The Effects of Political Attitudes on Affective Polarization: Survey Evidence from 165 Elections1

Home , Democrats (Brazil), Republicans (Brazil)

João V. Guedes-Neto2

Abstract

How individual-level political attitudes influence affective polarization in a global scale? This paper contributes to the debate on the social distance of party affect by testing a set of hypotheses in 165 elections across the world. With a sample of over 175,000 voters, the results of multilevel mixed-effects regressions demonstrate that ideological radicalism, political knowledge, and external efficacy substantively affect how voters see the main political parties in electoral disputes taking place in 52 countries from 1996 to 2019. Satisfaction with democracy, on the other hand, is context dependent: it positively influences affective polarization only when generalized system support is low. Furthermore, I show that these correlations remain stable regardless of the operationalization of affective polarization – that is, based on two dominant parties and weighted for multiparty competition. These findings provide robust inputs to the study of party preferences and social distance in a cross-national longitudinal perspective.

Acknowledgements: I am thankful for the comments provided by Jae-Jae Spoon and Mario Fuks. I also thank Markus Wagner for kindly sharing the code used in Wagner (2020) and the Comparative Study of Electoral Systems for making its data publicly available. This research was supported in part by the University of Pittsburgh Center for Research Computing through the resources provided.

1 This paper was prepared for presentation at the 12nd Encontro da Associação Brasileira de Ciência Política taking place on October 19-23, 2020. 2 Ph.D. candidate in the Department of Political Science at the University of Pittsburgh. Contact: [email protected]. Introduction

In Brazil, presidential elections take place every four years in the month of October. When no candidate is able to reach a simple majority, the people have to choose one out of the two front- runners in a second round that often takes place in November. Since the vast majority of the population is Christian, the following month is marked by some of the main family gatherings in the year – Christmas and the New Year’s Eve. Every four years, the recency of the election makes politics one of the hot topics of these celebrations. In 2018, as the country faced one of the most polarized campaigns of the century, most newspapers offered tips on how to navigate the ‘minefield’ of discussing politics while keeping family bonds unshattered in the year to come. To avoid labelling people (Folha de São Paulo 2019) and to talk about unifying topics (Maakaroun 2018) were among the most frequent recommendations.

This anecdotal illustration proposes that, whereas most scholarship has been dedicated to study affective polarization in the American context (Iyengar et al. 2018), it is not unlike to identify this phenomenon in comparative settings. In this paper, I aggregate knowledge to this debate by asking from a cross-national time-series perspective how individual-level political attitudes influence affective polarization in the world. I benefit from the large database of the Comparative Study of Electoral

Systems (CSES), which surveyed over 175,000 voters in 165 elections from 52 countries ranging from 1996 to 2019. Relying on this data, I use multilevel hybrid mixed-effects regressions to test the effects of individual-level political attitudes on voters’ affective polarization between the two most voted parties of each election.

This is definitely not the first study of affective polarization in a cross-national setting.

Westwood et al. (2018) use a set of games with participants from Great Britain, the United States,

Belgium, and Spain to find that voters assign more resources to co-partisans and those of ally parties

1 than opponent partisan groups. Ward and Tavits (2019, 6) consider affective polarization as an independent variable and use a subsample of CSES to calculate it based on “the standard deviation of a respondent’s affect towards parties.” The rationale is that whereas it is reasonable to calculate affective polarization in two-party systems by comparing attitudes to the two main political parties, this method may not be adequate in multiparty countries.

Reiljan (2020, 380) adds complexity to this formulation considering “the average divergence of partisan affective evaluations between in-party and out-parties, weighted by the electoral size

(vote share) of the parties.” His study is based on the subsample of European countries surveyed by

CSES, identifying that voters in Central Eastern and Southern Europe have higher levels of polarization than in the United States. A similar method is adopted by Wagner (2020) to study a larger subsample of the same data. Finally, Hernández et al. (2020) use Wagner’s operationalization to find a positive relationship between this phenomenon and election salience.

The argument that the differences between two-party and multiparty systems should be taken into account in the study of affective polarization is valid. Yet, in many multiparty democracies, two parties end up controlling most electoral disputes. Consider, for instance, the

German case, where the dominance of the center-right Christlich Demokratische Union (CDU) and the center-left Sozialdemokratische Partei Deutschlands (SPD) is indisputable. This is especially relevant since the larger dataset used in this paper also spans presidential countries, where run-offs are not unlike. These scenarios force voters to choose between the two main parties. Furthermore, it focuses on the main contestants of each election, reducing the noise caused by parties that stand limited chances of influencing government selection.

To address the plural possibilities of affective polarization, I consider two operationalizations of this dependent variable. First, two-party affective polarization, following the

2 U.S. model of Iyengar et al. (2018) that assesses the social distance between the preferences for the two most voted parties of a given election. Second, the weighted affective polarization, which is based on the Comparativist approach of Wagner (2020). This relies on the average social distance between the four main contestants of a given election, weighted by the vote share of each party.

Especially in the American context, authors have identified different causes for affective polarization, including social sorting (Mason 2015, 2016, 2018b; Mason and Wronski 2018; Robison and Moskowitz 2019), the moralization of politics (Garrett and Bankert 2020), psychological traits and predispositions (Martherus et al. Forthcoming; Simas, Clifford, and Kirkland 2020), and consumption of partisan media (Lau et al. 2017).

Here, I test four additional variables: satisfaction with democracy, external efficacy, political knowledge, and ideological radicalism. Some of these variables were extensively discussed in the literature. However, this paper provides at least four contributions to the study of polarization. First,

I show that regardless of how affective polarization is operationalized, the effects of these four independent variables remain virtually the same. Second, I show that these results are robust in a cross-national longitudinal dataset that includes 165 elections from different regions of the world.

Third, I disentangle the effects of individual-level attitudes from contextual characteristics.

That is, based on multilevel hybrid mixed-effects models, it is possible to show the specific effects of individual-level political attitudes on the dependent variable of interest. Here, all four variables have significant coefficients. In brief, the more externally efficacious and ideologically radical voters are, the more polarized they will be. Furthermore, political knowledge considerably reduces polarization.

The isolated effect of satisfaction with democracy is minimal when all countries are considered. Yet, an interactive model shows the substantive effects of this political attitude in countries with

3 generalized dissatisfied populations. In these contexts, individual-level satisfaction with democracy is highly conducive to affective polarization.

The remaining parts of this paper are divided as follows. First, I discuss the two proposed operationalizations of affective polarization. This includes some practical examples that highlight the pros and cons of each formula. Second, I provide the theoretical motivations for the study of the selected independent variables – individual-level political attitudes related to system engagement. In the next section, I present the data and describe the empirical strategy of this study. It is followed by the presentation of the results, including regressions that consider the isolated effects of the explanatory variables. Following, I further disentangle the results with a regression that considers the interactive effects of individual and country-level variables. This evidences the relevance of context for satisfaction with democracy. Finally, I discuss the findings of this paper and propose some future steps in this research agenda.

Measuring Affective Polarization

One of the main recent debates in political behavior has been that between Abramowitz and

Saunders (1998, 2006, 2008) and Fiorina and his colleagues (Fiorina and Abrams 2008; Fiorina,

Abrams, and Pope 2008; Fiorina and Levendusky 2006). As these studies show, they disagreed on whether the American public was polarized. While the first group of authors affirmed that the

American electorate starkly disagreed on terms of ideological self-placement and issue preferences, the second group argued that polarization only happened among those Democratic and Republican voters who had strong partisan attitudes – a small parcel of the population.

Iyengar, Sood, and Lelkes (2012) joined this debate proposing a different operationalization of polarization. Relying on social psychology and, more specifically, intergroup conflict theory

(Tajfel 1970, 1974; Tajfel and Turner 1979), Iyengar and colleagues proposed that polarization

4 should be measured not in terms of ideology but party affect. This was based on the calculation of the individual-level social distance between preferences for the two main players of the American party system: Republicans and Democrats.

There are at least two features of this proposition that should be highlighted. First, its essentially individual-based orientation. While ideological polarization is the distance between the aggregated preferences of Republicans and Democrats (Abramowitz and Saunders 2008; Fiorina and

Abrams 2008), affective polarization is the difference between an individual’s preference toward each of these parties (Iyengar, Sood, and Lelkes 2012). This centers the debate on the individual, not aggregated partisans.

Consider Equation 1. Here, the variable two-party affective polarization is calculated as the absolute difference between voter i’s preferences for each of the two most voted political parties (P1 and P2) of a given election. Therefore, each individual should have her own index of political polarization. For the sake of comparability, I recode the resulting values to the 0-5 range.

(

Second, this measurement adequately fits the American context. The authors use longitudinal survey data to show that affective polarization has considerably increased throughout the past decades (Iyengar, Sood, and Lelkes 2012). Furthermore, in other studies, they show that different aspects of the political spectrum, as well as non-political factors, end up contributing to the increasing social distance in the country (Iyengar et al. 2018). As it has been demonstrated elsewhere, partisanship became a primary social identity which aligns (Iyengar and Krupenkin 2018), reinforces, and is reinforced by other identities (Green, Palmquist, and Schickler 2004; Mason 2018b).

5 Wagner (2020) proposes that this mechanism falls short to assess affective polarization in multiparty contests. Arguing that voters may identify with different parties and that the effects of party affect are dependent on party size, he proposes an alternatively operationalization labeled weighted affective polarization. In his words, this regards “the spread of like-dislike scores for each respondent” (Wagner 2020, 4). In Equation 2, I use the authors notations to formally explain the weighted affective polarization (WAP) of voter i. First, between parenthesis, this formula assesses the difference between voter’s i party affect toward a given party p (as in P1i in Equation 1) and the average party affect toward the remaining 3 parties. To account for the vote share (vp) of these three parties, . In Equation 2, this subtraction is weighted by party’s p vote share. For each voter, this calculates the root of the sum of the four differences obtained through the formula between parenthesis (here, I consider only the four most voted parties). The resulting variable ranges from 0 to 5.

(

Also relying on data from the Comparative Study of Electoral Systems (CSES), the author shows that there is a correlation between ideological polarization, partisanship, and this new measurement of weighted affective polarization. Hernández, Anduiza, and Rico (2020) also rely on this measurement to show that this multiparty social distance increases in aftermath of elections, thus confirming previous findings in the literature (Hansen and Kosiara-Pedersen 2017), as well as the validity of the measurement.

There are several interesting factors that deserve discussion when comparing two-party

(TPAP) and weighted affective polarization (WAP). First, considering this global dataset with 165 6 elections, WAP’s (2.39) average is slightly higher than TPAP’s (1.95). This shows that whereas one would arguably expect that polarization were higher among the two most voted parties, the consideration of more parties and the weighting for vote share actually increases the observed affective polarization. This is evident in the histogram of these two variables (Figure 1). TPAP is left-skewed with most values concentrated between 0 and 1. Whereas WAP also has a large concentration of values at 0 (i.e., those voters which have no preference at all in the party system), the remaining portion of the values resembles a normal distribution reaching its peak between 2 and

3. Still, their correlation is considerably high: 0.82.

Figure 1. Histogram of dependent variables

Now, consider Table 1. It shows the most and least affectively polarized elections in the dataset (sorted by weighted affective polarization). As expected from the previous discussion, TPAP is lower than WAP in every case. The most polarized case in both measurements is Kenya 2013. In that year, voters selected the president and the legislators of the upper and lower houses – the first 7 election since the new constitution of 2010 (Harbeson 2014). As the numbers show, this was a very contested dispute, when the center-right Uhuru Kenyatta (back then, a member of the Jubilee Party) got 50.5% of the votes against the center-left Raila Odinga (Orange Democratic Movement), who was preferred by 43.7% of the electorate.

Both leaders were highly popular politicians. Odinga was the prime minister and Kenyatta his deputy since 2008. Furthermore, while Kenyatta was the son of Kenya’s first president Jomo

Kenyatta (1963-1978), Odinga’s father, Jaramogi Oginga Odinga, was the vice-president of Kenyatta from 1964 to 1966. The partnership was dissolved in 1966, when they disagreed over the international allegiances of Kenya – if with China or the United States (Hornsby 2012).

Table 1. The most and the least affectively polarized elections in the world

Country Year N Two-Party Aff. Pol. Weighted Aff. Pol. Kenya 2013 406 3.21 3.42 Turkey 2015 805 2.85 3.33 Albania 2005 893 3.12 3.25 Bulgaria 2001 958 2.48 3.14 Turkey 2018 767 2.92 3.14 … … … … … Hong Kong 2004 202 1.36 1.42 Taiwan 1996 430 1.11 1.37 Hong Kong 1998 463 1.13 1.32 Philippines 2016 924 0.81 1.14 Hong Kong 2000 319 0.92 1.13

The least polarized elections were those in Hong Kong in 2000, 1998, and 2004, respectively.

It is no surprise given its low levels of democracy – as it also occurs in Taiwan 1996, part of the same list. In that year, only 20 out of 60 seats of the Legislative Council were directly elected by the population (Shiu‐hing and Wing‐yat 2002). With a turnout of 43.6% of eligible voters, the main dispute occurred between the liberal Democratic Party (31.7% of the votes) and the pro-Beijing

8 Democratic Alliance for the Betterment of Hong Kong (28.4%). None of the following eight most voted parties received more than one-tenth of the votes.

In each of these cases, two actors were dominant in the political spectrum. This would lead to the expectation that a measurement of two-party affective polarization would outperform a multiparty operationalization. Yet, since Wagner’s (2020) model also weights party affect and social distance by vote share, WAP adequately captures the main competition in each country.

Now, consider the Brazilian case (Figure 2). Whereas there are over 30 political parties in the country, presidential disputes were dominated by the center-left Partido dos Trabalhadores (PT) and the center-right Partido da Social Democracia Brasileira (PSDB) from 1994 to 2014 (Kingstone and

Power 2017). Regardless of whether the main polarization occurred between PT and PSDB (Ribeiro,

Carreirão, and Borba 2011), or just around PT (Samuels and Zucco 2018), the presidential contest of

2018 significantly changed Brazilian politics. In that year, PSDB did not reach the run-off for the first time since 1994 and PT lost to the right-wing outsider Jair Bolsonaro (Hunter and Power 2019).

9 Figure 2. Two measurements of affective polarization in Brazil

Note: In 2002, the parties were: Partido dos Trabalhadores (PT), Partido da Social Democracia Brasileira (PSDB), Partido da Frente Liberal (PFL) and Partido do Movimento Democrático Brasileiro (PMDB); in 2006, they were: PMDB, PT, PSDB, and PFL; in 2010, they were: PT, PMDB, PSDB, and Partido da República (PR); in 2014, they were: PT, PSDB, PMDB, and Partido Progressista (PP); and in 2018, they were: Partido Social Liberal (PSL), PT, PSDB, and Partido Social Democrático (PSD). Only the two first parties of each year were used in the calculation of two-party affective polarization.

Figure 2 helps to understand Brazilian politics, as well as some pros and cons of each operationalization of affective polarization. The first highlight of the graph is the comparison between two measurements. The only year when they starkly differ is 2010, when, after two terms in power, Luiz Inácio Lula da Silva (PT) elected his successor, Dilma Rousseff (PT), against the center- right PSDB. In that year, whereas weighted affective polarization significantly increased, two-party affective polarization slightly decreased. This is explained by the parties used in each year, as well as the electoral context.

10 CSES ranks parties based on the votes obtained in the congressional election. Yet, for some countries, as it is the case of Brazil, these are not the main parties in the presidential elections. The decrease in both measurements from 2002 to 2006 is partially explained by this. PSDB, which was the second most voted party for the legislative house in 2002, came in third in 2006. This means that the center party PMDB was contrasted to PT in the two-party affective polarization of 2006.

Furthermore, this provides stronger weight for PMDB than for PSDB in the measurement of weighted affective polarization, thus driving it down as well.

In 2010, again, only PT and PMDB were considered for the measurement of two-party affective polarization – a potentially misleading strategy, given that PSDB was potentially more polarized against PT and possibly more relevant in terms of voter preferences. This is especially relevant in the highly contested power transition of 2010, when Lula could not re-run for president.

When PSDB is considered in the calculation of weighted affective polarization, the country average soars. The new rise is seen on 2018 when, as in the 2002 peak, the ruling party lost the presidential dispute.

The natural empirical strategy for the student of Brazilian politics interested in using the two-party affective polarization measurement would have been to replace PMDB by PSDB in the years between 2006 and 2014. Yet, this may not be ideal for a cross-national study that includes 165 elections. In this type of analyses, adopting special rules case-by-case could lead to arbitrary decisions. Following the ranking stablished by CSES based on congressional disputes seems a fair approach that should be valid for most countries.

This problem is partially addressed by the weighted approach. However, this is far from perfect as well. In the Brazilian case, it would still place great power on actors like PR, PP, and PSD that have a minimal number of partisans. Additionally, PMDB, which has significant legislative

11 power and a relatively large membership basis, would probably be downplayed by other Brazilianists interested in partisanship and social distance (Ribeiro, Carreirão, and Borba 2011; Samuels and

Zucco 2018). Overall, no solution is perfect. Still, at least in a global scale, weighted affective polarization has been able to capture starker levels of partisan divide.

Predicting Affective Polarization

The argument I put forward in this section is that affective polarization, regardless of the operationalization, is a function of someone’s engagement with the political system. Those which are the most engaged should have higher levels of social distance exactly. This happens because they are able to differentiate the existing political parties, they have firm ideological positions based on the country’s political spectrum, and they believe their vote matters.

Consider ideology, one of the most studied sources of polarization. Using an experimental approach, Rogowski and Sutherland (2016) demonstrate that when voters perceive political elites to be ideologically polarized, their level of affective polarization increases. This finding derives from a number of studies that confirm the relevance of elite cues on public opinion (Levendusky 2010;

Zaller 1991, 1992).

The mechanism behind this linkage is the influence of elites over voter’s ideology, which affects how individuals perceive their political in and out-groups. Ultimately, it leads to higher levels of affective polarization (Banda and Cluverius 2018; Webster and Abramowitz 2017). Lelkes

(Forthcoming) goes beyond to argue that voters polarize more along ideological lines than partisan preferences. This holds when ideology is measured as a social identity (Mason 2018a) and is especially true in the current American context where polarization is the norm (Ansolabehere and

Iyengar 1995; Hetherington 2001). Similar findings are identified elsewhere, for instance, in Latin

12 America (Béjar, Moraes, and López-Cariboni 2020; Singer 2016). Following the existing literature, I hypothesize that:

H1: The more ideologically radical a voter is, the higher her level off affective polarization will be.

Yet, voters not necessarily correctly understand the ideological placement of political parties.

Ward and Tavits (2019) use a sub-sample of the cross-national data proposed for this paper to affirm that those voters who hold higher levels of affective polarization also perceive parties to be more extreme. It is possible to suggest an endogenous relation, as this perception of extreme ideologies could be a source of affective polarization. That is, as voters see parties as more ideologically distant than they actually are, their attitudes toward different parties also become more divergent.

One of the sources of these radicalized perceptions is biased information. At least in the

American context, when voters follow politics through the lens biased media, their levels of partisan hostility go up (Lelkes, Sood, and Iyengar 2017), ultimately influencing affective polarization (Lau et al. 2017). These findings are confirmed in Europe as well, where biased media increases voters’ polarization around specific issues, e.g., attitudes toward the European Union (Wojcieszak, Azrout, and de Vreese 2018). Overall, biased knowledge creates a greater social distance within the electorate.

Even though it is possible that, given lack of political knowledge, voters will position parties closer to each other, electoral contexts tend to increase the salience of party differences (Hansen and

Kosiara-Pedersen 2017), thus pushing them apart. Following this logic and the expectation that this lack of accuracy will boost polarization, I hypothesize that:

H2: The less politically knowledgeable a voter is, the higher her level off affective polarization will be.

13 Political efficacy, defined by Campbell, Gurin, and Miller (1954) as “the feeling that individual political action does have, or can have, an impact upon the political process,” is a relevant element that characterizes system support. Kitchelt and McGann (1997) use the cases of Austria and

Italy to argue that voters look for anti-mainstream alternatives when they are disenchanted with the existing elites. Likewise, in the 1989 Brazilian and the 1990 Peruvian elections, outsiders ran under the platform that the existing political actors were not responsive to the population (Schedler 1996).

The emergence of these challengers is potentially linked to a disillusion with traditional politicians or their political parties. Put differently, those who believe that their vote or voice has less influence over the political process tend to be more politically moderated or independent from the political mainstream (Hetherington 2008). This suggests lower levels of polarization among those who hold lower levels of external efficacy – at least when only the most voted parties of an election are considered. That is, as voters perceive that their political engagement is irrelevant, they should not hold strong partisan preferences.

Perception of political relevance drives voters to pick a side (Robison 2017). There is robust evidence for the effects of politicization on social distance in different countries, including the

United States (Banda and Cluverius 2018), Germany (Simon et al. 2019), and the United Kingdom

(Hobolt, Leeper, and Tilley 2020). Following their findings, I argue that voters affectively polarize in the partisan arena when they believe that politicians are adequately representing their wishes, thus justifying stronger preferences for one political party instead of another. Furthermore, as already argued, this should in both the individual and electorate-levels.

H3: The more externally efficacious a voter is, the higher her level off affective polarization will be.

A more direct measurement of system support is satisfaction with democracy. Voters tend to express more positive views toward democratic institutions when they feel represented by it

14 (Anderson 1998; Justwan et al. 2018). When individuals believe that governments under different party labels have failed to deliver promises or to solve social problems, public trust toward democracy shrinks. In these cases, they support anti-mainstream challengers, as the case of

Eurosceptical parties (Abts, Heerwegh, and Swyngedouw 2009; Verney 2015) or change their voting habits. The latter is characterized either by reduced voting turnout due to lack of feasible alternatives

(Cappella and Jamieson 1997) or increased turnout dedicated to protest voting (Ezrow and

Xezonakis 2016).

Overall, the literature presents a link between satisfaction with democracy and attitudes toward the political mainstream. This should reflect on voters’ affect toward parties. That is, higher social distance should be identified only among the parcel of the electorate that is satisfied with democracy. Otherwise, voters should equally dislike or be indifferent toward the most voted parties.

This proposition is in line with previous discussions of this paper, as well as with the findings of

Klar, Krupnikov, and Ryan (2018). Thus, I hypothesize that:

H4: The more satisfied with democracy a voter is, the higher her level off affective polarization will be.

Empirical Strategy

I test these hypotheses relying on data from the five modules of Comparative Study of

Electoral Systems (CSES). This collaborative organization has been conducting nationally representative surveys around the world since 1996. This means that the data used in this paper contains 24 time points spanning elections from 1996 to 2019. Furthermore, it includes 52 countries, resulting in a total of 165 country-years (or elections). After excluding those observations that have missing data for any of the variables of interest, the final sample yielded a total of 177,331 voters.

The descriptive statistics are presented in Table 2.

15 Table 2. Descriptive statistics

Variable N Mean Std. Dev. Minimum Maximum Two-Way Affective Polarization 177,331 1.95 1.49 0.00 5.00 Weighted Affective Polarization 177,331 2.39 1.15 0.00 5.14 Satisfaction with Democracy 177,331 1.60 0.82 0.00 3.00 External Efficacy 177,331 2.97 1.18 0.00 4.00 Political Knowledge 177,331 13.18 2.72 0.00 17.00 Ideological Radicalism 177,331 1.95 1.66 0.00 5.00 Male 177,331 0.51 0.50 0.00 1.00 Education 177,331 4.14 1.83 0.00 7.00 Age 177,331 46.80 16.79 18 102

For the sake of comparability, CSES always fields surveys in the period surrounding a national-level election. Recent research shows that campaign messages positively influence affective polarization (Hansen and Kosiara-Pedersen 2017). Thus, the data at hand should represent the highest levels of polarization of the selected countries.

Dependent Variables

One of the questions asked in every wave of the CSES survey closely resembles the wording used in the Americanist literature (Iyengar et al. 2018) to assess affective polarization. This is the following:

“I’d like to know what you think about each of our political parties. After I read the name of a political party, please rate it on a scale from 0 to 10, where 0 means you strongly dislike that party and 10 means that you strongly like that party. If I come to a party you haven’t heard of or you feel you do not know enough about, just say so.”

As the question highlights, voters are free to shirk. To reduce this type of missing responses and to focus solely on the main disputes within each country, this paper considers two alternative operationalizations of affective polarization. First, following Equation 1, the two-party affective polarization – calculated as the absolute different between the response for the most voted party and the second most voted party – leads to a 0-5 variable with an average of 1.95 and a standard deviation of 1.49. Second, the weighted affective polarization relies on the four most voted parties

16 and, based on Equation 2, yields an average of 2.39 and a standard deviation of 1.15 in the same 0-5 range.

Independent Variables

The first hypothesis (H1) regards self-placement in a unidimensional ideological spectrum.

As in other standard surveys, voters were asked to place themselves in a left-right scale where 0 means left and 10 means right. Following Equation 3, I identify ideological radicalism (IR) of individual i by calculating the absolute difference between self-placement in the 0-10 left-right (Ii) and 5 (the median point), thus folding the original variable. This leads to a 0-5 scale, where 0 means centrist and 5 is very radical (i.e., those who responded either 0 or 10).

(3)

H2 regards political knowledge (PKi). As previously argued, this is a proxy for correct voting or the proper assessment of a party’s ideological placement. This is calculated having in mind the two most voted parties of a given election (i.e., parties a and b). Consider Equation 4. First, I calculate the absolute different between voter’s i assessment of the left-right position of party a (IAa) and the expert assessment of the same party’s position (EAa). This is added to the same assessment of the second most voted party of this election (party b), resulting in the overall distance between individual and expert assessments for the two most parties of a given election. This goes from 0 to

17. Since the measurement should indicate political knowledge, not its absence, I subtract this value from 17. This means that the higher the value, the higher the accuracy of the party placement is.

(4) The following hypothesis regard political (or external) efficacy. Respondents were asked the question presented following paragraph. Responses (PEi) were adjusted to range from 0 to 4.

17 “Some people say that no matter who people vote for, it won’t make any difference to what happens. Others say that who people vote for can make a big different to what happens. Using the scale on this card, (where 1 means that voting won’t make any difference to what happens and 5 means that voting can make a big difference), where would you place yourself?”

The last hypothesis refers to satisfaction with democracy. The survey question, again, follows the standard wording in the literature: “On the whole, are you very satisfied, fairly satisfied, not very satisfied, or not at all satisfied with the way democracy works in [COUNTRY]?” Responses (Di) were recoded to follow the ordinary range of 0 to 3.

The models also include control variables in the individual and context-levels. To control for context, I calculate the country-year average of each individual-level attitude described in this section. In the individual level, I rely on variables commonly found in the literature. They are age, level of education, and sex.

Models

As for the model, Steenbergen and Jones (2002) highlight that standard OLS models may ignore relevant aspects of multilevel studies, as the one proposed in this paper. In the own words, they may lead to “incorrect standard errors and inflated Type 1 errors” (Steenbergen and Jones

2002, 219). It is especially relevant given the small number of country-years which are considered in the model. Therefore, I follow their suggestion adopting a multilevel mixed-effects approach, which shall account for micro- and macro-level variations. In this case, the two levels are the individual responses and the country-year means. All the regressions are nested within country-year and with standard errors clustered at the same level.

Furthermore, I use the strategy proposed by Fairbrother (2014) to disentangle the specific individual-level effects regardless of context. First, this method regards demeaning the individual-

18 level variable, that is, subtracting the country-level average from the individual-level value. Second, as already mentioned, including both the demeaned individual-level variable (the within-subject effects) and the country-level variable in the model (the between-subject effects). This is often referred as the multilevel hybrid mixed-effects model. In this paper, I am interested in the within- subject effects of this calculation. This approach is relevant, since it allows identifying the role of individual political attitudes regardless of context.

This paper uses the same approach and terminology adopted by Rabe-Hesketh and Skrondal

(2008), which results in the notation expressed in Equation 5. In the model, is the individual- level intercept, is a matrix with control variables for each individual, and those other variables included in the first line regard the demeaned individual-level variables described in the previous section. In the second level, the variables reflect country-year variations, thus regarding between- effects. is the fixed intercept and is the residual or random intercept. The remaining variables reflect country-year means of the selected variables. The final model also includes country and year fixed effects.

(5)

For the sake of comparability, I also run limited forms of this model. This considers individual-level variables only (models A1 and A5), the inclusion of country-level variables (models

A2 and A6), the shift to demeaned individual-variables (models A3 and A7), and, finally, the inclusion of individual-level control variables (models A4 and A8). All the results are presented in the appendix (Appendix 1 and Appendix 2) and, despite the different operationalizations, the coefficients are virtually the same.

19 Results

I present the coefficients of the full models in Table 3. These results are based on Equation

5 and are also demonstrated in models A4 and A8 of the appendix (Appendix 1 and Appendix 2).

The first analysis regards the overall fit of the models. As earlier said, the results of simplified models are presented in the appendix. The use of demeaned individual-level variables and the inclusion of between effects and control variables increase the performance of the model, as shown in the variation of the log likelihood and AIC. The country-year random effects is significant (p <

0.001) in both final models, even though residuals are only significant when two-party affective polarization is considered. The remaining levels of significance and coefficient sizes and directions are virtually the same across models.

Table 3. The effects of political attitudes on affective polarization

Two-Party Affective Polarization Weighted Affective Polarization β p-value β p-value Within Effects Satisfaction with Democracy 0.051*** (0.000) 0.046*** (0.000) External Efficacy 0.169*** (0.000) 0.156*** (0.000) Political Knowledge -0.050*** (0.000) -0.051*** (0.000) Ideological Radicalism 0.209*** (0.000) 0.192*** (0.000) Between Effects Satisfaction with Democracy 0.060 (0.634) -0.031 (0.752) External Efficacy 0.023 (0.859) 0.122 (0.271) Political Knowledge -0.014 (0.694) 0.077** (0.005) Ideological Radicalism 0.482*** (0.000) 0.501*** (0.000) Controls Male -0.074*** (0.000) -0.056*** (0.000) Education -0.015** (0.002) -0.007^ (0.077) Age 0.004*** (0.000) 0.005*** (0.000) Constant 0.925^ (0.094) -0.085 (0.851) Random Effects Country-year -0.811*** (0.000) -1.129*** (0.000) Residuals 0.283*** (0.000) 0.012 (0.437)

20 Observations 177,331 177,331 Country-years 165 165 Log Likelihood -302,221.348 -254,205.083 Akaike’s Inf. Crit. 604,470.696 508,438.166 Notes: Multilevel hybrid random-effects regressions. All models are nested by country-years with standard errors clustered by standard errors. Coefficients are not standardized. The models differ in the dependent variable, which are the two-party affective polarization and weighted affective polarization. P-values: ^ p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001.

All independent variables of interest are statistically significant (p < 0.001) and follow the hypothesized direction, thus confirming that the more engaged with the system a voter is, the more she will hold affectively polarized attitudes not matter if these are based on the two or four most voted parties. Whereas this is not central to this paper, the only country-level variable that remains significant (p < 0.001) regardless of the dependent variable is ideological radicalism. This means that countries where voters hold more extreme ideological positions are also those where individual-level affective polarization tend to be higher.

To facilitate the analysis of the within-subject effects, I plot the marginal effects in Figure 3

(TPAP) and Figure 4 (WAP). In both cases, the effects of individual-level satisfaction with democracy is minimal. This weakens support for H4. Yet, the other three variables substantively influence affective polarization.

21 Figure 3. Predicting Two-Party Affective Polarization

First, consider ideological radicalism (H1). Ceteris paribus, the average two-party affective polarization among centrists (IRi=0) is 1.95. Among those with the highest levels of ideological radicalism (IRi=0), this raises to 2.99 – a rate of change of 53.3%. A similar trend is identified when the dependent variable is weighted affective polarization, which ranges from 2.37 to 3.33 if centrists are compared to the most radical voters (a rate of change of 40.5%).

H2 regards political knowledge, ranging from 0 (lowest accuracy of party ideological placement) to 17 (when voters place the two most voted parties exactly as experts have done). When the dependent variable is TPAP, accuracy reduces polarization from 1.95 to 1.10 (-43.6%).

Considering WAP, the variation is from 2.37 to 1.50 (-36.7%).

22 Finally, the graphs show the effects of individual-level political efficacy on affective polarization (H3). On average, those who are the least efficacious affectively polarize at a level of

1.95 (two-party measurement) compared to the most efficacious at 2.63 (34.9%). When WAP is taken into account, the predicted coefficient ranges from 2.37 to 3.00 (26.6%).

Figure 4. Predicting Weighted Affective Polarization

Democracy and Polarization

The weak substantive effects of satisfaction with democracy go against H5, which proposed that the higher the satisfaction, the higher affective polarization should be. The results obtained by

Luttig (2017, 2018) may explain this finding. The author demonstrates that, at least within the

American electorate, those voters who hold more authoritarian attitudes tend to be more affectively

23 polarized. If satisfaction with democracy actually measures support for this regime type or the degree of democratic attitudes (for a similar discussion, see Anderson 1998; Justwan et al. 2018), this could alter the proposed link between this variable and affective polarization. In fact, this goes in line with the findings of Ezrow and Xezonakis (2016), who rely on a longitudinal analysis of 12 democracies to show that when citizens’ satisfaction with democracy increases, voter turnout shrinks.

The explanation behind this link regards the interaction between individual and contextual variables. In countries with well-established democracies, being satisfied with the regime means that voters should be reasonably happy regardless of the ruling party. Centrism makes parties resemble each other and work in closer cooperation, thus resulting in lower levels of change after power alternation (Dostal 2017; Lochocki 2016). In these contexts, polarization will come from those who are dissatisfied with the current democracy (Grimm 2015). Thus, when satisfaction with democracy is generalized, individual-level system support should lead to lower levels of affective polarization.

Yet, when general levels of satisfaction with democracy are lower, the logic proposed in H4 should still apply.

To test this proposition, I run an additional model for each dependent variable interacting within and between effects, that is, individual-level variables and country averages. The coefficients are displayed in Model A5 (Appendix 1) and Model A10 (Appendix 2). In the regression considering two-party affective polarization (TPAP), the p-value of the interactive term is below 0.05. When the dependent variable is weighted affective polarization (WAP), this raises to 0.10. These results confirm my updated expectation as shown in the marginal effects plotted in Figure 5. The graphs demonstrate the effect of individual-level satisfaction with democracy in contexts where the country- average is the lowest possible (0.54), median (1.66), and highest possible (2.30). The differences are stark. 24 When overall satisfaction with democracy is low, individual-level attitudes either decrease

(TPAP) or slightly increase (WAP). Yet, when we consider mid and especially high levels of generalized satisfaction with democracy, individual-level system support substantively influences affective polarization in a positive direction regardless of the dependent variable. This is the only interactive effect that holds at reasonable levels of statistical significance in both models.

Figure 5. Contextual effects of satisfaction with democracy

Notes: These marginal effects are based on models A5 (Appendix 1) and A10 (Appendix 2). Discussion

This paper aims at a set of contributions to the existing debates on social distance in partisan preferences. First, a comparison between two measurements of affective polarization – on based on the two-party debate centered mostly in the United States (Iyengar, Sood, and Lelkes 2012) and the other relying on multiparty contests (Wagner 2020). Besides discussing the theoretical frameworks

25 that motivated each of these operationalizations, I provide examples of how similar (or different) they are depending on different cases.

Whereas their correlation is considerably high, weighted affective polarization is able to capture higher scores than two-party affective polarization. Even though the inclusion of more parties would probably suggest otherwise, weighting for vote share accounts for distortions caused by small political actors. This leads to WAP scores being often higher than TPAP. Yet, both share the same problem when considering multi-country studies: the arbitrary selection of parties.

The case of Brazil is illustrative. This shows that these measurements deviate from each other in 2010 and, whereas WAP is probably more accurate, it still falls short given the typical selection of parties adopted when using the CSES dataset – probably the most comprehensible effort to collect this type of survey data worldwide. That is, the most voted parties in congressional elections or even their vote shares are not necessarily the best approach to identify the most relevant players in an electoral contest. This type of problems can only be addressed through studies that consider a smaller set of countries, thus allowing lengthier discussion on the selection strategy in each case.

The second contribution is the assessment of how different forms of system engagement influence affective polarization. Besides showing that the results are virtually the same regardless of the dependent variable (TPAP or WAP), I confirm that ideological radicalism and external efficacy are positively related to affective polarization, while political knowledge reduces it. This evidences that those citizens who are happier with elections and whose tend to hold more extreme ideological preferences tend to be those who have a greater social distance in the partisan spectrum. Yet, while ideological radicalism increases polarization, being aware of parties’ ideological placement reduces

26 social distance – implying that lack of political knowledge may suggest perceiving higher levels of elite radicalism than actually exist.

Satisfaction with democracy, on the other hand, is context dependent. Its effect in countries with high levels of regime support are either minimal or negative. However, when generalized satisfaction with democracy is high or at least medium, individual-level attitudes matter substantively more and in a positive direction. This contributes to the existing literature, which rarely includes comparisons between countries that are considerably different in terms of democratic performance.

It is true that many of these independent variables may be contaminated with endogeneity. It would be possible to propose that, for instance, affective polarization causes ideological radicalism.

Yet, as previously argued in this paper, there is at least a theoretically reasonable argument to justify the causal direction proposed in this paper. Furthermore, there is merit in showing that this trend holds even in such a large number of cases – 165 elections. New studies should consider different methodological approaches to verify the size of the effect in each of the possible directions.

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32 Appendix

Appendix 1. Mixed-effects regressions predicting two-party affective polarization

(A5) (A1) (A2) (A3) (A4) Individual Level Satisfaction with Democracy 0.050*** 0.050*** - - - External Efficacy 0.169*** 0.169*** - - - Political Knowledge -0.052*** -0.052*** - - - Ideological Radicalism 0.211*** 0.211*** - - - Individual Level (Demeaned) Satisfaction with Democracy - - 0.050*** 0.051*** 0.176** External Efficacy - - 0.169*** 0.169*** 0.082 Political Knowledge - - -0.052*** -0.050*** -0.105 Ideological Radicalism - - 0.211*** 0.209*** 0.240*** Country Level Satisfaction with Democracy - 0.010 0.060 0.060 0.060 External Efficacy - -0.146 0.023 0.023 0.023 Political Knowledge - 0.043 -0.009 -0.014 -0.014 Ideological Radicalism - 0.272* 0.483*** 0.482*** 0.482*** Interaction: Individual Demeaned * Country Satisfaction with Democracy (i*c) -0.080* External Efficacy (i*c) 0.030 Political Knowledge (i*c) 0.004 Ideological Radicalism (i*c) -0.015 Controls Male - - - -0.074*** -0.074*** Education - - - -0.015** -0.015**

1 Age - - - 0.004*** 0.004*** Constant 1.644*** 0.961^ 0.961^ 0.925^ 0.926^ Random Effects Country-year -0.783*** -0.802*** -0.802*** -0.811*** -0.811*** Residuals 0.285*** 0.285*** 0.285*** 0.283*** 0.283*** Observations 177,331 177,331 177,331 177,331 177,331 Country-years 165 165 165 165 165 Log Likelihood -302,607.722 -302,604.542 -302,604.542 -302,221.348 -302,183.990 Akaike’s Inf. Crit. 605,229.443 605,231.084 605,231.084 604,470.696 604,403.981 Notes: Multilevel hybrid random-effects regressions. All models are nested by country-years with standard errors clustered by standard errors. Coefficients are not standardized. The dependent variable is two-party affective polarization. P-values: ^ p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001.

Appendix 2. Mixed-effects regressions predicting weighted affective polarization

(A10) (A6) (A7) (A8) (A9) Individual Level Satisfaction with Democracy 0.045*** 0.045*** - - - External Efficacy 0.157*** 0.157*** - - - Political Knowledge -0.052*** -0.052*** - - - Ideological Radicalism 0.194*** 0.194*** - - - Individual Level (Demeaned) Satisfaction with Democracy - - 0.045*** 0.046*** 0.121* External Efficacy - - 0.157*** 0.156*** 0.052 Political Knowledge - - -0.052*** -0.051*** -0.156* Ideological Radicalism - - 0.194*** 0.192*** 0.236*** Country Level Satisfaction with Democracy - -0.074 -0.029 -0.031 -0.031 External Efficacy - -0.035 0.122 0.122 0.122 Political Knowledge - 0.135*** 0.083** 0.077** 0.077** Ideological Radicalism - 0.305** 0.499*** 0.501*** 0.501*** Interaction: Individual Demeaned * Country Satisfaction with Democracy (i*c) -0.0491 External Efficacy (i*c) 0.036 Political Knowledge (i*c) 0.008^ Ideological Radicalism (i*c) -0.022 Controls Male - - - -0.056*** -0.057*** Education - - - -0.007^ -0.007^ Age - - - 0.005*** 0.005*** 3 Constant 2.141*** -0.008 -0.008 -0.085 -0.082 Random Effects Country-year -1.040*** -1.110*** -1.110*** -1.129*** -1.129*** Residuals 0.016 0.016 0.016 0.012 0.012 Observations 177,331 177,331 177,331 177,331 177,331 Groups 165 165 165 165 165 Log Likelihood -254,787.496 -254,775.899 -254,775.899 -254,205.083 -254,110.554 Akaike’s Inf. Crit. 509,588.991 509,573.799 509,573.799 508,438.166 508,257.108 Notes: Multilevel hybrid random-effects regressions. All models are nested by country-years with standard errors clustered by standard errors. Coefficients are not standardized. The dependent variable is weighted affective polarization. P-values: ^ p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001. 1 The p-value of this coefficient is 0.100.