University of Amsterdam PRELIMINARY VERSION DO NOT CITE ... · Paper to be presented at the 7th ECPR General Conference, Bordeaux, 5-7 September 2013. 2 Abstract In recent decades,

What makes them switch?

The impact of the media on vote change

Linda Bos & Claes de Vreese

The Amsterdam School of Communication Research ASCoR

University of Amsterdam

PRELIMINARY VERSION

DO NOT CITE

Paper to be presented at the 7th ECPR General Conference, Bordeaux, 5-7 September

2013.

2

Abstract

In recent decades, the number of floating voters is rising, especially in European

democracies: an increasing number of voters change their preference from election to

election, or within election campaigns, signalling rising party system instability. The wave

of “Dutch volatility”, moreover, is exceptional in this case: from one of the least to one

of the most volatile electorates in especially in the last decade. Volatility is mainly studied

in relation to changes in the structure of electoral competition or voter emancipation.

Surprisingly, attention to the role of the media has been scarce, which is why this topic is

underdeveloped. Since we do know that media content affects voter turnout as well as

vote choice, we expect media to also affect vote change. However, this has never been

studied in a systematic way. This paper aims to fill this gap by moving beyond current

explanations of electoral volatility, incorporating the media in an extensive way. We use a

combination of a panel survey and an extensive content analysis of the Dutch 2010

election campaign, combining newspaper articles with TV broadcasts, and look at the

impact of media use as well as media content on electoral volatility. Following state-of-

the-art political communication research we not only look at across-the-board effects,

but also investigate differences between voters in the extent to which they are affected by

the media. The results show that media effects on electoral volatility are moderated by

sophistication, political interest and voter doubt..

3

Introduction

Observers and scholars of political behavior all agree that the volatility of electorates in

Western democracies is increasing (e.g., Dalton, 2000). Citizens today change more often

between candidates and parties between elections than earlier, and changes during the

campaign, dubbed intra-election or campaign volatility, have also increased in magnitude

(e.g., Dassonneville, 2012). Most research so far has treated volatility as an aggregate level

phenomenon looking at the overall change in electoral preferences and behavior. More

recent work has considered volatility at the individual level, but this research is scarce,

perhaps in particular due to the data requirements for really addressing this dynamic.

Needless to say, getting a better understanding of the antecedents of electoral volatility is

crucial for assessing the health of contemporary democracies.

One aspect that has generally been overlooked in the literature on electoral

volatility is the role played by the media. This is surprising since general accounts of media

and politics highlight the role of the media (e.g. Bennett & Entman, 2001) and the

literature on mediatization more specifically suggests that political actors have adapted to

the logic of the media and that the media have become the key intermediary between

political elites and citizens (e.g., Stromback, 2011). Extant research on volatility that

includes the media is scarce and has produced inconclusive findings. Some report

positive correlations between media use and volatility (e.g., Baker et al., 2006; Schmitt-

Beack & Partheymuller, 2012) while others report the opposite (Dassonneville, 2012).

While these studies are instructive in terms of getting a sense of the role played by the

media, neither of them were able to consider in more detail the actual contents of the

media for electoral volatility.

In the current paper we explicitly aim to fill the lacunae in prior research. We first

of all investigate electoral volatility at the individual level and secondly, we consider it

specifically as a function of exposure to different kinds of media contents, while

controlling for other important antecedents of volatility. In doing so, we follow the

general recommendations of Slater (2007) and the recent study by Dilliplane, Goldman

and Mutz (2013) on differentiating between exposure to different media outlets.

Specifically we also extend the works of for example De Vreese and Semetko (2004) who

incorporated content features of the media coverage in to a study of change in electoral

behavior.

4

Electoral volatility

Electoral volatility is generally defined as “changes in party preferences within an

electorate” (Crewe, 1985: p.8) Literature distinguishes between several types of volatility

in the electoral realm. Initially, scholars mainly looked at electoral volatility at the

aggregate level (f.i., Pedersen, 1979). This focus on the overall systematic shift in party

support is also called net volatility and has to be distinguished from volatility at the level of

the individual voter. This overall or gross volatility refers to the total amount of individual

vote switching (Crewe, 1985). Finally, on this level an important distinction has to be

made between inter- and intra-election (or campaign) volatility. Whereas the first refers to party

change between two subsequent elections, the latter refers to “changes in party

preferences, at aggregate and individual level, between a general election and a point

preceding the subsequent one” (Crewe, 1985, p.11).

Volatility is mostly studied on the aggregate level, looking at net volatility. Studies in this

context have found significant differences between electoral and party systems, and

sometimes relate differences to economic development rates (cf. Bartolini & Mair, 1990;

Pedersen, 1979; Roberts & Wibbels, 1999; Tavits, 2005). The topic has recently been

studied more extensively in Central and Eastern European countries (Tavits, 2008; Sikk,

2005), and has in those cases been related to serious democratic problems, such as party

system instability. Studies on individual sources of (gross) volatility are scarce, however

(Kuhn, 2009; Van der Meer et al, 2013). In those cases it is assumed to be related to

changes in the structure of electoral competition, such as ideological dealignment, and

realignment, (Aarts and Thomassen, 2008; Irwin and Van Holsteyn, 2008; Pennings and

Keman, 2008, Van der Meer, Lubbe, van Elsas, Elff, and Van der Brug, 2012), but also

to voter emancipation. Whereas voter behavior in earlier days could be predicted based

on mechanisms of socialization (Campbell et al., 1960; Lipset and Rokkan, 1967), party

identification (Crewe, 1976) and immunization (Butler and Stokes, 1974), the decline of

cleavage politics (Franklin, 1992) has lead to a decline in voter loyalty. These structural

changes imply that vote choices are, at least to a certain extent, subject to short-term

factors (Dalton, 1984, 2013, Dalton et al., 2000), such as issues or candidates (cf.

Michigan model). This voter emancipation implies a more informed electoral decision,

which might have volatile side-effects, but is not bad per se (also see van der Meer, et. al.,

2013).

5

The Dutch case

Within the wider European trend of electoral volatility the Dutch situation is exceptional

and therefore an interesting case to study. The Netherlands is ‘extreme’ with regard to

the increase in electoral volatility since the 1960s. Moreover, it has experienced some of

the most volatile elections within the European context in this period, of which three

took place in the last decade (Mair, 2008). As in other countries Dutch volatility can be

related to the erosion of cleavages, and more specifically, the erosion of pillarization

(Andeweg & Irwin, 2002). This trend combined with the relative openness of the Dutch

electoral system can account for a part of the exceptional character of the Dutch case,

according to Mair (2008).

When Dutch voters change their party preference, or vote choice, they do so in an

ideologically consistent way (Van der Meer et al., 2012). This is partially explained by the

overwhelming amount of, ideologically very close, parties that compete in the Dutch

elections, and by the fact that Dutch voters appear to identify with ideologies, which

allows voters to identify with multiple parties (Van der Eijk & Niemöller, 1983). In line

with this, Mair (2008: p.249) notices Dutch voters seem to be easily swayed and “appear

susceptible to even the most minimal of currents”. This is illustrated by the sudden rise

of the PvdA in the campaign of 2003 (and we witnessed a very similar event in 2012):

“even something with so little weight as a short and quite bland television debate early in

the campaign has the capacity to turn around a very large percentage of voters and to

transform the PvdA from being one of the biggest losers in contemporary Dutch politics

into one of the biggest winners” (Mair, 2008: p.249).

Media and Volatility

The individual level antecedents of volatility have generally not included the media.

Below we explicate in more detail how we conceive media influences, but in general

terms we first turn to Converse. In 1966, Converse – based on the US de facto two party

system – proposed a U-shaped relationship between mass media exposure and voter

volatility: “Those most influenced by the media are either highly stable or highly volatile

voters. Highly stable voters, who decide how to vote well before the final weeks of an

election campaign, are seen to pay close attention to the media's coverage of the

campaign because of their interest in politics. In contrast, the highly volatile group uses

the media as a source of new information to help their voting choice. Thus [the election]

6

campaign has a reinforcing rather than a persuading role for the stable voter but a

persuading or at least guiding role for the volatile voter.” (Forrest & Marks, 1999, p. 100)

Despite these general observations almost 50 years ago, there are only a few studies that

have incorporated the media into their models predicting electoral volatility (Baker,

Ames & Renno, 2006; Dassonneville, 2012; Forrest & Marks, 1999; Schmitt-Beck &

Partheymüller, 2012; Van der Meer et al., 2013), and in many of those cases it constituted

only a (small) part of the explanation. Moreover, results are scattered and inconclusive.

For instance, Van der Meer et al. (2013) look at the factors explaining several types of

volatility and find that among other factors readers of certain newspapers are more likely

to change vote preferences. Which newspaper readership enhances or obstructs volatility

depends on the type of volatility that is studied. On the other hand, Dassonneville (2012)

found no effects of exposure to newspapers, TV news and radio in her models

explaining campaign and inter-election volatility in Belgium. Similarly, Schmitt-Beck &

Partheymüller (2012) conclude that the communication hypothesis (operationalized as

exposure to newspapers, public TV news and media polls) does not gain clear support in

their analysis of time of voting decisions in the German federal elections of 2005 and

2009. Yet, when taking previous preference into account, significant effects are found. In

their study of the 2002 Brazil elections Baker et al. (2006) found significant effects of

exposure to certain biased media outlets on vote switching, dependent upon candidate

preference before the election campaign. Similarly, Forrest and Marks (1999) found

significant, yet small, effects of exposure to specific news content on (considered) vote

switching to or from specific parties.

Framing effects

Even though these studies have contributed to our knowledge on the effect of media use

on electoral volatility, the effects of media content are understudied. In this study we focus

on how media content is framed. There is not a single definition of framing which is

agreed upon and used by most scholars. Despite the absence of a single definition, we

conceptually can define news frames as ‘a central organizing idea or story line that

provides meaning to an unfolding strip of events, weaving a connection among them.

The frame suggests what the controversy is about, the essence of the issue’ (Gamson and

Modigliani, 1989: 143). In short, a news frame can affect an individual by stressing

certain aspects of reality and pushing others into the background – it has a selective

7

function. In this way, certain issue attributes, judgments and decisions are suggested (de

Vreese, 2005).

Framing research has established effects on a variety of dependent variables such

as issue interpretations, cognitive complexity, public opinion and issue support, and voter

mobilization (see De Vreese & Lecheler, 2012 for an overview). However, the research

on the effects of news framing on actual electoral behavior is limited (e.g., Valentino et

al., 2001; Elenbaas & de Vreese, 2008). In this paper we focus specifically on the

presence and effects of two frames.

Building on Adriaansen et al. (2012), Kleinniijenhuis and de Ridder (1998) and

Takens (2013) we first of all look at issue framingi, since this stimulates issue voting (De

Vries, van der Brug, van Egmond & van der Eijk, 2011). Theories regarding prospective

and retrospective voting underline the importance of issue news in establishing vote

decisions (Takens, 2013). Whereas in the first case voters base their vote choice on

agreement with parties’ statements on future policies (Lockerbie, 1992; Nadeau and

Lewis-Beck, 2001), in the latter case it is assumed to be based on evaluations of past

societal developments (Hetherington, 1996; Söderlund, 2008).

As for the effect on volatility, results are inconclusive: Adriaansen et. al (2012)

argue and find that issue framing – as opposed to strategic framing – can possibly induce

voter uncertainty (a proxy for electoral volatility). This was particularly true for high

knowledgeable individuals. On the other hand Takens (2013) finds a negative effect from

two issue frames on volatility. Accordingly, we pose the first research question:

RQ1: What is the effect of exposure to issue framing on campaign volatility?

Moreover, research has shown that attention to issue framing is declining at the cost of

increasing attention to strategic news framing (e.g., Patterson, 1993;), in which stories

about politics are not written in a descriptive style, but rather are interpreted by the

journalist for their contribution towards the political prospects of the politicians

involved. In this study we follow Cappella and Jamieson (1997) by defining strategic

framing as including the coverage of gains and losses (often based on poll results), the

power struggle between political actors, their performance and the public perception of

their performance. Previous research has shown that strategic framing induces political

cynicism (e.g., Adriaansen et. al, 2012; Cappella and Jamieson, 1997; de Vreese and

Elenbaas, 2008; Jackson, 2011), because voters consuming strategic news think in

8

strategic terms (Rhee, 1997). Consequently, one can also expect effects of strategic news

on vote switching. Because voters are provided with strategic information on the

feasibility of coalitions, they might adapt their vote choice (Bargsted and Kedar, 2009).

Moreover, research has shown that cynicism is an important predictor of individual vote

switching (Dalton and Weldon, 2005; Dassonneville, 2011; Zelle, 1995) which might also

indicate an indirect effect of strategic framing on volatility through cynicism. Both

Adriaansen et. al (2012) and Takens (2013) look at the effects of strategic and contest

coverage on volatility, and again come to diverging conclusions. Whereas Adriaansen et.

al (2012) find no effect, Takens does find a positive effect. As a result, we propose a

second research question:

RQ2: What is the effect of exposure to strategic framing on campaign volatility?

A conditioned relationship?

Framing effects do not appear to be equally strong for all individuals at all times in

relation to all issues. Research has focused on features that have the potential to enhance,

limit or even obliterate framing effects. Thus far, the literature presents a number of

individual-level moderator variables (such as knowledge) as well as contextual moderators,

attempting to bring the study of framing effect closer to ‘real life’, such as source

characteristics and interpersonal communication.

In this paper we consider three individual-level moderators. We first of all focus

on sophistication (Valentino et. al, 2001), also sometimes labelled as political knowledge,

or political awareness (Zaller, 1992), and political interest. The evidence with regard to

these moderator is divided: one group of scholars finds less sophisticated and less

interested individuals to be more susceptible to framing effects (Valentino et. al, 2001; de

Vreese, 2005b), whereas a second group argues the opposite (Jackson, 2011; Rhee, 1997;

de Vreese en Elenbaas, 2008). The first line of argument entails that it are the less

sophisticated and interested that are most affected, because they are less resistant to the

journalists’ interpretation of the news (Valentino et. al, 2001). On the other hand it could

be argued that it are the most sophisticated and interested that are more affected,

because sophistication “increases the likelihood that the considerations emphasized in a

frame will be available or comprehensible to the individual” (Chong and Druckman,

2007, p. 112). Because scientific results thus far are conflicting, we pose a third research

question:

9

RQ3: How do sophistication political interest moderate the effects of strategic and

issue frames on campaign volatility?

Moreover, on a different note, we build on the work of Converse (1966) and Zaller

(1992), and investigate whether doubters, i.e., the people that report being most

uncertain about their vote, are more affected by media frames than others.

Hypothesis 1: The effects of the issue and the strategic frame is moderated by the

degree of voter doubt, such that people that report being more uncertain are more

affected by these frames.

Method

Following Crewe (1985), we use three waves of a four-wave panel data set to assess

individual-level vote switching. We link these panel data to a substantive content analysis

of campaign news on television and in newspapers during the Dutch 2010 elections.

Panel Data

The survey data set we used was collected by TNS NIPO in collaboration with

the University of Amsterdam and de Volkskrant. These data were gathered in the period

of the 2010 Dutch parliamentary elections of June 9. The first respondents were

approached in February 2010 (t-3: n = 1,557), and recontacted in April (t-2: n = 1,242;

recontact rate: 80%), May (t-1: n = 985; recontact rate: 79%) and June (t: n = 902;

recontact rate: 92%). In this paper we only use the last three waves (t-2, t-1, and t). The

data were gathered using computerassisted self-interviewing. Our data are by and large

representative of the Dutch population.

Our dependent variable is based on three variables. At t-2 and t-1 respondents were

asked which party they would vote for if elections were held today, and at t respondents

were asked which party they ended up voting for in the elections. We constructed a

dummy variable Volatility by assigning each respondent a ‘0’ by default, and a ‘1’ if they

either changed their vote preference between t-2 and t-1 or between t-1 and t. Because

we interpret campaign volatility as a crystallization of vote choice, we treat a switch to or

from ‘don’t know’, ‘blank’, and ‘abstain’ as a vote switch. Only ‘refuse’, ‘no right to vote’,

and ‘other, namely…’ were treated as missing.

10

To connect media content to the individual-level data we use media exposure

variables to the various media outlets included in the analysis (see below), measured on a

5-point scale ranging from never (0) to (almost) daily (4).

In addition, we used several control variables. First of all, we control for the usual

socio demographic variables sex (1 = male, 2 = female, M = 1.42, SD = 0.49), age (M =

50, SD = 16.63)ii, social class (ranging from 1 to 5, M = 2.59, SD = 1.19) and income

(measured in 27 income groups, average income falling in group 36,000 to 47,600).

Ideology is measured with a variable tapping left-right self-placement (with 1 = left and

10 = right, M = 5.66, SD = 2.17). Recent research has found that it are the people in the

middle of the political spectrum that make the most vote switches (Van der Meer et. al,

2013), which is why we include the variable Ideological Extremity by recoding Ideology 1

through 5, where ‘1’ denotes being in the middle of the political spectrum, and ‘5’ being

at either extreme end. Additionally, it are generally the people who are less trustworthy,

efficacious, i.e., the more politically cynical who are more liable to switch vote

preferences (Dassonneville, 2012), prompting us to include Cynicism (M = 2.66, SD =

0.55), which is the average score of 8 items (Cronbach’s alpha = 0.844) measured on a 4-

point scale ranging from ‘completely disagree’ to ‘completely agree’. Items are, for

instance, ‘Politicians promise more than they can achieve’ and ‘Political parties are only

interested in my vote, not in my opinion’. We use education (measured in 7 categories,

with average education falling in the fourth category, equalling college level) as a proxy

for sophistication, following Valentino et. al (2001). Political interest was measured with

an item that asked respondents how interested they are in politics on a 7-item scale,

ranging from ‘1’, not at all interested, to ‘7’, very interested (M = 4.06, SD = 1.80). The

number of don’t knows is computed by counting the number of times respondents

stated they did not know which party to vote for, ranging from 0 to 3 (M = 0.24, SD =

0.61).

Content Analysis

To incorporate media content into the analysis, we conducted a content analysis of the

last three weeks of the election campaign (May 16 to June 9, 2010). All television

programs with political content were coded in collaboration with the Dutch public

broadcasting agency (NPO), by a team of four coders. In this study we only include

those programs for which media exposure was tapped in the panel data set (n=131).

Those are the news programs of the public broadcaster NOS Journaal, and two

11

commercial stations RTL Nieuws and Hart van Nederland, the current affairs programs

Eén Vandaag, Netwerk and NOVA/Den Haag Vandaag, the talkshow Knevel and van

den Brink (all public broadcasts), and the infotainment programs De Wereld Draait Door

and RTL Boulevard (only the latter is a commercial broadcast). In each program items

were coded that satisfied the conditions of campaign news, in the sense that the story

was about the elections, party leaders, or about the government. Items were identified

based on content and form.

Additionally, three (different) coders coded articles in six different newspapers

(n=705). We coded a random sample of 705 articles, which is 53% of all articles, taking

proportions between newspapers into account. We included two broadsheet/elite

newspapers: de Volkskrant and NRC Handelsblad, two semi-tabloid newspapers: de

Telegraaf and Algemeen Dagblad, and two (popular) free dailes: Sp!ts and Metro. Again

articles were coded if they satisfied the conditions of campaign news, as explained above.

Measures. In each item or article indicators of the issue frame and the strategy

frame were coded. The issue frame was coded with the following dummy variables: “Is

the story mainly about substantial policy issues, - problems and –solutions?”, “Does the

story describe the position or standpoints of the actor on substantial policy issues?”

“Does the story describe the consequences or effects of (proposed) legislation for the

public?”. The strategic was tapped with: “Does the story describe strategies of politicians

or parties to win the elections or win debates?” and “Does the story describe a future

coalition or government formation?iii”. In both cases ‘1’ indicated presence of the frame.

Intercoder reliability checks indicated sufficient agreement between coders on

these items: Krippendorff’s Alpha was generally above 0.6.iv

To see whether the different items are connected, we investigated the extent to

which they constitute scales. Because the items are dichotomous, we used Mokken-

scaling, a probabilistic version of the better-known Guttmann scale (Mokken, 1971). The

Mokken scale analysis shows that the three items measuring the issue frame together

form a strong scale (H=0.826, p=0.000), and the two items tapping the strategy frame

form a medium scale (H=0.386, p=0.000). We used an average score to tap the presence

of each frame in each article (Issue frame: M = 0.422, SD = 0.400; Strategic frame: M =

0.215, SD = 0.332).

Linking survey data to content data. For each respondent, media exposure was

weighted on the basis of the media coverage variables, computing individual exposure to

the frames, where Framemedium represents the average presence of each frame per medium.

12

which represents the individual respondents’ exposure to each frame. These

weighted media exposure variables are thus contingent on the media outlets each

respondent uses, as well as on the average attention to both frames in each outlet.

Because some respondents are exposed to more political news than others, we also

computed two variables tapping the exposure to political news, in which

political_newsmedium represents the sum of all political items or articles per medium:

Due to large differences in numbers between newspapers and television

computed two variables: one for the exposure to political news in newspapers, and one

for the exposure to political news on television.

Results

Before we look at the effects of media content on electoral volatility, table 1 shows the

extent to which the respondents in our panel data set switched between waves. Results

show that each party loses a certain amount of voters, and also gains some between the

three waves, which exemplifies the non-stable electoral base of most parties. Moreover,

most of the voters stating they do not know which party to vote for at t-2 or t-1, do end

up voting for one of the parties in the elections of 2010. Of the respondents included in

the last three waves, 34.77% changed their vote preference at least once in the last month

before the electionv. Even though the amount of switching depends on how one defines

a switcher, these results indeed show a significant amount of individual-level campaign

volatility.

<<Table 1 about here>>

Table 2 shows the attention to campaign news as well as the presence of the issue

frame and the strategic frame during the 2010 election campaign. The issue frame is in

general more present in the news than the strategic frame. Results also show differences

between media, and between genres: the TV news, current affairs programs and the

political talk show feature more issue framing than the two infotainment programs

incorporated in this study. But there are also differences within genres: NOVA pays

much more attention to the strategic frame than the other two current affairs programs.

!

Framei = Framemedium " Media_exposuremediumi

!

Political_ newsi = Political_ newsmedium " Media_exposuremediumi

13

Differences between newspapers are small, although the free dailies do pay less attention

to campaign news.

<<Table 2 about here>>

Effects of both frames on vote switching are tested in table 3. Like Van der Meer et. al

(2013) we find that it are the people in the middle of the political spectrum that change

their vote preference the most, and the more cynical, like Dassonneville (2012). We find

no direct effects of media content on vote switching in model I. In other words: the

consumption of more or less political news, or the exposure to the issue frame or the

strategic frame in itself does not lead voters to change their preferred party across the

board. In model II to V, however, we investigate whether the framing effects differ for

different respondents.

<< Table 3 about here>>

Model II shows that both effects are moderated by sophistication, a finding that is

exemplified in figure 1 and 2. Figure 1 shows that issue framing only leads to an increase

in vote switching for the extremely highly sophisticated, whereas figure 2 shows that the

opposite is the case for the strategic frame. This frame dampens vote change for all cases

in the model, and this effect gets stronger the more sophisticated people are.

<< Figure 1 about here>>


In model III the moderation by political interest is tested, and figure 3 and 4

show that results are similar to the moderation of education: issue framing has a positive

effect, and strategic framing has a negative effect on vote switching. Both effects increase

when political interest increases, and both effects are insignificant for the people who are

not interested in politics. Reviewing model II and III we can answer our RQ3 by stating

that political sophistication and political interest boost the effects of issue framing as well

as strategic framing on vote switching.

14



Model IV shows the impact of a strong moderator: the log likelihood of this

model shows that this is the moderator that explains most variance in the dependent

variables. Results are in the opposite direction compared to the interaction effects with

sophistication and political interest: the more respondents report they do not know

which party to vote for in the months preceding the general elections, the more they are

affected by the strategic frame, and the less they are affected by the issue frame. Again,

both effects increase when voter doubt increases, which lends support to our hypothesis

1. These findings are shown in Figure 5 and 6.



Model V, finally, tests the three-way interaction between voter doubt,

sophistication, and the two frames. Results show this model explains most variance in

our dependent variable. To interpret these coefficients, the interactions are plotted in

graphs 7 and 8. Graph 7 shows that people that are no that sophisticated (i.e., score one

or two standard deviations below average on this variable) are not affected by the issue

frame. However, people that are somewhat (i.e., score average on the sophistication

scale) to highly sophisticated (one to two standard deviations above average), are

significantly negatively impacted by this frame. This impact increases with more

sophistication, and with more reported don’t knows.


A similar situation occurs in the moderation of the strategic frame (figure 8).

Again the impact of the frame increases with sophistication and number of don’t knows

so that the sophisticated ‘doubters’ (the respondents reporting more don’t knows) are the

most affected by the frame, but in this case the impact is positive. Moreover, now the

unsophisticated are affected: the strategic frame has a positive impact on their volatility,

but only if they do not report any don’t knows.

15


In light of these results, we can turn to our research questions 1 and 2. As for the

effect of issue framing on campaign volatility (RQ1), our results show there is no across-

the-board effect. The impact differs for different respondents. Whereas issue framing

leads to more vote switching for people with higher political interest, the opposite is the

case for the voters that report more don’t knows. They switch significantly less when

exposed to issue framing. Combining these moderators shows again a negative impact of

issue framing on campaign volatility, increasing with voter doubt and sophistication.

For RQ2 we similarly do not have a clear-cut answer. Whereas strategic framing

dampens vote switching when moderated by sophistication and interest, it boosts

volatility for people that report more don’t knows. And the combination of the

moderators again shows a positive impact of strategic framing on volatility, for some

voters.

Conclusion

Volatility is on the rise in Europe, in its most extreme form in the Netherlands (Mair,

2008). An increasing amount of scholars devote their attention to explanations of

volatility, in recent years also paying attention to individual level explanations. However,

none of this research has focused specifically on one of the most important information

sources about politics. In this study we therefore incorporate the individual-level impact

of media content, in a comprehensive study combining panel data and quantitative

content analysis.

As stated above we do not find sweeping across the board effects across all types

of voters (see also De Vreese & Lecheler, 2012). On the one hand, those with higher

levels of political interest switch more when exposed to issue framing. We find that to be

a very important finding. First of all, we are not finding that electoral volatility is a

phenomenon reserved for the political novices and those without interest in politics.

Second, switching also occurs in response to issue coverage which is the most

information rich type of news. It thus seems that switching follows from interest and

exposure to actual political information. In that sense, we argue that the role played by

the media in relation to vote switching appears to be a rather positive one, since electoral

preferences are updated and shaped by substantive coverage. From a normative

standpoint, this is one of the most important functions of an election campaign. On the

16

other hand, those with higher levels of uncertainty switch less when exposed to issue

framing. In particular those with high levels of sophistication and uncertainty, are less

prone to switching in response to exposure to issue framing. This indicates that the small

share of sophisticated voters that is uncertain in fact stabilizes their vote choice in

response to substantive coverage.

The impact of the strategic frame on volatility, unfortunately, is in line with the

pessimistic views of some. Our analysis shows that even though the sophisticated switch

less after reading or seeing strategic news, which is reassuring, the less sophisticated, and

particularly the large part of them that reports less uncertainty, switches more.

Our study is to our knowledge the first to combine panel survey data and media

content analysis in an individual level study of vote switching. The content analysis

enables us to say more about what kind of media contents in conducive to changes in

preferences and as shown, it seems that substantive news in particular has this potential.

Our study obviously has a number of caveats: we rely on only a sample of the media and

information sources available during an election campaign and we are confined by self-

reported measures of media usage, which are prone to errors (e.g., Prior, 2007).

However, our individual level measures of exposure at the program level has been hailed

as the best way of tapping media exposure (e.g., Dilliplane & Mutz, 2012) and with the

combination with the media content data, we are able to make stronger assessments of

the effects and their underlying causes (e.g. De Vreese & Semetko, 2004).

Future research should further explore our findings. It has been an implied

assumption in much of the research on electoral volatility that this is a signal of

dissatisfaction with electoral and democratic processes and that volatility is particularly

manifest among the uninterested voters. Our study, however, suggests the story is not

that simple. Yes, some vote switching is fuelled by strategic framing. This particularly

concerns a large part of the novices that tends to make more substantive choices. On the

other hand, if interested voters change their vote choice, they do so in response to issue

framed news. Scholars of public opinion formation and electoral behaviour should

welcome and further probe such findings pointing towards a process of informed

change.

17

References

Aarts, K., & Thomassen, J. (2008). Dutch voters and the changing party space 1989–

2006. Acta Politica, 43(2), 203-234.

Adriaansen, M. L., van Praag, P., & de Vreese, C. H. (2012). A mixed report: The effects

of strategic and substantive news content on political cynicism and voting.

Communications, the European Journal of Communication Research, 37(2), 153-172.

Andeweg, R. B., & Irwin, G. A. (2002). Governance and politics of the Netherlands.

Basingstoke, England: Palgrave Macmillan.

Baker, A., Ames, B., & Renno, L. R. (2006). Social context and campaign volatility in new

democracies: Networks and neighborhoods in Brazil's 2002 elections. American

Journal of Political Science, 50(2), 382-399.

Bargsted, M. A., & Kedar, O. (2009). Coalition‐targeted duvergerian voting: How

expectations affect voter choice under proportional representation. American Journal

of Political Science, 53(2), 307-323.

Bartolini, S., & Mair, P. (1990). Policy competition, spatial distance and electoral

instability. West European Politics, 13(4), 1-16.

Bennett, W. L., & Entman, R. M. (Eds.). (2001). Mediated politics: Communication in the future

of democracy. Cambridge University Press.

Butler, D., & Stokes, D. (1974). Political change in Britain: The evolution of electoral choice.

London: Macmillan.

18

Campbell, A., Converse, P. E., Miller, W. E., & Stokes, D. E. (1960). The American voter.

New York: John Wiley & Sons, Inc.

Cappella, J. N., & Jamieson, K. H. (1996). News frames, political cynicism, and media

cynicism. The Annals of the American Academy of Political and Social Science, 71-84.

Chong, D., & Druckman, J. N. (2007). Framing theory. Annual Review of Political Science,

10, 103-126.

Crewe, I. (1976). Party identification theory and political change in Britain. New York: Wiley.

Crewe, I. (1985). Introduction: Electoral change in western democracies: A framework

for analysis. In I. Crewe, & D. Denver (Eds.), Electoral change in western democracies:

Patterns and sources of electoral volatility (pp. 1-22). Kent: Croom Helm Ltd.

Dalton, R. J. (1984). Cognitive mobilization and partisan dealignment in advanced

industrial democracies. The Journal of Politics, 46(1), 264-284.

Dalton, R. J. (2000). The decline of party identification. In R. J. Dalton, & M. P.

Wattenberg (Eds.), Parties without partisans : Political change in advanced industrial

democracies (pp. 19-36). Oxford: Oxford University Press.

Dalton, R.J. and Weldon, S.A. (2005) Public images of political parties: A necessary evil?

West European Politics 28(5): 931–951.

Dassonneville, R. (2011). Electoral volatility, political sophistication, trust and efficacy: A

study on changes in voter preferences during the Belgian regional elections of 2009.

Acta Politica, 47(1), 18-41.

19

De Vreese, C. H. (2005). News framing: Theory and typology. Information design journal+

document design, 13(1), 51-62.

De Vreese, C. H. (2005b). The spiral of cynicism reconsidered. European Journal of

Communication, 20(3), 283-301.

De Vreese, C. H., & Elenbaas, M. (2008). Media in the game of politics: Effects of

strategic metacoverage on political cynicism. The International Journal of Press/Politics,

13(3), 285-309.

De Vreese, C. H., & Lecheler, S. (2012). News framing research: an overview and new

developments. The SAGE Handbook of Political Communication, 292.

De Vreese, C. H., & Semetko, H. A. (2002). Cynical and engaged strategic campaign

coverage, public opinion, and mobilization in a referendum. Communication Research,

29(6), 615-641.

De Vreese, C. H., & Semetko, H. A. (2004). News matters: Influences on the vote in the

Danish 2000 euro referendum campaign. European Journal of Political Research, 43(5),

699-722.

De Vries, C. E., Van der Brug, W., van Egmond, M. H., & Van der Eijk, C. (2011).

Individual and contextual variation in EU issue voting: The role of political

information. Electoral Studies, 30(1), 16-28.

Dilliplane, S., Goldman, S. K., & Mutz, D. C. (2013). Televised exposure to politics: New

measures for a fragmented media environment. American Journal of Political Science,

57(1), 236-248.

20

Elenbaas, M., & De Vreese, C. H. (2008). The effects of strategic news on political

cynicism and vote choice among young voters. Journal of Communication, 58(3), 550-

567.

Franklin, M. N., Mackie, T. T., & H. Valen (1992). Electoral change: Responses to evolving social

and attitudinal structures in western countries. Cambridge: Cambridge University Press.

Forrest, J., & Marks, G. N. (1999). The mass media, election campaigning and voter

response: The Australian experience. Party Politics, 5(1), 99-114.

Gamson, W. A., & Modigliani, A. (1989). Media discourse and public opinion on nuclear

power: A constructionist approach. American journal of sociology, 1-37.

Hetherington, M. J. (1996). The media's role in forming voters' national economic

evaluations in 1992. American Journal of Political Science, , 372-395.

Irwin, G. A., & Van Holsteyn, J. J. M. (2008). What are they waiting for? Strategic

information for late deciding voters. International Journal of Public Opinion Research,

20(4), 483-493.

Jackson, D. (2011). Strategic media, cynical public? Examining the contingent effects of

strategic news frames on political cynicism in the United Kingdom. The International

Journal of Press/Politics, 16(1), 75-101.

Kleinnijenhuis, J., & De Ridder, J. A. (1998). Issue news and electoral volatility. European

Journal of Political Research, 33(3), 413-437.

Kuhn, U. (2009). Stability and change in party preference. Swiss Political Science Review,

15(3), 463-494.

21

Lipset, S. M., & Rokkan, S. (1967). Party systems and voter alignments: Cross-national

perspectives. New York: Free Press.

Lockerbie, B. (1992). Prospective voting in presidential elections, 1956-1988. American

Politics Research, 20(3), 308-325.

Mair, P. (2008). Electoral volatility and the Dutch party system: A comparative

perspective. Acta Politica, 43(2), 235-253.

Nadeau, R., & Lewis‐Beck, M. S. (2001). National economic voting in US presidential

elections. Journal of Politics, 63(1), 159-181.

Patterson, T. E. (1993). Out of order (p. 20). New York: Knopf.

Pedersen, M. N. (1979). The dynamics of European party systems: Changing patterns of

electoral volatility. European Journal of Political Research, , 1-26.

Pennings, P., & Keman, H. (2008). The changing landscape of Dutch politics since the

1970s: A comparative exploration. Acta Politica, 43(2), 154-179.

Rhee, J. W. (1997). Strategy and issue frames in election campaign coverage: A social

cognitive account of framing effects. Journal of Communication, 47(3), 26-48.

Roberts, K. M., & Wibbels, E. (1999). Party systems and electoral volatility in Latin

America: a test of economic, institutional, and structural explanations. American

Political Science Review, 575-590.

Schmitt-Beck, R., & Partheymüller, J. (2012). Why voters decide late: A simultaneous test

of old and new hypotheses at the 2005 and 2009 German federal elections. German

Politics, 21(3), 299-316.

22

Sikk, A. (2005). How unstable? Volatility and the genuinely new parties in Eastern

Europe. European Journal of Political Research, 44(3), 391-412.

Slater, M. D. (2007). Reinforcing spirals: The mutual influence of media selectivity and

media effects and their impact on individual behavior and social identity.

Communication Theory, 17(3), 281-303.

Söderlund, P. (2008). Retrospective voting and electoral volatility: A Nordic perspective.

Scandinavian Political Studies, 31(2), 217-240.

Strömbäck, J. (2011). Mediatization and Perceptions of the Media’s Political Influence.

Journalism Studies, 12(4), 423-439.

Takens, J. (2013). Media logic and electoral democracy. Amsterdam: Vrije Universiteit.

Tavits, M. (2008). On the linkage between electoral volatility and party system instability

in Central and Eastern Europe. European Journal of Political Research, 47(5), 537-555.

Tavits, M. (2005). The Development of Stable Party Support: Electoral Dynamics in

Post‐Communist Europe. American Journal of Political Science, 49(2), 283-298.

Valentino, N. A., Beckmann, M. N., & Buhr, T. A. (2001). A spiral of cynicism for some:

The contingent effects of campaign news frames on participation and confidence in

government. Political Communication, 18(4), 347-367.

Van der Eijk, C., & Niemoller, B. (1983). Electoral change in the netherlands: Empirical results

and methods of measurement. Amsterdam: CT Press.

Van der Meer, T., Lubbe, R., van Elsas, E., Elff, M., & van der Brug, W. (2012).

Bounded volatility in the Dutch electoral battlefield: A panel study on the structure

23

of changing vote intentions in the Netherlands during 2006–2010. Acta Politica,

47(2), 333-355.

Van der Meer, T. W., van Elsas, E., Lubbe, R., & van der Brug, W. (2013). Are volatile

voters erratic, whimsical or seriously picky? A panel study of 58 waves into the

nature of electoral volatility (The Netherlands 2006–2010). Party Politics.

Zaller, J. (Ed.). (1992). The nature and origins of mass opinion. Cambridge: Cambridge

University Press.

Zelle, C. (1995) Social dealignment versus political frustration: Contrasting explanations

of the floating vote in Germany. European Journal of Political Research 27(3): 319–

345. |

24

Tables

Table 1. Gain and loss of voters between waves

t-2 to t-1 t-1 to t t-2 tot t

lost gained lost gained lost gained

CDA -23% 20% -21% 26% -32% 35%

PvdA -31% 25% -26% 39% -29% 40%

SP -26% 53% -38% 49% -45% 74%

VVD -13% 43% -29% 16% -24% 42%

PVV -35% 23% -17% 34% -32% 32%

GroenLinks -31% 46% -42% 35% -48% 54%

ChristenUnie -32% 18% -9% 19% -23% 27%

D66 -49% 28% -51% 32% -60% 20%

PvdD -64% 43% -29% 38% -64% 50%

SGP 0% 44% -27% 0% 0% 0%

TON -64% 44% -50% 43% -73% 57%

Abstain -38% 32% -24% 43% -31% 59%

Blank -71% 56% -78% 33% -86% 33%

Refusal -75% 80% -75% 88% -75% 88%

DK -59% 40% -100% 0% -100% 0%

Total -39% 40% -37% 35% -44% 44%

25

Table 2. Overview of Issue Frame and Strategic Frame per medium

Issue Frame Strategic Frame Average media consumption

(n=1577) N M SD M SD

TV News

NOS Journaal 21 0.651 0.441 0.136 0.228 2.99

RTL Nieuws 25 0.400 0.430 0.135 0.302 2.38

Hart van Nederland 2 0.500 0.707 0.000 0.000 1.70

Current Affairs Programs

EenVandaag 20 0.550 0.394 0.100 0.205 1.13

Netwerk 9 0.370 0.484 0.167 0.354 1.18

NOVA 23 0.478 0.480 0.413 0.389 1.23

Infotainment programs

DWDD 5 0.267 0.435 0.200 0.447 1.47

RTL Boulevard 4 0.083 0.167 0.000 0.000 1.30

Talkshow

Knevel & van den Brink 19 0.579 0.456 0.237 0.348 1.38

Broadsheet newspapers

de Volkskrant 182 0.412 0.388 0.217 0.358 0.32

NRC Handelsblad 127 0.472 0.393 0.276 0.355 0.27

Tabloid Newspapers

de Telegraaf 128 0.432 0.391 0.203 0.329 0.99

Algemeen Dagblad 143 0.312 0.357 0.178 0.299 0.64

Free Dailies

Metro 64 0.417 0.412 0.258 0.309 0.84

Sp!ts 61 0.443 0.416 0.197 0.32 0.92

Note. Average media consumption shows the unweighted average value of the media

consumption variables in the panel data set, n=1577, scale runs from 0 to 4 where 0

denotes “never” and 4 means “(almost) daily”.

26

Table 3. Logistic regression analysis predicting volatility with media content

Model 1 Model 2 Model 3 Model 4 Model 5

No of DK's 1.079 (0.170)*** 1.092 (0.170)*** 1.123 (0.171)*** 1.316 (0.413)*** 1.304 (0.424) **

Sex 0.221 (0.166) 0.170 (0.168) 0.194 (0.168) 0.211 (0.168) 0.170 (0.170)

Age -0.007 (0.006) -0.007 (0.006) -0.007 (0.006) -0.008 (0.006) -0.009 (0.006)

Social class -0.083 (0.096) -0.086 (0.096) -0.090 (0.096) -0.080 (0.097) -0.079 (0.097)

Income -0.022 (0.026) -0.027 (0.027) -0.027 (0.027) -0.018 (0.027) -0.022 (0.027)

Ideology -0.034 (0.040) -0.038 (0.040) -0.039 (0.040) -0.042 (0.040) -0.043 (0.041)

Sophistication -0.010 (0.066) -0.055 (0.129) -0.004 (0.066) -0.013 (0.067) 0.076 (0.140)

Pol interest 0.012 (0.056) 0.017 (0.056) -0.102 (0.127) -0.001 (0.056) -0.001 (0.057)

Id. extremity -0.189 (0.070)** -0.191 (0.070)** -0.196 (0.071)** -0.180 (0.070)* -0.176 (0.076)*

Pol cynicism 0.489 (0.151)*** 0.486 (0.152)*** 0.487 (0.152)*** 0.518 (0.153)*** 0.497 (0.154)***

Pol news papers 0.000 (0.000) 0.000 (0.000) 0.000 (0.000) 0.000 (0.000) 0.000 (0.000)

Pol news tv -0.001 (0.003) -0.001 (0.003) -0.001 (0.003) -0.001 (0.003) 0.000 (0.003)

Strategic frame 0.088 (0.222) 0.079 (0.223) 0.112 (0.223) -0.124 (0.232) -0.149 (0.234)

Issue frame 0.040 (0.080) 0.041 (0.080) 0.012 (0.081) 0.099 (0.083) 0.097 (0.084)

Soph * strat frame -0.251 (0.097)** -0.318 (0.105)**

Soph* issue frame 0.099 (0.041)* 0.125 (0.045)**

Pol int * strat frame -0.259 (0.093)**

Pol int * issue frame

0.108 (0.040)**

No of DK * strat frame

1.202 (0.337)*** 1.269 (0.376)***

No of DK * issue frame

-0.436 (0.143)** -0.473 (0.161)**

No of DK * Soph 0.189 (0.274)

No of DK * Soph * strat frame

0.491 (0.225)*

No of DK * Soph * issue frame

-0.194 (0.095)*

Intercept -0.400 (0.851) -0.189 (0.727) -0.240 (0.860) -0.427 (0.736) 0.107 (1.004)

Log Likelihood -953.828 -946.759 -945.661 -940.044 -927.985

Nagelkerke R2 0.151 0.162 0.164 0.172 0.189

Note. Entries are unstandardized regression coefficients; Standard errors in brackets. N=811. * p < 0.05, ** p <0.01,

*** p < 0.001.

27

Figures Figure 1. Plot of the moderation of the issue frame by sophistication

Figure 2. Plot of the moderation of the strategic frame by sophistication

Figure 3. Plot of the moderation of the issue frame by political interest

1.5

0.5

Mar

gina

l Effe

ct o

f Iss

ue F

ram

e

1 2 3 4 5 6 7

Sophistication

Marginal effect of Issue Frame95% Confidence Interval

54

32

10

1

Mar

gina

l Effe

ct o

f Stra

tegi

c Fr

ame

1 2 3 4 5 6 7

Sophistication

Marginal effect of Strategic Frame95% Confidence Interval

28

Figure 4. Plot of the moderation of the strategic frame by political interest

.50

.51

1.5

Mar

gina

l Effe

ct o

f Iss

ue F

ram

e

1 2 3 4 5 6 7

Political Interest


32

10

1

Mar

gina

l Effe

ct o

f Stra

tegi

c Fr

ame

1 2 3 4 5 6 7

Political Interest


29

Figure 5. Plot of the moderation of the issue frame by the number of don’t knows

Figure 6. Plot of the moderation of the strategic frame by the number of don’t knows

21

01

Mar

gina

l Effe

ct o

f Iss

ue F

ram

e

0 1 2 3

Number of don’t knows


10

12

34

56

Mar

gina

l Effe

ct o

f Stra

tegi

c Fr

ame

0 1 2 3



30

Figure 7. Plot of the moderation of the issue frame by number of don’t knows and sophistication

Figure 8. Plot of the moderation of the strategic frame by number of don’t knows and sophistication

* * * * * * * * * * * * * * * * * * * * * * * * * * *

**

**

**

**

**

**

**

**

**

**

**

**

**

**

**

**

**

**

**

**

**

**

Very High Sophistication

Neutral

Very Low Sophistication

* indicates significance at the 95% level3.5

32.

52

1.5

1.5

0.5

Mar

gina

l Effe

ct o

f Iss

ue F

ram

e

0 1 2 3


* ** * * * *

* * * * * * * * * * * * * * * * * * * * * * * * * * * * *

**

**

**

**

**

**

**

**

**

**

**

**

**

**

**

**

**

**

**

**

**

**

**

**

*Very High Sophistication

Neutral

Very Low Sophistication

* indicates significance at the 95% level10

12

34

56

78

9

Mar

gina

l Effe

ct o

f Stra

tegi

c Fr

ame

0 1 2 3


31

i There is discussion in the literature whether issue coverage is a frame, or the absence of the strategy frame. ii Following Van der Meer et. al (2013) we tested differential effects of different age groups, but found no differences between age groups. Similarly, we also tested effects of education squared, but found no significant effects. iii We also included items tapping coverage of poll (results), as part of the strategy frame. Due to low intercoder reliability we had to drop these from the scales. However, results are very similar if we include them. iv Only in one case Krippendorff’s alpha was 0.54. v This includes voters switching to and from ‘don’t know’, voting blank and abstaining from voting.

Documents

University of Amsterdam PRELIMINARY VERSION DO NOT CITE ... · Paper to be presented at the 7th ECPR General Conference, Bordeaux, 5-7 September 2013. 2 Abstract In recent decades,