Red Economy,Blue Economy: How Media-Party Parallelism Affects the Partisan Economic Perception Gap

Abstract

Partisan identities do not only shape people’s political attitudes, but also their perceptions of real-world developments. This is evident from the partisan economic perception gap: Government supporters have more positive economic perceptions than opposition supporters, especially when the economic situation is ambiguous. Recent research has shown that the size of this partisan gap varies across different contexts and that the state of the economy and working of political institutions are important moderators. Still, little is known about the influence of another important contextual variable: the degree of partisanship in the media system. Based on a theoretical discussion of partisan-motivated rationalization and the information environment, the paper tests the hypothesis that, due to selective exposure and exposure to more partisan content, people in partisan media systems have more polarized economic perceptions. A multilevel analysis of representative surveys in twenty-six European countries in 2014 shows that the partisan perception gap is, indeed, larger in countries with more polarized media systems, after controlling for other relevant country characteristics. People with the highest level of media consumption are most affected by media-party parallelism. The findings are relevant for worldwide discussions about posttruth politics, as they show that the media environment influences gaps in people’s perceptions of real-world developments.

Keywords

media systems economic news economic perceptions partisan motivated reasoning media comparative research

Economic perceptions have important electoral consequences. The perception that the economy is in decline can have more impact on the outcome of elections than the actual state of the economy (Hetherington 1996). Thus, it is of democratic importance that people are well informed about the economic situation (Duch and Stevenson 2008; Strömbäck 2005). Yet, extant research has shown that economic perceptions vary widely across partisan lines (e.g., Bartels 2002; Conover et al. 1986; De Vries et al. 2018; Evans and Andersen 2006). Partisanship serves as a “perceptual screen” (Campbell et al. 1960: 133), which creates the economic perception gap: People who voted for a governing party are generally more positive about the state of the economy compared with supporters of opposition parties. The reason for this is that perceived positive economic developments put the government in a positive light (e.g., Bisgaard 2015). Hence, partisanship is a “pervasive dynamic force shaping citizens’ perceptions of, and reactions to, the political world” (Bartels 2002: 138).

The size of the economic perception gap is not only dependent on people’s individual characteristics but also shaped by national contexts, such as the economic climate (Bisgaard 2015; Bisgaard et al. 2016; Stanig 2013) and institutional settings of the political system (Hobolt et al. 2013; Parker-Stephen 2013). This paper adds to this growing literature by investigating how the media system of a country, and specifically the degree of media-party parallelism (Hallin and Mancini 2004), affects divergence in economic perceptions between government supporters and opposition supporters. This paper argues that the economic perception gap should be larger in more partisan media systems due to selective exposure, the presence of partisan cues in partisan content, and because such content makes partisan identities more salient (see also Lelkes 2016). This expectation is tested in a multilevel analysis based on representative surveys in twenty-six European countries in 2014 (Schmitt et al. 2015a; 2015b). The results show that the partisan perception gap is, indeed, larger in countries with more partisan media systems, after controlling for the state of the economy, the institutional setting, and political polarization among the population. Additional evidence is given that effects are strongest for people with high media consumption. With these results, the paper answers calls put forth by Jerit and Barabas (2012) and Bisgaard (2019: 467) for more attention on how the media and the information environment affect partisan perception biases.

Partisan Economic Perceptions

When assessing ambiguous situations, people will interpret the situation in a way that is congruent with their partisan identity. People who feel connected to a political party interpret the world around them in a way that puts the party they identify with in a positive light (e.g., Gaines et al. 2007; Schaffner and Roche 2016). Such partisan-motivated reasoning is an important explanation of the economic perception gap where government supporters are more positive about the state of the economy than opposition supporters (e.g., Bisgaard 2015).¹ Similarly, partisans hold different views on whether the crime rate went up or down, the country is more or less secure from foreign enemies, or whether the moral climate improved or deteriorated over time (e.g., Bartels 2002: 152; Jerit and Barabas 2012; Ladd 2011).

To protect their partisan identity, partisans process new information selectively (Bolsen et al. 2014). Information that is positive toward the party they support is more likely to be uncritically accepted by partisans, while information that puts their party in a negative light will be rejected or counterargued (Schaffner and Roche 2016). Not only do partisans process information selectively, they also expose themselves selectively to information. Information seeking is characterized by a confirmation bias, as partisans prefer to expose themselves to arguments that confirm their preexisting attitudes (Taber and Lodge 2006). This is done to avoid cognitive dissonance, a mental discomfort that people feel when they simultaneously hold opposing views or beliefs (Frey 1982). While there is extensive evidence that partisans actively seek confirmatory information, studies generally find that avoidance of counterattitudinal information is weaker (e.g., Frey 1982; Holbert et al. 2010). Even though people are not completely shielded from counterattitudinal information, selective exposure is related to partisan polarization (Stroud 2010; Taber and Lodge 2006; Tsfati et al. 2014). Because people more easily accept congenial information, exposure to pro-attitudinal news reinforces prior beliefs. An experimental study by Taber and Lodge (2006) shows that selective exposure, in turn, leads to further polarization of attitudes. In a longitudinal study, Stroud (2010) showed that partisan selective exposure is related to polarized attitudes among the American population. Similarly, it can be expected that economic perceptions polarize due to selective exposure. There is broad evidence that economic coverage affects economic perceptions not only on the aggregate level (e.g., Damstra and Boukes 2018; Jonkman et al. 2020; Soroka et al. 2015), but also on the individual level (Boomgaarden et al. 2011; Van Dalen et al. 2018). Thus, if partisans are selectively exposed to economic news that confirms their world view, this should reinforce the economic perception gap.

Despite selective processing and selective exposure, there are boundaries to the partisan-motivated economic perception gap. First, the size of this gap is conditioned by the state of the economy. In the case of unambiguous events of national importance, partisans converge in their perceptions of key facts (Gaines et al. 2007). Bisgaard (2015) has shown that when the economy is clearly and unambiguously in crisis, even strong government supporters acknowledge that the economy is not doing well. In these cases, partisans can reconcile these facts with their partisan identity “by attributing responsibility in a highly selective fashion, crediting their own party for success and blaming other actors for failure” (Bisgaard 2019: 2). Partisan identity triggers group-serving biases, which affect whether people hold the sitting government accountable (Tilley and Hobolt 2011). A group-serving bias “refers to the tendency of in-group members to attribute positive actions committed by their own group to positive in-group qualities and negative actions by the favored group to external causes” (Hobolt and Tilley 2014: 20). Government supporters attribute responsibility for the economy to factors outside the government during economic crises (Bisgaard 2019). For opposition supporters, the pattern is the opposite. They are more likely to blame the government when the economy goes down (Bisgaard 2015). This explains why economic perceptions converge during economic crisis, while partisans can align this with their partisan identity through the way they attribute responsibility.

The clarity of institutional responsibilities in the political system is another contextual variable that affects how government and opposition partisans perceive economic developments. Unclear divisions of power between political institutions “blur lines of responsibility and make it more difficult for voters to assign responsibility and sanction governments on the basis of their performance” (Hobolt et al. 2013: 164–65). When institutional responsibility is unclear, institutional rules are more ambiguous about where the responsibility for economic development lies (Hobolt et al. 2013), leaving room for partisans to attribute responsibility in line with their political identity. This makes the partisan attribution gap larger. In such situations, partisan-motivated reasoning about the state of the economy will lead to an attribution gap rather than large perception gap (Parker-Stephen 2013). When institutional responsibility is clear, the partisan perception gap should be larger, because “interpreting responsibility is difficult for partisans to justify when responsibility is not in doubt. This is why partisans in high-clarity countries interpret the facts about the economy differently” (Parker-Stephen 2013: 510).

Media-Party Parallelism and the Partisan Perception Gap

This paper argues that the degree of media-party parallelism in a media system is another important country-level variable that moderates the size of the partisan perception gap. The mass media are an important source of information about economic development and have a strong influence on people’s perception of the state of the national economy (e.g., Mutz 1992; Soroka 2014; Van Dalen et al. 2016). Broadly, media-party parallelism refers to the extent to which there is a connection between parties, on one hand, and media outlets, on the other hand (Seymour-Ure 1974; Van Kempen 2007). An important element of political parallelism is the extent to which media outlets “reflect distinct political orientations in their news and current affairs reporting” (Hallin and Mancini 2004: 28). In countries where media-party parallelism is low, media coverage is characterized by political nonpartiality, and there are no large differences in political perspective across media outlets. Where media-party parallelism is high, media outlets have distinct political profiles as their coverage reflects the perspective of different political groups. The degree of media-party parallelism varies widely across Europe and is larger in countries belonging to the polarized-pluralist media system (such as Spain, Greece, or Italy), than in countries belonging to the democratic-corporatist media system (such as Denmark, Belgium, or Norway) (e.g., Albæk et al. 2014; Brüggemann et al. 2014; Hallin and Mancini 2004). Politically partial coverage is also a common feature in Central-Eastern European countries, although, associations between parties and media outlets are less stable (Castro-Herrero et al. 2016; Hallin and Mancini 2011).

Media-party parallelism can most clearly be observed in editorials and opinion pages, but also affects the broader coverage (e.g., Albæk et al. 2014). This is not only the case for political news, but also for coverage of the national economy. American newspapers give less attention to negative unemployment news when a president is in office whom they endorsed in their editorials (Larcinese et al. 2011). In the United Kingdom, the attention given to economic issues follows the attention given by the party the newspaper endorses (Brandenburg 2006). Also in Germany, unemployment coverage aligned with the political profile of media outlets (Garz 2014).

Media-party parallelism is expected to increase the partisan economic perception gap for several reasons. First, as described above, selective exposure could be an important mechanism explaining why partisans diverge in their economic perceptions. In media systems where media-party parallelism is high and economic coverage has a partisan bias, partisans will be more exposed to one-sided information that confirms their own economic perceptions, while they can more easily avoid opposing information. In a cross-national comparative study, Goldman and Mutz (2011), indeed, showed that partisans are more exposed to congenial views in countries where parallelism between parties and the press is higher. In countries where media-party parallelism is lower, this is less the case (Dahlgren 2019; Skovsgaard et al. 2016). Partisan media use enhances partisan perception gaps for contested political issues (Veenstra et al. 2014). Anson (2016) gives experimental evidence that pro-attitudinal information affects economic perceptions of government supporters and opponents.

A second reason why the partisan economic perception gap should be larger where media-party parallelism is high is that exposure to biased economic coverage makes it more likely to perceive a link between one’s partisan identity and economic perceptions. Zaller (1992: 44) showed that partisans reject new information that challenges their political predisposition, but “only to the extent that they possess the contextual information necessary to perceive a relationship between the message and their predispositions.” When people do not see a connection between the state of the national economy and their partisan identity, they would not take the political leaning of the current government into account when assessing the state of the economy. A partisan media environment could strengthen partisan identities. Exposure to partisan news with an often emotional and negative tone toward political adversaries is related to biased views about political opponents (Iyengar et al. 2012), and people have stronger partisan attachments in media systems with higher levels of press-party parallelism (Horwitz and Nir 2015). This should trigger more partisan motivated reasoning.

Partisan media environments can also trigger more partisan-motivated reasoning through the presence of party cues. The partisan leaning of a newspaper can affect the attention for political actors, the attention for their preferred issues, and the degree of criticism of these actors (e.g., Brandenburg 2006; Haselmayer et al. 2017; Kahn and Kenney 2002). For example, Austrian newspapers gave more attention to press releases of the parties that their readers identify with (Haselmayer et al. 2017). Political elites make the connections between economic evaluations and partisan identity salient through the way they talk about the economy (Hart 2013). Thus, it can be expected that economic coverage in partisan media systems feature party cues that make it likely that the economy will be seen as a partisan issue. Party cues, in turn, influence the partisan economic perception gap (Bisgaard and Slothuus 2018; but see Anson 2016).

While no study has, to my knowledge, systematically studied the influence of media-party parallelism on the gap in economic perceptions, several studies show results that are in line with my expectations. Lelkes (2016) showed that partisan gaps in political trust and satisfaction with democracy are larger in media systems where more ideologically biased content is available. In Denmark, where media-party parallelism is low, and economic news shows limited variation across media outlets with different political leaning (Van Dalen et al. 2018), partisan media exposure did not affect economic perceptions (Bisgaard and Slothuus 2018). The situation is different in the United States, where media coverage including economic news shows more partisan differences (Larcinese et al. 2011). In line with the argument put forward in this paper, Jerit and Barabas (2012: 683) argue that, in the United States, “the contemporary media environment all but creates the conditions for perceptual bias, making it easy for partisans to seek out news that reinforces their political beliefs while avoiding sources that challenge them.”

The following hypothesis will be tested to analyze whether media-party parallelism increases the partisan economic perception gap: The higher the degree of media-party parallelism in a country, the stronger the effect of partisanship on economic perceptions.

Data and Measures

The central hypothesis is tested in a multilevel analysis of the 2014 European Election Studies (EES)’s first postelectoral voter study (Version v4-0-0; Schmitt et al. 2015b). The EES consists of representative surveys in the European Union (EU) member states, collected right after the 2014 European Parliamentary Elections. The surveys included questions about economic perceptions, media use, and partisanship (see Table 1). At the time of the study, there was large variation in the state of the economy across Europe, while the institutional settings of the countries and the degree of media-party parallelism in the media systems also vary widely (see Table A1 in the Supplemental Material). The analyses presented in this paper are based on a dataset including the twenty-six EU member states listed in Table A1 in the Supplemental Material.² For each country, a multistage random sample was drawn with the population of the country’s nationality aged eighteen years and over (sixteen for Austria) as universe. Taylor Nelson Sofres (TNS) Opinion interviewed around 1,100 respondents face-to-face using computer-assisted personal interviewing (CAPI) in each country, with the exception of Cyprus and Malta, with, respectively, 530 and 544 respondents (Schmitt et al. 2015b).

Table 1.

Descriptive Statistics.

	N	Minimum	Maximum	M	SD
Economic perception	27,861	1	5	2.824	0.999
Government responsibility	27,813	0	10	7.689	2.626
Government partisans: Voted for government vs. opposition party	18,106	0	1	0.516	0.500
Partisanship2: Percentage feels close to opposition party	24,630	0	1	0.361	0.480
Partisanship2: Percentage feels close to no party	24,630	0	1	0.346	0.476
Partisanship2: Percentage feels close to government party	24,630	0	1	0.293	0.455
Percentage Female	28,448	0	1	0.549	0.498
Age: Percentage under 25	28,448	0	1	0.083	0.275
Age: Percentage between 25 and 39	28,448	0	1	0.209	0.407
Age: Percentage between 40 and 54	28,448	0	1	0.252	0.434
Age: Percentage over 55	28,448	0	1	0.457	0.498
Political knowledge	28,448	0	1	0.703	0.307
Political interest	28,254	1	4	2.420	0.980
Unemployed	28,448	0	1	0.090	0.286
Feeling economic change	27,448	0	1	0.304	0.317
Percentage Low on socioeconomic ladder	27,629	0	1	0.234	0.424
Percentage Medium on socioeconomic ladder	27,629	0	1	0.523	0.499
Percentage High on socioeconomic ladder	27,629	0	1	0.242	0.428
Trust in national parliament	27,794	1	4	2.323	0.924
News exposure	28,280	0	10	6.780	2.754
Change in GDP since last year (mean centered)	28,448	–2.351	2.977	0	1
Level of unemployment (mean centered)	28,448	–1.025	3.169	0	1
Annual change in inflation (mean centered)	28,448	–2.767	1.908	0	1
Institutional clarity (mean centered)	28,448	–1.706	1.798	0	1
Media-party parallelism (mean centered)	28,448	–1.529	2.875	0	1
Political polarization among citizens (mean centered)	28,448	–1.444	2.193	0	1
Elite polarization (mean centered)	22,999	–1.615	1.772	0	1

Dependent Variable

The dependent variable of the study is economic perceptions. It is measured with the following question: “What do you think about the economy? Compared with twelve months ago, do you think that the general economic situation in (OUR COUNTRY) . . .?” The responses ranged from 1 = a lot better, 2 = a little better, 3 = stayed the same, to 4 = a little worse, and 5 = a lot worse. In line with the theoretical argument that government partisans should be more positive about economic developments and attribute this to the work done by the government, this is a retrospective measure of economic perceptions, asking the respondents to evaluate developments of the past year. Because the government should be expected to have a more direct influence on the broader economy than on people’s personal economy, the economic perception questions deal with the general economic situation (so-called sociotropic perceptions). Following Parker-Stephen (2013), it is assumed that the variable is measured at interval-level.

Independent Variables

Government partisans versus opposition partisans were operationalized in two ways. The first operationalization is a government partisan-dummy based on the party that respondents voted for during the latest national elections (Lelkes 2016). Respondents who voted for a party that was in government at the time of the survey received value “1.” Respondents who voted for another party received value “0.” Respondents who did not belong to one of these groups were coded as missing. The second operationalization (partisanship2) is based on how close people feel to a political party (Parker-Stephen 2013). Respondents who consider themselves close to a government party were coded as government supporters. Those who chose an opposition party were coded as opposition party supporters. People who did not consider themselves close to any party were coded into a separate category. As both operationalizations lead to the same conclusions, the analyses using the government partisan-dummy variable are presented in the paper and the analyses for the partisanship2 variable are presented in the Supplemental Material.

Media-party parallelism is the main country-level indicator of interest. This is operationalized as the “partall”-measure of the 2010 European Media Systems Survey (EMSS; Popescu et al. 2011). This measure is based on national experts who ranked the degree to which media outlets in their country advocated for special policies, and the degree to which their coverage was influenced by a specific party. The measure is an aggregated score of the measures of the different outlets weighted by the audience shares of these outlets. By giving more weight to outlets with a larger audience share than to niche outlets, the media-party parallelism measure reflects the overall composition of a country’s media system. This measure focuses on the alignment between parties and outlets based on content rather than on audience affiliations. It has to be acknowledged that the measure is not based on an actual content analysis, but rather on expert evaluations. Such expert evaluations come with the inherent limitation that it is difficult to test data validity because of a lack of benchmarks (see Popescu et al. 2011 for a discussion). Other studies have also used these expert ratings to operationalize country-level differences in party parallelism in media content (Brüggemann et al. 2014; Lelkes 2016). Lelkes (2016: 539) found that the expert evaluations align closely, but not perfectly, with Van Kempen’s (2007) media-party parallelism measure based on the political affiliation of media audiences. Brüggemann et al. (2014: 1,048) found that the EMSS expert ratings form a reliable media-political parallelism scale together with other partisanship measures, including actual content data on the separation between news and opinions. Although this gives some evidence for the validity of the measure, the expert evaluations should be seen as a proxy of partisanship in actual media content. The advantage of using this measure is that it is available for twenty-six countries included in the EES.

Other country-level variables of interest were real economic developments, measured separately by three indicators: annual change in GDP for the second quarter of 2014,³ the national level of unemployment as percentage of working population,⁴ and the annual rate of change in inflation.⁵ Clarity of institutional responsibility was operationalized with the institutional clarity index by Hobolt, Tilley, and Banducci (2013), which is based on the operationalization by Powell and Whitten (1993) and measures “the degree to which power is concentrated institutionally” (Hobolt et al. 2013: 174). Strong committee structures, unitary state, parliamentary system, and unicameralism are indicators of clear institutional responsibility. These items are combined in an index, which can range from 0 to 1. This institutional clarity index can be seen as stable over the medium term (Hobolt et al. 2013: 175).⁶

News exposure is measured on a scale from 1 (never follows the news) to 6 (follows the news everyday/almost everyday). The answers to questions asking about exposure to news on television and in newspapers were recoded and combined in one news exposure-measure ranging from 0 to 10).⁷

Several individual-level control variables were included in the analyses that were expected to be relevant for both partisanship and economic perceptions: a dummy variable indicating whether the respondent was female; a four-category variable for age (16/18–24; 25–39; 40–54; 55+); a political knowledge scale ranging from 0 to 1 based on two knowledge questions. Respondents received a score of 0 if none was answered correctly, 0.5 if one was answered correctly, and 1 if both questions were answered correctly; a four-category variable measuring political interest (“Are you interested in politics?” 1 = No, not at all; 2 = No, not really; 3 = Yes, to some extent; 4 = Yes, definitely); a three category variable for position on the socioeconomic ladder (low, medium, high); a dummy variable indicating whether the respondent was unemployed; a measure of feeling economic change ranging from 0 to 2 (this measure was based on three items: troubles paying bills, lost job, and decreased income); and a measure of trust in national parliament measured as a four-category variable (“Do you trust the national parliament?”: 1 = No, not at all; 2 = No, not really; 3 = Yes, somewhat; 4 = Yes, totally).

To take the hierarchical structure of the data into account, the hypothesis is tested in random-intercept regression models using maximum likelihood estimation (Hox et al. 2010). First, it is tested whether economic perceptions vary significantly between government and opposition supporters. Second, it is tested whether the effect of being either a government or opposition supporter is moderated by the degree of media-party parallelism in a country. This would support the hypothesis and show that the partisan perception gap is larger in a more polarized-media system. Thus, a model was run that included a cross-level interaction between this country-level variable and the government partisan-variable. A significant positive cross-level interaction between media-party parallelism and being a government partisan indicates support for the hypothesis. Castro-Herrero et al. (2018) and Lelkes (2016) take a similar approach to test whether the media environment affects gaps in cross-cutting exposure and legitimacy, respectively. In addition, the model includes cross-level interactions between, on one hand, the government partisan-variable and, on the other hand, the economic conditions in the country and institutional clarity, respectively. The country-level variables included in the interactions were standardized. Additional analyses looked into whether the moderating effect of media-party parallelism is stronger for people with more news exposure.

Results

Figure 1 shows the mean economic perception of opposition and government partisans in twenty-six European countries on a scale from 1 (perceiving that the economy does a lot worse than a year ago) to 5 (perceiving that the economy does a lot better than a year ago). The farther apart the mean scores are, the larger the economic partisan perception gap in the country. In all but three countries, people supporting the government have significantly more positive economic perceptions than supporters of opposition parties (p < .05, two-tailed t tests). Although the partisan perception gap is present across Europe, there is also large variation in the size of the gap. There is no significant difference in Belgium and Czech Republic. Lithuania is the only country where opposition supporters are more positive about the economy than government supporters. If we consider 3 to be the neutral middle point of the scale, we can see that in five countries, government supporters and opposition supporters have contrasting views on the state of the economy. In Cyprus, Estonia, Latvia, Malta, and Spain, government supporters believe, on average, that the economy improved, while opposition supporters, on average, believe that the economy declined. The difference is largest in Malta, where the partisan gap is 1.5 points on a 5-point scale.

Figure 1.

Economic perception among opposition and government partisans.

The partisan economic perception gap is confirmed in multilevel regression analysis, which controls for several individual- and country-level variables and takes the hierarchical structure of the data into account (see Table 2). From the individual-level control variables included in Model 1, political knowledge, political interest, trust in national parliament, and high socioeconomic status are related to more positive economic perceptions. Economic perceptions are more negative for women, older people, people who are unemployed or otherwise negatively affected by economic change, as well as people with low socioeconomic status. Looking at all European countries combined, news exposure does not have an effect on economic perceptions. Of the country-level variables, only change in GDP has a significant effect. People are more positive about the economy in countries where GDP grew over the last year.⁸ After controlling for these individual-level and country-level variables, there is a positive effect of being a government partisan on economic perceptions, confirming the presence of the partisan perception gap (see Model 1).

Table 2.

Explaining Economic Perceptions across Europe.

	Model 1		Model 2
	B	SE	B	SE
Female	–0.087***	0.013	–0.085***	0.013
Age: Baseline = under 25
25–39	–0.124***	0.034	–0.130***	0.034
40–54	–0.223***	0.034	–0.230***	0.034
55+	–0.272***	0.033	–0.279***	0.033
Unemployed	–0.090***	0.027	–0.090***	0.027
Political knowledge	0.054*	0.026	0.055*	0.026
Political interest	0.050***	0.008	0.050***	0.008
Feeling economic change	–0.323***	0.025	–0.323***	0.025
Trust in national government	0.259***	0.008	0.255***	0.008
Position on socioeconomic ladder: baseline = medium
Low on socioeconomic ladder	–0.112***	0.018	–0.113***	0.018
High on socioeconomic ladder	0.079***	0.016	0.086***	0.016
News exposure	0.001	0.003	0.002	0.003
Government partisan	0.228***	0.014	0.238***	0.014
Change in GDP since last year	0.161**	0.059	0.157**	0.058
Level of unemployment	–0.079	0.061	–0.051	0.061
Institutional clarity	0.038	0.060	0.003	0.060
Media-Party parallelism	–0.022	0.057	–0.099	0.056
Government partisan × Level of unemployment			–0.046**	0.015
Government partisan × Institutional clarity			0.057***	0.014
Government partisan × Media-Party parallelism			0.148***	0.014
Constant	2.338***	0.073	2.337***	0.072
N (level 1)	16,777		16,777
N (level 2)	26		26
Variance: country (Intercept)	0.080		0.078
Variance: Residual	0.695		0.689
Log likelihood	–20,810.462		–20,734.531

Note. Unstandardized coefficients.

*p < .05.

**p < .01.

***p < .001 (two-tailed t tests).

Significant cross-level interaction effects confirm that the size of the partisan perception gap is significantly related to the national context (Model 2). First, there is a significant negative cross-level interaction effect of levels of unemployment and government partisanship on economic perceptions. In line with previous research, this confirms that the partisan perception gap is smallest where the economy is doing unambiguously badly, and even government supporters have to acknowledge the dire state of the economy (see also Bisgaard 2015; Dickerson and Ondercin 2017). The national level of unemployment is the only indicator of the state of the economy that is included in the cross-level analysis. When national unemployment is included together with change in GDP and change in inflation, only national unemployment interacts significantly with partisanship (see Table A2 in Supplemental Material).

Second, there is a significant positive cross-level interaction of institutional clarity and government partisanship on economic perceptions. Thus, partisanship, indeed, plays a larger role in the formation of economic perceptions where there is less ambiguity about the responsibilities of the government (see also Bolsen et al. 2014; Parker-Stephen 2013).

Third, after controlling for the moderating effect of levels of unemployment and institutional clarity, there is a positive cross-level interaction effect between the degree of media-party parallelism in a country and being a government partisan, indicating that the effect of partisanship on economic perceptions is stronger in countries with higher levels of media-party parallelism.⁹ In other words, media-party parallelism exacerbates the partisan perception gap, supporting the hypothesis. The significant cross-level interaction effect of media-party parallelism and government partisanship is confirmed when partisanship is operationalized as feeling close to a government party (see Table A3 in Supplemental Material), in analyses where the five countries with the highest levels of media-party parallelism are excluded (see Table A4 in Supplemental Material), and in analyses where three countries that had elections within the last year were excluded (see Table A5 in Supplemental Material).

The influence of media-party parallelism on the partisan economic perception gap is illustrated in Figure 2. The figure shows the predicted mean economic perceptions for government partisans and opposition partisans based on regression Model 2 in Table 2. The figure illustrates how the mean economic perceptions of these two groups vary depending on the level of media-party parallelism in a country (ceteris paribus), ranging from low levels of media-party parallelism to high levels of media-party parallelism. On the left side of the figure, where media-party parallelism is low, economic perceptions of government partisans and opposition partisans converge around the neutral middle point of the 5-point scale. As we move farther to the right of the figure and media-party parallelism increases, economic perceptions diverge. Government supporters become slightly more positive, while a large change can be seen among supporters of opposition parties, whose economic perceptions are 0.5 points more negative in the countries with the most partisan media system, compared with the countries with the lowest level of media-partisanship.

Figure 2.

Predicted mean economic perceptions for opposition and government partisans with 95 percent confidence intervals.

Confirming the Role of the Media

Additional analyses further support that media-party parallelism strengthens the effect of partisan identity on economic perceptions. First, the central hypothesis is confirmed after controlling for other types of polarization. One potential confounding factor is the degree of political polarization among the population, which could both influence the degree of polarization in the media and in economic perceptions. To test for this, Model 2 was run while additionally controlling for the degree of political polarization among the population in each country, operationalized as the standard deviation of the Left-Right political leaning in each country, which the EES measured on an 11-point scale (see Table A6 in Supplemental Material). As can be expected, the degree of political polarization strengthens the effect of being a government partisan. Thus, indeed, the partisan perception gap is larger in countries where the population is more polarized. Even after controlling for this, the cross-level interaction effect between media-party parallelism and government partisanship is confirmed. Following Lelkes (2016: 535), the results were furthermore confirmed in a model that included the national level of elite polarization as a control (see Table A7 in Supplemental Material).¹⁰

It was analyzed whether the moderating effect of media-party parallelism is stronger for people with more news exposure. This would be further evidence that the media affect the size of the partisan perception gap. There was no significant three-way interaction effect of partisanship, media-party parallelism, and the continuous news exposure variable on economic perceptions (see Table 3). However, the news exposure variable was highly skewed as 24 percent of the respondents indicated they follow news on television and in newspapers (almost) every day. Therefore, additional exploratory analysis was done with a dummy variable for respondents with the highest level of media use versus all other respondents. In support of the role of the media was a significant three-way interaction of government partisanship, media-party parallelism, and a high news exposure-dummy variable. Thus, the effect of media-party parallelism on differences in economic perceptions between opposition and government supporters is strongest for respondents with the highest level of news consumption.

Table 3.

Explaining Economic Perceptions across Europe; Exploratory Analysis of the Effect of News Exposure on the Relation between Media-Party Parallelism and the Partisan Perception Gap.

	Model 1Continuous News Exposure Measure		Model 2High News Exposure-Dummy
	B	SE	B	SE
Female	–0.085***	0.013	–0.086***	0.013
Age: Baseline = under 25
25–39	–0.130***	0.034	–0.128***	0.034
40–54	–0.229***	0.034	–0.224***	0.033
55+	–0.278***	0.033	–0.268***	0.033
Unemployed	–0.090***	0.027	–0.091***	0.027
Political knowledge	0.055*	0.026	0.058*	0.026
Political interest	0.050***	0.008	0.052***	0.008
Feeling economic change	–0.324***	0.025	–0.325***	0.025
Trust in national government	0.254***	0.008	0.255***	0.008
Position on socioeconomic ladder: Baseline = medium
Low on socioeconomic ladder	–0.113***	0.018	–0.115***	0.018
High on socioeconomic ladder	0.085***	0.016	0.086***	0.016
News exposure	–0.003	0.011	–0.063**	0.023
Government partisan	0.238***	0.014	0.227***	0.016
Government partisan × News exposure	0.017	0.015	0.077*	0.031
Change in GDP since last year	0.156**	0.058	0.156**	0.058
Level of unemployment	–0.052	0.061	–0.055	0.061
Institutional clarity	0.003	0.060	0.004	0.060
Media-Party parallelism	–0.099	0.056	–0.093	0.057
Government partisan × Level of unemployment	–0.044**	0.015	–0.042**	0.015
Government partisan × Institutional clarity	0.058***	0.014	0.058***	0.014
Government partisan × Media-Party parallelism	0.149***	0.015	0.131***	0.016
News exposure × Media-Party parallelism	–0.004	0.010	–0.038	0.022
Gov. partisan × News exposure × Media-Party parallelism	0.012	0.014	0.085**	0.031
Constant	2.352***	0.072	2.350***	0.072
N (level 1)	16,777		16,777
N (level 2)	26		26
Variance: Country (Intercept)	0.079		0.079
Variance: Residual	0.690		0.689
Log likelihood	–20,743.147		–20,738.156

Note. Unstandardized coefficients.

*p < .05.

**p < .01.

***p < .001 (two-tailed t tests).

Figure 3 shows the predicted effect of being a government supporter for people with lower news exposure and people with higher news exposure, based on regression Model 2 in Table 3. The figure illustrates how this effect varies depending on the level of media-party parallelism in a country (ceteris paribus), ranging from low levels of media-party parallelism to a high level of media-party parallelism. On the left side of the figure, where media parallelism is low, being a government partisan has no effect. As we move farther to the right of the figure and media-party parallelism increases, the effect becomes stronger for both people with lower and higher levels of news exposure. The increase is larger for people with higher levels of news exposure. Thus, in more partisan media systems, the partisan perception gap is present for both people with high and lower media use, while the gap is largest for people with high media use.

Figure 3.

Predicted marginal effect of being a government partisan on economic perceptions at different levels of media-party parallelism for people with higher and lower levels of news exposure with 95 percent confidence intervals.

Conclusion and Discussion

Research in widely varied contexts has shown that people’s partisan identity affects their economic perceptions. At the same time, the strength of this effect varies cross-nationally and depends on factors such as the state of the economy and institutional clarity. This paper adds to our understanding of cross-national differences in the economic perception gap by showing that the degree of media-party parallelism in a country affects the degree of divergence in economic perceptions between government and opposition supporters. In countries where partisan bias in media content is not common, government supporters have similar economic perceptions as opposition supporters (ceteris paribus). In countries where media content has a clear political slant, opposition supporters are significantly more pessimistic about economic developments than are government supporters. The moderating effect of media-party parallelism is significant after controlling for the state of the economy, institutional clarity, and even political polarization among the population and elite polarization.

While effects of media-party parallelism on the partisan perception gaps were stronger for people with more media exposure, also people with low media exposure were significantly affected. This suggests that media-party parallelism also has an effect on the partisan perception gap for people who are not directly exposed. One explanation for this could be that media-party parallelism affects others beyond direct-exposure. One explanation for this could be a two-step-flow process where people who are exposed to partisan news affect the economic perceptions of those around them through interpersonal communication (see Van Kempen 2007).

On the macro-level, the findings suggest that the media environment is one of the key contextual factors that help us to understand cross-national differences in the partisan economic perception gap. Similarly, single-country studies in countries where the degree of media-party parallelism changes over time should take the rise or fall of partisan media content into account when studying longitudinal trends in the partisan perception gap. In democratic-corporatist media systems, newspapers gradually lost their partisan slant after the 1980s but have shown signs of increasing partisanship in later periods (Schultz 2007). In the United States, polarization of political elites and a fragmentation of the media system led to the rise of openly partisan media outlets (Ladd 2011). Such trends should affect the size of the partisan perception gap over time.

The effect of media-party parallelism on partisan gaps may extend beyond partisan perceptions and affect partisan attributions as well. This study has confirmed that the partisan perception gap is smaller where unemployment is high. Previous research has shown that partisans can align this with their political identity by attributing responsibility selectively. When economic malaise cannot be denied, opposition supporters are more likely to blame the current economic situation on the sitting government than are government supporters (Bisgaard 2015). Apart from self-serving attribution biases (Hobolt and Tilley 2014: 20), selective exposure facilitated by partisan media systems could also be an explanation for selective attribution. Reading articles about who should be held accountable for societal developments affects how people attribute responsibility (Iyengar 1994; Valkenburg et al. 1999). Thus, if partisans are selectively exposed to news holding either the government or others responsible, this should reinforce the economic attribution gap. Unfortunately, the possibility to test the effect of media-party parallelism on attribution gaps with the EES is limited, as the study only includes twenty-six countries.¹¹

A cross-national comparative design was necessary to consider other contextual variables when studying the effect of media-party parallelism on the partisan perception gap. As a trade-off, this necessarily comes with limitations, which should be the topic of future research. First, the breadth of the analysis goes at the expense of a deeper understanding of the mechanisms linking the media system with economic perceptions, or of why certain countries such as Lithuania do not match the general pattern. Another limitation is the lack of content analysis data measuring political bias in economic coverage. The expert measures used in this study only serve as a proxy for partisanship in media content. Political bias might manifest itself in subtle ways, and it will take a lot of resources to measure this in a comparative twenty-six-country study, especially when measured across different types of outlets. Still, single country studies or carefully selected comparative studies with a limited number of cases might include content analysis measures in the future. A combination of surveys and content analysis methods would also allow us to study whether negative information has more effects on partisan perceptions than positive information, in line with the well-known negativity bias (Soroka 2014). Another trade-off of the cross-national design was the lack of precision in individual-level news exposure measures, which, for example, did not measure social media access and only differentiated between a limited number of outlets. Future research could also take the role of trust in the media into account, which was not measured in the EES.

Experiments or case studies of specific economic events would give more insight into the mechanisms that link the presence of partisan content on the system levels with the partisan perception gap among the population. Forthcoming studies could assess how the relation between media-party parallelism, news exposure, and economic perceptions is affected by technological changes, such as the spread of broadband (Lelkes et al. 2017) or the growing importance of platforms such as Google or Facebook (Cardenal et al. 2019).

Further research is important given the practical implications of the findings. Economic perceptions are an important antecedent of voting decisions (Hetherington 1996), and when partisans misperceive the state of the economy, they will be less likely to hold governments accountable for the real state of the economy (e.g., Ladd 2011). The finding that media-party parallelism strengthens the partisan economic perception gap corroborates research showing that there is more cross-cutting news exposure in media systems where public service broadcasting has a large market share, because media coverage is more politically balanced in these media systems (Castro-Herrero et al. 2018: 12). This suggests that media-party parallelism depends on the way a media system is organized and, in particular, on the strength of media institutions. By extension, media policy, which strengthens such institutions, could narrow the partisan perception gap.

Supplemental Material

sj-pdf-1-hij-10.1177_1940161220926931 - Supplemental material for Red Economy, Blue Economy: How Media-Party Parallelism Affects the Partisan Economic Perception Gap

Supplemental material, sj-pdf-1-hij-10.1177_1940161220926931 for Red Economy, Blue Economy: How Media-Party Parallelism Affects the Partisan Economic Perception Gap by Arjen van Dalen in The International Journal of Press/Politics

Footnotes

Acknowledgments

The author thanks Thor Bech Schrøder and Camilla Lund Knudsen for their help with data analysis. Furthermore, he thanks the editor and anonymous reviewers, as well as Martin Bisgaard and the members of the journalism research group at the University of Southern Denmark, for their valuable inputs.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Arjen van Dalen

Supplemental Material

Supplemental material for this article is available online.

Notes

Author Biography

Arjen van Dalen is a professor with Special Responsibilities at the Centre for Journalism of the Department of Political Science at the University of Southern Denmark. He wrote his PhD dissertation on political journalism in comparative perspective. His main research interests are in political communication, in particular, the relations between journalists and politicians, as well as economic news. He has published on these topics with Cambridge University Press and in journals such as Political Communication, International Journal of Press/Politics, Journalism, and Public Opinion Quarterly.

References

Albæk, Erik, Arjen Van Dalen, Nael Jebril and Claes H. de Vreese. 2014. Political Journalism in Comparative Perspective. Cambridge: Cambridge University Press.

Anson

Ian G.

2016. “Just the Facts? Partisan Media and the Political Conditioning of Economic Perceptions.” Political Research Quarterly 69 (3): 444–56.10.1177/1065912916648016

Bartels

Larry M.

2002. “Beyond the Running Tally: Partisan Bias in Political Perceptions.” Political Behavior 24 (2): 117–50.10.1023/A:1021226224601

Bisgaard

Martin.

2015. “Bias Will Find a Way: Economic Perceptions, Attributions of Blame, and Partisan-motivated Reasoning during Crisis.” The Journal of Politics 77 (3): 849–60.10.1086/681591

Bisgaard

Martin.

2019. “How Getting the Facts Right Can Fuel Partisan‐Motivated Reasoning.” American Journal of Political Science 63 (4): 824–39.10.1111/ajps.12432

Bisgaard

Martin

and Rune Slothuus. 2018. “Partisan Elites as Culprits? How Party Cues Shape Partisan Perceptual Gaps.” American Journal of Political Science 62 (2): 456–69.10.1111/ajps.12349

Bisgaard

Martin

, Peter Thisted Dinesen and Kim Mannemar Sønderskov. 2016. ” Reconsidering the Neighborhood Effect: Does Exposure to Residential Unemployment Influence Voters’ Perceptions of the National Economy?” The Journal of Politics 78 (3): 719–32.10.1086/685088

Bolsen

Toby

, James N. Druckman and Fray Lomax Cook. 2014.” The Influence of Partisan Motivated Reasoning on Public Opinion.” Political Behavior 36 (2): 235–62.10.1007/s11109-013-9238-0

Boomgaarden

Hajo G.

, Joost van Spanje, Rens Vliegenthart and Claes H. de Vreese. 2011. ” Covering the Crisis: Media Coverage of the Economic Crisis and Citizens' Economic Expectations.” Acta Politica 46 (4): 353–79.10.1057/ap.2011.18

10.

Brandenburg

Heinz.

2006. “Party Strategy and Media Bias: A Quantitative Analysis of the 2005 UK Election Campaign.” Journal of Elections, Public Opinion and Parties 16 (2): 157–78.10.1080/13689880600716027

11.

Brüggemann

Michael

, Sven Engesser, Florin Büchel, Edda Humprecht and Laia Castro. 2014. “Hallin and Mancini Revisited: Four Empirical Types of Western Media Systems.” Journal of Communication 64 (6): 1037–65.10.1111/jcom.12127

12.

Campbell

Angus

, Philip E. Converse, Warren E. Miller and Donald E. Stokes. 1960. The American Voter. Ann Arbor, MI: University of Michigan Press.

13.

Cardenal

Ana S.

, Carlos Aguilar-Paredes, Carol Galais, and Mario Pérez-Montoro. 2019. "Digital Technologies and Selective Exposure: How Choice and Filter Bubbles Shape News Media Exposure." The International Journal of Press/Politics 24 (4): 465–86.10.1177/1940161219862988

14.

Castro-Herrero

Laia

, David N. Hopmann and Sven Engesser. 2016. “Parties, Ideology, and News Media in Central-Eastern and Western Europe: A Comparison of Media Bias Twenty Years after the Fall of the Iron Curtain.” East European Politics and Societies 30 (3): 571–93.10.1177/0888325415625090

15.

Castro-Herrero

Laia

, Lilach Nir and Morten Skovsgaard. 2018. “Bridging Gaps in Cross-cutting Media Exposure: The Role of Public Service Broadcasting.” Political Communication 35 (4): 542–65.10.1080/10584609.2018.1476424

16.

Conover

Pamela Johnston

, Stanley Feldman and Kathleen Knight. 1986. “Judging Inflation and Unemployment: The Origins of Retrospective Evaluations.” The Journal of Politics 48 (3): 565–88.10.2307/2131168

17.

Dahlgren

Peter M.

2019. “Selective Exposure to Public Service News over Thirty Years: The Role of Ideological Leaning, Party Support, and Political Interest.” The International Journal of Press/Politics 24 (3): 293–314.10.1177/1940161219836223

18.

Dalton

Russell J.

2008. “The Quantity and the Quality of Party Systems: Party System Polarization, its Measurement, and its Consequences.” Comparative Political Studies 41 (7): 899–920.10.1177/0010414008315860

19.

Damstra

Alyt

and Mark Boukes. 2018. “The Economy, the News, and the Public: A Longitudinal Study of the Impact of Economic News on Economic Evaluations and Expectations.” Communication Research. doi:0093650217750971.

20.

De Vries

Catherine E.

, Sara B. Hobolt and James Tilley. 2018

.” Facing up to the Facts: What Causes Economic Perceptions?”

Electoral Studies 51: 115–22.10.1016/j.electstud.2017.09.006

21.

Dickerson

Bradley T.

and Heather L. Ondercin. 2017. “Conditional Motivated Reasoning: How the Local Economy Moderates Partisan Motivations in Economic Perceptions.” Political Research Quarterly 70 (1): 194–208.10.1177/1065912916684031

22.

Duch

Raymond

and Randolph T. Stevenson. 2008. The Economic Vote: How Political and Economic Institutions Condition Election Results. Cambridge: Cambridge University Press.

23.

Evans

Geoffrey

and Robert Andersen. 2006. “The Political Conditioning of Economic Perceptions.” The Journal of Politics 68 (1): 194–207.10.1111/j.1468-2508.2006.00380.x

24.

Frey

Dieter.

1982. “Different Levels of Cognitive Dissonance, Information Seeking, and Information Avoidance.” Journal of Personality and Social Psychology 43 (6): 1175–83.10.1037/0022-3514.43.6.1175

25.

Gaines

Brian J.

, James H. Kuklinski, Paul J. Quirk, Buddy Peyton and Jay Verkuilen. 2007. “Same Facts, Different Interpretations: Partisan Motivation and Opinion on Iraq.” The Journal of Politics 69 (4): 957–74.10.1111/j.1468-2508.2007.00601.x

26.

Garz

Marcel.

2014. “Good News and Bad News: Evidence of Media Bias in Unemployment Reports.” Public Choice 161 (3-4): 499–515.10.1007/s11127-014-0182-2

27.

Gerber

Alan S.

and Gregory A. Huber. 2010

.” Partisanship, Political Control, and Economic Assessments.”

American Journal of Political Science 54 (1): 153–73.10.1111/j.1540-5907.2009.00424.x

28.

Goldman

Seth K.

and Diana C. Mutz. 2011. “The Friendly Media Phenomenon: A Cross-national Analysis of Cross-cutting Exposure.” Political Communication 28 (1): 42–66.10.1080/10584609.2010.544280

29.

Hallin

Daniel C.

and Paolo Mancini. 2004. Comparing Media Systems: Three Models of Media and Politics. Cambridge: Cambridge University Press.

30.

Hallin

Daniel C.

and Paolo Mancini. 2011. Comparing Media Systems Beyond the Western World. Cambridge: Cambridge University Press.

31.

Hart

Austin.

2013. “Can Candidates Activate or Deactivate the Economic Vote? Evidence from Two Mexican Elections.” The Journal of Politics 75 (4): 1051–63.10.1017/S0022381613001011

32.

Haselmayer

Martin

, Markus Wagner and Thomas M. Meyer. 2017. “Partisan Bias in Message Selection: Media Gatekeeping of Party Press Releases.” Political Communication 34 (3): 367–84.10.1080/10584609.2016.1265619

33.

Hetherington

Marc J.

1996. “The Media's Role in Forming Voters' National Economic Evaluations in 1992.” American Journal of Political Science 40 (2): 372–95.10.2307/2111629

34.

Hobolt

Sara B.

and James Tilley. 2014. Blaming Europe? Responsibility without Accountability in the European Union. Oxford: Oxford University Press.

35.

Hobolt

Sara B.

, James Tilley and Susan Banducci. 2013. “Clarity of Responsibility: How Government Cohesion Conditions Performance Voting.” European Journal of Political Research 52 (2): 164–87.10.1111/j.1475-6765.2012.02072.x

36.

Holbert

R. Lance

, R. Kelly Garrett and Laurel S. Gleason. 2010. “A New Era of Minimal Effects? A Response to Bennett and Iyengar.” Journal of Communication 60 (1): 15–34.10.1111/j.1460-2466.2009.01470.x

37.

Horwitz

S. Nechama

and Lilach Nir. 2015. “How Politics-News Parallelism Invigorates Partisanship Strength.” International Political Science Review 36 (2): 153–67.10.1177/0192512113516900

38.

Hox

Joop

, Mirjam Moerbeek and Rens Van de Schoot (Eds.). 2010. Multilevel Analysis: Techniques and Applications. London: Routledge.

39.

Iyengar

Shanto.

1994. Is Anyone Responsible? How Television Frames Political Issues. Chicago: University of Chicago Press.

40.

Iyengar

Shanto

, Gaurav Sood and Yphtach Lelkes. 2012. ” Affect, Not Ideology. A Social Identity Perspective on Polarization.” Public Opinion Quarterly 76 (3): 405–31.10.1093/poq/nfs038

41.

Jerit

Jennifer

and Jason Barabas. 2012. “Partisan Perceptual Bias and the Information Environment.” The Journal of Politics 74 (3): 672–84.10.1017/S0022381612000187

42.

Jonkman

Jeroen

, Mark Boukes and Rens Vliegenthart. 2020. “When Do Media Matter Most? A Study on the Relationship between Negative Economic News and Consumer Confidence across the Twenty-Eight EU States.” The International Journal of Press/Politics 25 (1): 76–95.10.1177/1940161219858704

43.

Kahn

Kim F.

and Patrick J. Kenney. 2002

.” The Slant of the News: How Editorial Endorsements Influence Campaign Coverage and Citizens' Views of Candidates.”

American Political Science Review 96 (2): 381–94.10.1017/S0003055402000230

44.

Ladd

Jonathan M.

2011. Why Americans Hate the Media and How it Matters. Princeton: Princeton University Press.

45.

Larcinese

Valentino

, Riccardo Puglisi and James M. Snyder Jr. 2011. “Partisan Bias in Economic News: Evidence on the Agenda-setting Behavior of U.S. Newspapers.” Journal of Public Economics 95: 1178–89.10.1016/j.jpubeco.2011.04.006

46.

Lelkes

Yphtach.

2016. “Winners, Losers, and the Press: The Relationship between Political Parallelism and the Legitimacy Gap.” Political Communication 33 (4): 523–43.10.1080/10584609.2015.1117031

47.

Lelkes

Yphtach

, Gaurav Sood and Shanto Iyengar. 2017. “The Hostile Audience: The Effect of Access to Broadband Internet on Partisan Affect.” American Journal of Political Science 61 (1): 5–20.10.1111/ajps.12237

48.

Mutz

Diana C.

1992. “Mass Media and the Depoliticization of Personal Experience.” American Journal of Political Science 36 (2): 483–508.10.2307/2111487

49.

Parker-Stephen

Evan.

2013. “Clarity of Responsibility and Economic Evaluations.” Electoral Studies 32 (3): 506–11.10.1016/j.electstud.2013.05.006

50.

Popescu

Marina

, Gabor Toka, Tania Gosselin and Jose Santana Pereira. 2011. European Media Systems Survey 2010: Results and Documentation. Colchester, UK: University of Essex.

51.

Powell Jr G. Bingham and Guy D. Whitten. 1993. “A Cross-national Analysis of Economic Voting: Taking Account of the Political Context.” American Journal of Political Science 37 (2): 391–414.10.2307/2111378

52.

Schaffner

Brian F.

and Cameron Roche. 2016. “Misinformation and Motivated Reasoning: Responses to Economic News in a Politicized Environment.” Public Opinion Quarterly 81 (1): 86–110.

53.

Schmitt

Hermann

, Sara B. Hobolt and Sebastian A. Popa. 2015. “Does Personalization Increase Turnout? Spitzenkandidaten in the 2014 European Parliament Elections.” European Union Politics 16 (3): 347–68.10.1177/1465116515584626

54.

Schmitt Hermann , Sara B. Hobolt, Sebastian A. Popa and Eftichia Teperoglou. 2015. European Parliament Election Study 2014, Voter Study. GESIS Data Archive, Cologne. ZA5160 Data file Version, 2(0).

55.

Schultz

Ida.

2007. “Fra Partipresse over Omnibuspresse til Segmentpresse [From Party Press to Mainstream Press to Segment Press].” Journalistica - Tidsskrift for Forskning i Journalistik 5: 5–26.

56.

Seymour-Ure

Colin.

1974. The Political Impact of Mass Media. London: Constable.

57.

Skovsgaard

Morten

, Adam Shehata and Jesper Strömbäck. 2016. “Opportunity Structures for Selective Exposure: Investigating Selective Exposure and Learning in Swedish Election Campaigns Using Panel Survey Data.” The International Journal of Press/Politics 21 (4): 527–46.10.1177/1940161216658157

58.

Soroka

Stuart N.

2014. Negativity in Democratic Politics. Causes and Consequences. Cambridge: Cambridge University Press.

59.

Soroka

Stuart N.

, Dominik A. Stecula and Christopher Wlezien. 2015. “It's (Change) in the (Future) Economy, Stupid: Economic Indicators, the Media, and Public Opinion.” American Journal of Political Science 59 (2): 457–74.10.1111/ajps.12145

60.

Stanig

Piero.

2013. “Political Polarization in Retrospective Economic Evaluations during Recessions and Recoveries.” Electoral Studies 32 (4): 729–45.10.1016/j.electstud.2013.05.029

61.

Strömbäck

Jesper.

2005. “In Search of a Standard: Four Models of Democracy and their Normative Implications for Journalism.” Journalism Studies 6 (3): 331–45.10.1080/14616700500131950

62.

Stroud

Natalie J.

2010. “Polarization and Partisan Selective Exposure.” Journal of Communication 60 (3): 556–76.10.1111/j.1460-2466.2010.01497.x

63.

Taber

Charles S.

and Milton Lodge. 2006

.” Motivated Skepticism in the Evaluation of Political Beliefs.”

American Journal of Political Science 50 (3): 755–69.10.1111/j.1540-5907.2006.00214.x

64.

Tilley

James

, and Sara B. Hobolt. 2011. "Is the government to blame? An experimental test of how partisanship shapes perceptions of performance and responsibility." The Journal of Politics 73 (2): 316–30.10.1017/S0022381611000168

65.

Tsfati

Yariv

, Natalie J. Stroud and Adi Chotiner. 2014. “Exposure to Ideological News and Perceived Opinion Climate: Testing the Media Effects Component of Spiral-of-silence in a Fragmented Media Landscape.” The International Journal of Press/Politics 19 (1): 3–23.10.1177/1940161213508206

66.

Valkenburg

Patti M.

, Holli A. Semetko and Claes H. de Vreese. 1999

.” The Effects of News Frames on Readers' Thoughts and Recall.”

Communication Research 37 (1): 42–64.

67.

Van Dalen

Arjen

, Claes H. De Vreese and Erik Albæk. 2016

.” Mediated Uncertainty: The Negative Impact of Uncertainty in Economic News on Consumer Confidence.”

Public Opinion Quarterly 81 (1): 111–30.

68.

Van Dalen, Arjen, Helle Svensson, Antonis Kalogeropoulos, Erik Albæk and Claes H. De Vreese. 2018. Economic News: Informing the Inattentive Audience. London: Routledge.

69.

Van Kempen

Hetty.

2007. “Media-Party Parallelism and its Effects: A Cross-national Comparative Study.” Political Communication 24 (3): 303–20.10.1080/10584600701471674

70.

Veenstra

Aaron S.

, Mohammed Delwar Hossain and Benjamin A. Lyons. 2014. “Partisan Media and Discussion as enhancers of the Belief Gap.” Mass Communication and Society 17 (6): 874–97.10.1080/15205436.2013.855791

71.

Zaller

John.

1992. The Nature and Origins of Mass Opinion. Cambridge: Cambridge University Press.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.22 MB