used

measure to place parties in the political issue space (Lijphart, 1999: 78-79; Crepaz, 1990: 171; Abedi,

2002: 558). There are numerous types of sources here that can be used to compute party placements

(Marks, 2007): analysis of party-manifesto’s such as the CMP-data (Budge & McDonald, 2006;

Franzmann & Kaiser, 2006), expert surveys as in the Chapel Hill data (Hooghe et al, 2010) or the

American State Departement (Sigelman & Nam Yough, 1978), media analysis (Kriesi, Grande,

Lachat, Dolezal, Bornschier, & Frey, 2006) or using the placement by voters in surveys (Dalton,

2008). We will use the latter option, this way we have the opinion of the electorate on where the

position of the parties is, regardless of the underlying dynamic that is behind these locations in issue

space. Because we study individual voter behavior this is our main interest. Recent scholarship (Freire,

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2008) shows that the left-right placement of individuals is still most strongly linked with their party-

choice and that in ‘newer’ European democracies the capacity of individuals to situate themselves on

the scale has noticeably improved making the scale increasingly reliable in comparative research using

surveys. It has equally been shown that respondents are capable of meaningfully placing different

parties on a same left-right issue scale (Budge & McDonald, 2006).

The first type of polarization indexes limit themselves to using these placements for calculating the

distance between parties as a measure of polarization. Most often the two most extreme parties on the

left and right side are used (Crepaz, 1990; Abedi, 2002). We will use an unweightened measure

gauging the distance between the two most extreme parties as an index of this type of polarization

index.

A second piece of information that can be added is the size of parties (most often the electoral result)

which leads to an index that takes into account the relative strength of the parties. This is the case of

the index that Russell Dalton (2008) uses based on the CSES survey data and that will be the second

type of index used in this paper. The formula uses information on the location of all of the parties and

their electoral size and is related to a measure for the standard deviation:

Ezrow (2007) argues that next to distance and size a third element that should be incorporated in a

polarization index is the diversity of voters themselves compared to parties. An indicator that

incorporates an element of variance of the electorate and keeps the full amount of information from all

parties is Alvarez & Nagler’s (2004) compactness measure. In their measure they do not only look at

how dispersed the parties are on a LR scale, but they relate this to the dispersion of the general

population, resulting in an index measuring ‘compactness’. Issues that are more compact are those for

which a large variance in the population are combined with a rather limited range in the positions of

the main parties. Although the measure was initially developed to look at separate issues, Ezrow

(2007, 2008) has demonstrated this can also be applied to the general ideological placement of voters

and parties, yielding a measure of ideological compactnessvi. We will also test this measure in a

separate analysisvii

:

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3.2.2. Individual variables

Political sophistication as a concept refers to voters’ level of conceptualization (Campbell, Converse,

Miller & Stokes, 1960). Voters with a high level of conceptualization have well organized and

developed political opinions, tend to be highly interested in politics and are more likely to participate

politically (Lachat, 2007; Luskin, 1990). Political sophistication is not easily measured and scholars

often have to rely on proxy variables for investigating the effects of political sophistication. Dalton

(1984; 2007), for example, makes use of an additive index of cognitive mobilization, composed of

voters’ levels of education and interest in politics. Although the CSES surveys did not include

questions on respondents’ interest in politics, knowledge questions were included. Since political

knowledge is an important and even called the most straightforward dimension of political

sophistication (Marthaler, 2008), we will use these measures of political knowledge to investigate the

effect of political sophistication on electoral volatility. Within the CSES each national survey includes

three political knowledge questions, which regularly deal with political institutions or specific political

actors. National surveyteams have to design the three questions in such a way that they are answered

correctly by two thirds, half and one third of the respondents respectively (Grönlund & Milner, 2006).

Adding the number of correct answers on the three questions gives for each respondent a political

knowledge score between 0 and 3.viii

For investigating the impact of political disaffection on volatility several related political attitudes can

be looked at. Previous research has shown that political trust, dissatisfaction with democracy and

external efficacy can all be linked to electoral volatility (Dalton & Weldon, 2005; Dassonneville,

2012; Söderlund, 2008; Zelle, 1995). The CSES does not include measures of political trust but

questions on satisfaction with democracy and measures of external political efficacy are included and

will be used in the analyses. Satisfaction with democracy is measured by means of a single item asking

respondents ‘On the whole, are you very satisfied, fairly satisfied, not very satisfied, or not at all

satisfied with the way democracy works in [country]’ (reverse coding). For external efficacy, we make

use of the items ‘who is in power can make a difference’ and ‘who people vote for makes a

difference’, both asked on a 1 to 5-scale. Given that these items load strongly on a one-dimensional

scale (Eigenvalue: 1.00; Explained variance: 50.02%), they were added into a 1 to 5 sumscale of

external political efficacy.

Additionally some socio-structural variables are controlled for, we include age and gender in the

analyses. Furthermore, respondents’ highest level of education is taken into account. This variable

consists of eight categories ranging from none to a university undergraduate degree and is treated as a

continuous variable.

Missing values in the dependent variable and for some of the independent variables considerably

reduce the dataset. Additionally, since measures for the individual independent variables were not

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included in all national surveys the number of countries included in the analyses is reduced as well.

Estimations are done on a sample of 25,531 individuals, nested in 32 electionsix. Elections in 25

different countries are included in the sample. Descriptive statistics on the independent variables are

listed in Table 1.

Table 1. Summary statistics of independent variables

N Mean Std. Dev. Min Max

Age 25,531 50.15 15.63 18 100

Female 25,531 0.50 0.50 0 1

Education 25,531 5.45 1.77 1 8

Political knowledge 25,531 1.79 0.96 0 3

External efficacy 25,531 3.51 0.92 1 5

Satisfaction with

democracy

25,531 2.64 0.87 1 4

Ls 32 4.84 3.53 0.72 16.73

ENEP 32 4.77 1.32 2.94 8.98

Max. left-right

distance

32 5.38 1.31 2.94 7.63

Polarization 32 3.69 0.93 2.03 5.89

Compactness 32 1.42 0.38 0.89 2.56

Data: CSES Module 2 and 3.

3.3. Method

Investigating what causes electoral volatility at the individual level by means of a binary dependent

variable, the analyses will take the form of logit analyses. Furthermore, making use of a broad cross-

national dataset, this source of heterogeneity in the dataset has to be taken into account. We should do

so because voters in a particular electoral context are more alike than voters in different contexts.

Since we are interested in mechanisms that operate both at the contextual level and at the individual

level, multilevel analysis techniques are called for. This approach has the additional advantage of

allowing to investigate whether the same contextual mechanisms hold for all individuals or vary

depending on individual-specific covariates. As a consequence, the method allows to integrate both

individual and contextual theories explaining volatility, an approach which is still not regularly taken

in voting behavior research (Steenbergen & Jones, 2002).

4. Results

Looking at the proportion of respondents that reports to have switched votes from one election to

another, there seems to be a considerable amount of variance between the elections included in the

data (Figure 1). While 29.55% of the whole sample changed parties, this varies from 10.42% in the

Spanish 2004 election to 61.55% in the 2006 election in Israel. Additionally, differences in the amount

14

of volatility between elections already hints at some effect of the electoral system; volatility is rather

low in the highly disproportional British system, but very high in the fairly proportional Israeli system.

Figure 1. Proportion of volatile voters by election

Data: CSES Module 2 and 3.

We start the analyses by having a look at the individual determinants of vote switching. Table 2

reports logit coefficients and standard errors of the models. Not yet including contextual variables, the

first two models are random intercept models, allowing for different levels of volatility in the elections

included. Model I tests for curvilinear effects of both age and political knowledge, which are both not

significant. Only including linear effects in Model II indicates that there is a significant negative effect

of political knowledge on vote switching, indicating that better informed voters are less likely to

switch votes from one election to another. This finding confirms the original floating voter hypothesis

(Berelson et al. 1963), for inter-election volatility. Therefore contrary to our fourth hypothesis, there is

a linear effect of political sophistication, with higher levels of political sophistication decreasing the

likelihood of volatility. Quite remarkably, the effect of education is in the opposite direction. Higher

educated voters are significantly more likely to switch votes. These contrasting results are reason for

caution when investigating the effect of political sophistication by means of indices including levels of

education. This is for example the case in Dalton’s cognitive mobilization index (1984; 2007). Clearly,

two variables that can both be expected to be indicators of voters’ level of political sophistication

operate in opposite directions. Furthermore, age is significantly related to volatility. Not surprisingly,

the older voters are, the less they are inclined to switch votes. Results further confirm Zelle’s (1995)

hypothesis of political disaffection causing electoral volatility. For external efficacy as well as for

satisfaction with democracy, significant negative effects can be observed. This indicates that it is

0

.2

.4

.6

.8

Pro

port

ion

of

vo

latile

vo

ters

AU

S 0

4C

HE

03

CZ

E 0

2D

EU

02

DE

U 0

9D

NK

07

ES

P 0

4E

ST

11

FIN

03

FIN

07

GR

C 0

9H

RV

07

HU

N 0

2IR

L 0

2IS

L 0

7IS

L 0

9IS

R 0

3IS

R 0

6IT

A 0

6N

LD

06

NO

R 0

1N

OR

05

NZ

L 0

2N

ZL 0

8P

OL

01

PR

T 0

2R

OU

04

SV

K 1

0S

VN

04

SW

E 0

2S

WE

06

UK

05

15

especially voters with low levels of external political efficacy and voters that are dissatisfied with how

democracy works in their country that change vote choices. We can therefore confirm our fifth

hypothesis, political disaffection indeed increases the probability of vote switching. Given the

heterogeneity in the data, with individuals from a number of countries over Europe, contextual

variables might be important to incorporate in order to increase the explanatory power of the models.

The necessity of doing so is also apparent from the variance at the election level. When including the

individual covariates, almost 28% of the variance is situated at the level of elections.

Model III to V include, besides the individual covariates, the contextual variables we are interested in.

The least squares index for disproportionality (Ls) and the Laakso-Taagepera measure for the effective

number of parties (ENEP) are included in all models. Additionally, for assessing the impact of how

polarized a party system is, the three variables presented in the literature section are included one by

one in the models. As can be observed, the significant negative effect of disproportionality is robust

across the three models. Contrary to the theory that more disproportional systems would increase vote

switching, because of the presence of incentives for strategic and economic voting and because of

weak party attachments, disproportionality actually decreases vote switching. This effect is significant

even though the number of parties in an electoral system, which could be argued to confound the

effect of disproportionality, is controlled for in the analyses. Therefore, we have to reject our first

hypothesis. For the size of the party system, results are in expected directions in all models. As

hypothesized (Hypothesis 2), the more options voters have in the polling booth, the higher chances for

vote switching.

Looking at the effect of polarization then, none of the measures included in the analyses is

significantly related to chances for vote switching in the dataset. The effect of the maximum

ideological distance between two parties in a system is negative, as expected. This effect does not

reach a conventional level of significance, however. Furthermore, for the Dalton measure of

polarization, the effect is not significant and not even in the expected negative direction. For Alvarez

en Nagler’s measure of compactness, we hypothesized a positive effect on volatility. We did so

because the more alike the ideological positions of the parties are to the ideological positions of the

electorate, the higher the probability of voters to find different parties close to their own opinions and

therefore to switch parties. As can be observed in Model V, however, compactness is not significantly

related to vote switching either, although the effect is in the direction expected. The non-significant

effects of the three polarization measures tested therefore lead us to reject our third hypothesis as well.

Volatility does not appear to be suppressed by polarization in the party system.

Simulating quantities of interest allows to visually present the effects estimated in the analyses.

Starting from the estimated effects in Model V 10,000 simulated observations were drawn from a

16

random normal distribution. The estimated probability of volatility was then calculated for different

quantities of the independent variables focused upon.x

The estimated effect of how disproportional an electoral system is (as estimated in Model V) is

presented in Figure 2. How votes are translated into seats obviously matters; the more disproportional

this process, the lower the probability of a voter to switch parties from one election to another. This

effect is quite strong, in highly proportional systems with a least squares index of 1, voters have

probability of about 33% to switch votes. In a very disproportional system, with a least squares index

of 16, this probability is reduced to 17%. It should be noted however, that given the limited number of

highly disproportional elections in the dataset, confidence intervals for high levels of the Ls-index are

fairly wide.

17

Table 2. Explaining electoral volatility: multilevel logit regression coefficients

Model I Model II Model III Model IV Model V

Coef. (s.e.) Coef. (s.e.) Coef. (s.e.) Coef. (s.e.) Coef. (s.e.)

Individual level

Age -0.006 (0.006) -0.014*** (0.001) -0.014*** (0.001) -0.014*** (0.001) -0.014*** (0.001)

Age² -0.000 (0.000)

Female -0.010 (0.029) -0.009 (0.029) -0.009 (0.029) -0.009 (0.029) -0.009 (0.029)

Education 0.042*** (0.009) 0.043*** (0.009) 0.042*** (0.009) 0.042*** (0.009) 0.042*** (0.009)

Political knowledge -0.014 (0.053) -0.066*** (0.017) -0.066*** (0.017) -0.066*** (0.017) -0.066*** (0.017)

Political knowledge² -0.017 (0.015)

External efficacy -0.099*** (0.018) -0.099*** (0.018) -0.098*** (0.018) -0.098*** (0.018) -0.098*** (0.018)

Satisfaction with

democracy

-0.165*** (0.017) -0.165*** (0.017) -0.166*** (0.017) -0.166*** (0.017) -0.166*** (0.017)

Contextual variables

Ls -0.052* (0.026) -0.046° (0.025) -0.058* (0.027)

ENEP 0.161* (0.066) 0.171* (0.067) 0.134° (0.072)

Max. left-right

distance

-0.041 (0.061)

Polarization 0.005 (0.085)

Compactness 0.245 (0.230)

Model fit statistics

N individuals 25,531 25,531 25,531 25,531 25,531

N elections 32 32 32 32 32

BIC 29,658 29,640 29,655 29,656 29,565

Elections σ² 0.278 0.278 0.168 0.171 0.165

Significance levels: ° p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001. Maximum likelihood estimation using adaptive Gauss-Hermite approximation with 11 quadrature points

in Stata. Data: CSES Module 2 and 3. Level-2 variance in the nullmodel (σ²null): 0.290.

18

Figure 2. Estimated effect of disproportionality on electoral volatility

Estimated probability of vote switching for different levels of Ls, based on 10,000 simulated observations from

Model V. Data: CSES Module 2 and 3.

The effect of the effective number of parties in an electoral system on electoral volatility appears to be

even stronger. As evident from Figure 3, the proportion of voters changing votes is estimated to range

between about 22% when the ENEP is two to over 40% in systems where there are nine effective

electoral parties. Uncertainty about the estimated effect of the number of parties, however, is

particularly high for large party systems of which only very few are included in the dataset.

0

.1

.2

.3

.4

.5

Pro

bab

ility

of

vo

latilit

y

0 2 4 6 8 10 12 14 16

Ls

19

Figure 3. Estimated effect of effective number of parties on electoral volatility

Estimated probability of vote switching for different levels of ENEP, based on 10,000 simulated observations

from Model V. Data: CSES Module 2 and 3.

Not finding a significant effect of the degree of polarization of an electoral system on probabilities of

vote switching might suggest that there is simply no effect of polarization on volatility. The non-

finding might also be a consequence of a poor operationalisation of the concept. Using three different

measures to capture polarization, however, we limited the impact of measurement-specific issues. A

third possible explanation or the non-finding is that the effect of polarization on volatility varies

between individuals. For polarization to affect voting behavior, voters should be aware of where

parties are situated in the ideological space. Given that this is cognitively demanding, we might

therefore hypothesize this effect to be dependent on how politically sophisticated voters are.

Additionally, based on theories of strategic voting, we expected more vote switching in highly

disproportional systems. The effect of the least squares index on volatility is in opposite directions,

however. Given that strategic voting as well is quite demanding, we can expect this effect as well to be

dependent on voters’ level of political sophistication.

Investigating such varying effects, cross-level interactions are tested. Therefore, we start from Model

Vxi in Table 2 and add interactions between political knowledge and disproportionality, the effective

number of parties and compactness. Doing so, we test for the three types of contextual variables

whether their effect varies depending on voters’ level of political sophistication. As results in Table 3

make clear, the cross-level interactions are not significant, indicating that the effects of the contextual

variables do not vary according to voters’ level of political sophistication. Clearly, when finding

effects of institutional variables, these effects hold for the whole electorate and are not dependent on

0

.1

.2

.3

.4

.5

Pro

bab

ility

of

vo

latilit

y

2 3 4 5 6 7 8 9

ENEP

20

what voters know about the political system. This is apparent from the variance coefficient for the

effect of political knowledge, even before including cross-level interactions variance is limited to 0.2%

only.

21

Table 3. Explaining electoral volatility: multilevel logit regression coefficients (cross-level interactions)

Model VI Model VII Model VIII

Coef. (s.e.) Coef. (s.e.) Coef. (s.e.)

Individual level

Age -0.014*** (0.001) -0.014*** (0.001) -0.014*** (0.001)

Female -0.009 (0.029) -0.009 (0.029) -0.009 (0.029)

Education 0.042*** (0.009) 0.042*** (0.009) 0.042*** (0.009)

Political knowledge -0.100** (0.030) 0.045 (0.073) -0.128° (0.075)

External efficacy -0.099*** (0.018) -0.099*** (0.018) -0.099*** (0.018)

Satisfaction with democracy -0.166 *** (0.017) -0.166*** (0.017) -0.166*** (0.017)

Contextual variables

Ls -0.071 * (0.028) -0.059* (0.026) -0.058* (0.026)

ENEP 0.138° (0.072) 0.176* (0.075) 0.140° (0.072)

Compactness 0.237 (0.229) 0.236 (0.229) 0.152 (0.245)

Cross-level interactions

Ls*Political Knowledge 0.008 (0.005)

ENEP*Political Knowledge -0.023 (0.014)

Compactness*Political

Knowledge

0.046 (0.052)

Model fit statistics

N individuals 25,531 25,531 25,531

N elections 32 32 32

BIC 29,674 29,673 29,675

Elections σ² 0.158 0.157 0.157

Political knowledge σ² 0.001 0.001 0.001

Significance levels: ° p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001. Maximum likelihood estimation using adaptive Gauss-Hermite approximation with 11 quadrature points

in Stata. Data: CSES Module 2 and 3. Level-2 variance in the nullmodel (σ²null): 0.290. Original variance in effect of political knowledge: 0.002.

22

Additionally, besides theories linking political sophistication to electoral volatility, political

disengagement has been shown to explain vote switching as well. As apparent from results in Table 2,

external political efficacy and voters’ level of satisfaction with democracy are indeed strong predictors

of volatility. With regard to political disengagement as well, we might wonder whether the same

mechanisms hold and function likewise across the electoral contexts observed. Therefore, focusing on

satisfaction with democracy as the key variable measuring political disengagement, cross-level

interactions with the contextual variables are tested as well.

As is clear from Table 4 there is a significant interaction effect between satisfaction with democracy

and the number of political parties in an election. For disproportionality and compactness no

significant interactions can be found. The effect of satisfaction with democracy therefore varies

depending on how many parties voters can choose between. In order to understand this positive and

significant interaction effect, some quantities of interest were simulated and graphically presented in

Figure 4.xii

The figure illustrates the effect of the number of parties for voters who are not at all

satisfied with the way democracy functions and for voters who are very satisfied with the functioning

of democracy in their country. As can be seen, for the very dissatisfied voters, the number of parties

hardly affects their probability of switching parties. Dissatisfied voters have a high probability of

switching overall. For satisfied voters, on the other hand, the number of parties has a strong positive

effect on their probability of volatility. The interaction effect clearly illustrates that reward-punishment

mechanisms function mostly in contexts where clarity of responsibility is high. As can be observed, in

a context with a limited number of parties, where clarity of responsibility can be suggested to be high,

whether or not voters are satisfied with democracy matters a lot for their probability of switching

parties. In low clarity of responsibility contexts on the other hand, with more than six effective parties,

whether or not voters are satisfied does not affect their chances of switching. In such an electoral

system that is crowded with parties, all voters have a high probability of switching and these switches

are not dependent on how satisfied voters are with democracy.

23

Figure 4. Estimated effect of effective number of parties on electoral volatility by level of

satisfaction with democracy

Estimated probability of vote switching for different levels of ENEP, based on 10,000 simulated observations

from Model X. Data: CSES Module 2 and 3.

Satisfaction = 1

Satisfaction = 5

0

.1

.2

.3

.4

.5

.6

Pro

bab

ility

of

vo

latilit

y

2 3 4 5 6 7 8 9 10

ENEP

24

Table 4. Explaining electoral volatility: multilevel logit regression coefficients (cross-level interactions)

Model IX Model X Model XI

Coef. (s.e.) Coef. (s.e.) Coef. (s.e.)

Individual level

Age -0.014*** (0.001) -0.014*** (0.001) -0.014*** (0.001)

Female -0.007 (0.029) -0.006 (0.029) -0.007 (0.029)

Education 0.041*** (0.009) 0.041*** (0.009) 0.041*** (0.009)

Political knowledge -0.066*** (0.017) -0.066*** (0.017) -0.066*** (0.017)

External efficacy -0.097*** (0.018) -0.097*** (0.017) -0.097*** (0.018)

Satisfaction with democracy -0.138** (0.050) -0.444*** (0.104) -0.267* (0.117)

Contextual variables

Ls -0.055° (0.028) -0.064* (0.025) -0.067** (0.026)

ENEP 0.048 (0.070) -0.002 (0.071) 0.048 (0.070)

Compactness 0.315 (0.220) 0.311 (0.219) 0.238 (0.237)

Cross-level interactions

Ls*Satisfaction with democracy -0.009 (0.009)

ENEP*Satisfaction with

democracy

0.055** (0.021)

Compactness*Satisfaction with

democracy

0.063 (0.080)

Model fit statistics

N individuals 25,531 25,531 25,531

N elections 32 32 32

BIC 29,650 29,644 29,651

Elections σ² 0.107 0.110 0.106

Satisfaction with democracy σ² 0.016 0.013 0.016

Significance levels: ° p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001. Maximum likelihood estimation using adaptive Gauss-Hermite approximation with 11 quadrature points

in Stata. Data: CSES Module 2 and 3. Level-2 variance in the nullmodel (σ²null): 0.290. Original variance in effect of satisfaction with democracy: 0.016

25

5. Conclusions

We started the paper from the conclusion that contextual theories of electoral volatility are usually

explained by mechanisms at the level of individual voters. Despite this reasoning, the link between

contextual variables and electoral volatility has so far only been tested at an aggregate level.

Addressing this gap in the literature and by means of a large scale and cross-country dataset of voter

surveys, we investigated whether disproportionality, the number of parties and polarization can be

related to volatility at the voter level as well. Additionally, we incorporate the impact of individual

characteristics and the interaction between these and the contextual variables as well in our analyses.

At the individual level, contrary to suggestions of curvilinear effects of political sophistication on

volatility, we found a significant linear effect of political knowledge. More knowledgeable voters

switch parties significantly less. Surprisingly, the effect of education is in opposite directions.

Therefore, our findings suggest that how political sophistication is operationalized can be expected to

affect results investigating the link between sophistication and electoral volatility. For theories of

political disengagement, results were in expected directions. Satisfaction with democracy and high

levels of external political efficacy suppress vote switching.

For the institutional variables, contrary to what was expected, voters in highly disproportional systems

are not significantly more volatile. Although scholars reason that more strategic voting and weaker

party attachments in disproportional systems are conducive for volatility, there is on the contrary less

volatility when levels of disproportionality are higher. The process of dealignment of the past decades

might be an explanation for not finding more vote switching in more disproportional systems. As party

attachment across the Western industrialized have weakened, differences with the highly

disproportional systems in this regard might have become negligible.

The number of parties in an electoral system is, as hypothesized, a significant predictor of volatility.

The more options voters have in the polling booth, the higher their probability of switching parties.

Contrary to the reasoning framed by Pedersen for explaining this, differences in levels of polarization

do not explain this difference. Even though we used three different measures of polarization, none of

them was found to be significantly related to levels of volatility. In this regard, the use of a one-

dimensional LR scale implies a reduction of the party-space and the options voters have in any given

election. Although many authors contend that it still captures the most salient dimension of voter

placement compared to parties, other authors state multidimensional scales would do better. Especially

Kriesi et al (2006), show using newspaper data that the cultural axis has become more salient in recent

times. By using media analysis, they draw the issue space as it is presented to voters rather than what

is found in manifestos or pre-defined survey scales. It would be interesting for future research to add

these multidimensional scales to these analysis.

26

Investigating cross-level interactions between the contextual variables and voter specific covariates,

we did not find voters’ level of political knowledge to affect reactions to the institutional context.

Therefore, how voters respond to the electoral system is a general mechanism and does not seem to

depend on voters’ understanding and knowledge of politics. When looking at disengagement with

politics we do find a significant cross-level interaction with the number of parties in an electoral

system. Clearly, in small party systems, satisfaction with democracy determines to a large extent

whether or not voters switch parties from one election to another. In large party systems, on the other

hand, no significant differences in levels of volatility can be found between very satisfied and

dissatisfied voters. This finding suggests that when voters have a lot of parties to choose between,

switching is no longer driven by dissatisfaction. This obviously raises the question what reasons they

do switch for in such a context.

27

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APPENDIX A

module Election N Voted_now Vote_last Voted_both N_analysis

2 Australia 2004

1769 1625 1628 1525 1525

3 Croatia 2007 1004 811 732 663 571

2 Czech Republic 2002 948 695 537 489 476

3 Denmark 2007 1442 1390 1367 1332 1259

3 Estonia 2011 1000 790 638 575 434

2 Finland 2003 1196 957 903 821 707

3 Finland 2007 1283 1066 1048 970 839

2 Germany 2002 2000 1878 1784 1710 1569

3 Germany 2009 2095 1645 1571 1418 1258

3 Greece 2009 1022 902 897 823 653

2 Hungary 2002 1200 992 964 866 783

3 Iceland 2007 1595 1373 1289 1190 1023

3 Iceland 2009 1385 1266 1200 1167 940

2 Ireland 2002 2367 2019 1808 1652 1297

2 Israel 2003 1212 1081 981 906 758

3 Israel 2006 1200 965 945 836 645

2 Italy 2006 1439 1102 NA NA 499

2 New Zealand 2002 1741 1402 1456 1243 1243

3 New Zealand 2008 1149 1064 920 893 886

2 Norway 2001 2052 1698 1662 1458 1449

3 Norway 2005 2012 1795 1654 1555 1453

2 Poland 2001 1794 1035 1211 855 678

2 Portugal 2002 1303 971 973 869 704

2 Romania 2004 1913 1504 1307 1155 818

3 Slovak Republic 2010 1203 939 987 836 747

2 Slovenia 2004 1002 758 742 648 410

2 Spain 2004 1212 1077 938 897 779

2 Sweden 2002 1060 937 861 810 745

3 Sweden 2006 1547 1398 1306 1234 1094

2 Switzerland 2003 1418 1048 1003 908 355

3 The Netherlands 2006 2359 2196 1966 1922 1850

2 United Kingdom 2005 860 620 623 549 540

32

APPENDIX B

The reference brackets refers to the source(s) where the election report can be found. ES stands for the

journal Electoral Studies, EJPR for European Journal of Political Research

Australia 2004 (ES: Vol. 24, 3, pp. 545–551)

The coalition between Liberal and National might make some voters switch their first choice on the

ballot purely out of strategic reasons if the chance of winning has drastically changed in a

constituency, but we doubt this will happen more than on a very rare occasion.

New Zealand 2002 (ES: Vol. 23, 1, pp. 149–155)

Between 1999 and the current election United Future, formed in 2000 through a merger between the

United Party and the Christian-based Future NewZealand Party. So we consider previous voters from

those parties that now vote United Future as non-switchers

Czech Republic 2002 (ES: Vol. 22, 4, pp. 772–778)

Before the 1998 local and Senate elections, discontent with the opposition agreement led to the

formation of the so-called Quad Coalition of four smaller parties: the Christian Democrats (KDU-

ČSL), the Freedom Union (US), the Civic Democratic Alliance (ODA), and Democratic Union

(DEU). The Quads split again at the beginning of 2002. The ODA left the Quads. Just before the

ODA departed, the US and DEU merged, so the Quad Coalition then had only two parties and was

renamed simply the Coalition. Voters coming from the original 1998 ‘Quad’ that vote for one of the

resulting parties that split from the Quad or the Coalition that is the remainder of the Quad are non-

switchers.

Denmark 2007 (EJPR: Vol. 47, pp. 952-961)

A new party (New Alliance) was founded by a number of MP’s from existing parties, but since the

original parties still run separately we consider this not to be a party connected to the previous ones.

People voting for New Alliance are coded as switchers.

33

Norway 2005 (ES: Vol. 26, 1, pp. 219–223)

Although parties ran as possible future coalition-blocks both on left and right, all parties kept running

separately in the election.

Hungary 2002 (ES: Vol. 22, 4, pp. 799–807)

Fidesz-MPP and MDF joint forces in 2002, so previous voters from both parties voting for this block

are non-switchers. Although some MP’s of the small FKGP ran for Fidesz-MPP/MDF in 2002, we

follow the EJPR (volume 43, pp. 968-969) and consider it as a separate party.

Slovak Republic 2010 (ES: Vol. 31, 1, pp. 222–225)

Most-Híd, ran as a new party, but was led by Béla Bugár, the former long-time head of the Party of

the Hungarian Coalition (SMK). We therefore consider voters for Most-Hid coming from SMK are

non-switchers

Croatia 2007 (ES: Vol. 27, 4, pp. 752–755)

The HSS and HSLS parties formed the so-called ‘yellow-green’ coalition. Voters for HSS and HSLS

in previous election are non-switchers when voting on the coalition of both parties in the 2007 election

Iceland 2007 (ES : Vol. 27, 2, pp. 373–377)

A new party entered the electoral arena (Iceland Movement), but was not a split from an existing

party. Any new voters for this party are considered switchers.

Israel 2003 (ES: Vol. 23, 2, pp. 353–360 & EJPR: Vol. 43, pp. 1033-1040)

A number of smaller parties had splits or coalitions such as One Nation (Am Ehad), but it had no

voters in the CSES, so there is no recoding. The same goes for the Green Leaf (Ale Yarok) party and

for Herut (The National Party). Similarly Labour-Meimad 2003 was Yisrael Ahat in 1999 and

although CSES has a code for Labour-Meimad, there were no voters. Yisrael Ahat was not coded

34

separately by CSES. The parties Meimad en Gesher that also formed Yisrael Ahat have no voters in

CSES. National Unity-Israel our Home was two separate parties in 1999. Herut that was part of this

group ran separately in 2003.

Iceland 2009 (ES: Vol. 29, 3, pp. 523–526)

The Icelandic movement became a part of the Social Democratic Alliance for the 2009 election, so

voters for Icelandic movement going to SDA are non-switchers.

Poland 2001 (ES : Vol. 22, 2, pp. 367–374 & EJPR : Vol. 41, pp. 1057-1067)

SLD and UP voters 1997 are stable if they voted for the SLD-UP coalition in 2001. AWS voters in

1997 are considered stable if they voted for the AWSP (name change of what was left after several

splinter groups left the party) in 2001. Since a lot of splinter parties emerged and these new parties

were not homogeneous (MP’s from different parties joined the new formations PO and PiS), we do not

consider voters switching to all of these parties as stable. Additionaly, voters for Polish Accord

(Porozumienie Polskie) in 1997 now voting for LPR could also be considered stable, but the only

voter for that party in 1997 in the CSES did not vote in 2001.

Ireland 2002 (ES: Vol. 23, 1, pp. 155–160)

Democratic Left and Irish Labour Party merged ‘Irish Labour Party’ in 2002 and voters from these

parties going to Labour are considered stable.

Israel 2006 (ES: Vol. 26, 3, pp. 707–711 & EJPR: Vol. 46, pp. 984-992)

On 20 November 2005, Israels prime minister Sharon resigned from Likud and announced his

intention to lead a new political party: Kadima (Forward). We consider it to be a new party (as does

Abraham in the EJPR yearbook) . We consider 2003 voters of Labour/Meimad and One Nation are

stable if they voted Labour/Meimad in 2006 and same for voters from National Union/Yisrael Beitenu

if they voted Yisrael Beitenu in 2006. The 2003 voters of Ra’am are also stable if they voted

Ra’am/Ta’al in 2006.

35

Estonia 2011

There was no entry yet in ES or EJPR for this election, but the OECD election reportxiii

does not

mention any major changes in the party system

Romania 2004 (ES : Vol. 25, 2, pp. 409–415)

At a joint congress in September 2004, the Social Democrats agreed to establish an electoral alliance

with the Humanist Party of Romania (Partidul Umanist din România, PUR) called the National Union

(Uniunea Naţională). Equally the PNL (National Liberal Party) and the Democratic Party (PD) went

into the election togheter. Respondents that voted for parties that went into the election as a coaltion

are stable if they come from one of the parties forming that coalition.

Slovenia 2004 (Vol. 25, 4, pp. 825–831)

The SKD (Slovenian Christian Democrats) were still named on the ticket in 2000 but they were

already then de facto in the SLS. The New Slovenia party is a breakaway from this union by the

former SKD leader, but since both already took part as separate parties in 2000 they are to be

considered distinct parties in both elections.

Italy 2006 (ES: Vol. 27, 1, pp. 185–190)

It is important to note that the 2006 election was done using a PR system, changing from the mixed

PR+Majoritarian system used since 1993. In the centre-left camp, Romano Prodi re-launched the

Olive Tree Federation, based on the Democratic Left (DS), Daisy (DL), and the Italian Social

Democrats (SDI). The Rose in the Fist is a ‘New Labour’ kind of party founded in 2005 from minor

parties including the Radicale (code 22). The UDC is a merger between the CCD and CDU, but since

those two already participated ‘merged’ in the 2001 election there is no recoding of respondents.

36

Elections with no significant changes in the party-system

*Norway 2001 (ES: Vol. 22, 1, pp. 179–185)

*United Kindom 2005 (ES: Vol. 25, 4, pp. 814–820)

*The Netherlands 2006 (ES: Vol. 26, 4, pp. 832–837)

*New Zealand 2008 (ES: Vol. 28, 3, pp. 507–510)

*Sweden 2002 (ES: Vol. 22, 4, pp. 778–784)

*Germany 2002 (ES: Vol. 23, 1, pp. 143–149)

*Germany 2009 (ES: Vol. 29, 2, pp. 289–292)

*Sweden 2006 (ES: Vol. 26, 4, pp. 820–823)

*Spain 2004 (ES: Vol. 24, 1, pp. 149–156)

*Finland 2007 (ES: Vol. 26, 4, pp. 797–803)

*Finland 2003 (ES: Vol. 23, 3, pp. 557–565)

*Portugal 2002 (EJPR: Vol. 43, pp. 1058-1066)

*Greece 2009 (ES: Vol. 29, 2, pp. 293–296)

i Correlation between ENEP and the maximum left-right distance is -0.081 (ns), correlation between ENEP and

the Dalton measure of polarization is -0.196 (ns) and correlation between ENEP and compactness is 0.313

(marginally significant at 0.1-level). ii Although recall questions tend to underestimate the true amount of volatility (Schoen & Falter, 2001), cross-

national panel data on elections is not available. While acknowledging possible underestimation of volatility,

relying on recall questions for investigating the individual determinants of electoral volatility between elections

is regularly done within the scholarly literature (Lachat, 2009). iii

Looking at the percentage of volatile voters in the elections for which we could code vote switching, the

percentage of switchers in Canada (87.64%) was implausible. Therefore the Canadian 2004 election was not

included in the analysis. iv One could refer to the 2002 election in The Netherlands where the populist LPF party gained 17percent of the

votes out of nothing, but that also saw a 6 percentage pointincrease in turnout. v We also removed Albania (2005) from the sample as the Freedom House index for that year only classifies it as

a ‘partly free’ democracy and the OSCE election report states that this election “complied, only in part, with

OSCE commitments and other international standards for democratic elections”. The elections of Bulgaria

(2001), Czech Republic (2006) and Poland (2007) were also not added for this paper as a control of the data with

the actual election result showed some differences when aggregating the responses for vote choice for the

‘previous’ election. These will be added to the analysis for this paper when these minor issues are resolved. The

Canadian 2004 survey had only 258 respondents out of 1674 that had information on their electoral choice in

both elections, which is a too large distortion of the original sample and the actual turnout to retain it (see

endnote iii on this). vi Note that Ezrow inverses this index to become a measure of dispersion rather than compactness as this is more

straightforwardly understood. We will use the original Alvarez & Nagler formula. vii

The pearson correlation between the Dalton and Alvarez & Nagler measure is -0,854, this high correlation

makes us predict that the difference in coefficient will probably be negligible. viii

Wrong answers, don’t knows and refusals to answer the questions were all treated equally as wrong.

37

ix

Elections included are the Australia 2004, Switzerland 2003, Czech Republic 2002, West Germany 2002,

Germany 2009, Denmark 2007, Spain 2004, Estonia 2011, Finland 2003 and 2007, United Kingdom 2005,

Greece 2009, Croatia 2007, Hungary 2002, Ireland 2002, Iceland 2007 and 2009, Israel 2003 and 2006, Italy

2006, the Netherlands 2006, Norway 2001 and 2005, New Zealand 2002 and 2008, Poland 2001, Portugal 2002,

Romania 2004, Slovak Republic 2010, Slovenia 2004 and Sweden 2002 and 2006. x For disproportionality, estimated probabilities were calculated for Ls 0.5 to 17, with delta 0.5. For the effective

number of parties, estimated probabilities were calculated for ENEP from 2 to 9, with delta 0.5. xi

The cross-level interactions were tested for the other measures of polarization (with Model III and Model IV as

the base model) as well. Neither for the maximum ideological distance nor for the Dalton measure of

polarization, there was a significant interaction effect with political knowledge. xii

Estimated probabilities were calculated for ENEP from 2 to 9, with delta 0.5. This was done both for a level of

satisfaction of 1 as well as for a level of satisfaction of 5. xiii

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