Political polarization and its effects on turnout

Data and operationalization

We use data from the Comparative Study of Electoral Systems (CSES), which is a collaborative research programme of election study teams from around the world. Participating countries include a common module of survey questions in their post-election studies (see http://www.cses.org/). Specifically, this article uses data from waves 3 and 4 – that is, data collected between 2006 and 2011 (Module 3), and 2011 and 2016 (Module 4).Footnote ¹ Single case studies have dominated past research, most of which focus on the USA (e.g. Abramowitz Reference Abramowitz2010; Abramowitz and Stone Reference Abramowitz and Stone2006; Enders and Armaly Reference Enders and Armaly2019; Fiorina et al. Reference Fiorina, Abrams and Pope2005; Hetherington Reference Hetherington, Nivola and Brady2008, Reference Hetherington2009; Levendusky and Malhotra Reference Levendusky and Malhotra2016; Rogowski Reference Rogowski2014; Westfall et al. Reference Westfall, van Boven, Chambers and Judd2015; for other single case studies, see, e.g. Ford Reference Ford, Hopkins and Sides2015; Munzert and Bauer Reference Munzert and Bauer2013; Wolf and Roßteutscher Reference Wolf and Roßteutscher2013). Some studies use aggregate data (Dalton Reference Dalton2008; Siaroff and Merer Reference Siaroff and Merer2002; Steiner and Martin Reference Steiner and Martin2012) and the few comparative studies based on individual data are limited in scope, as they focus exclusively on (Western) European countries (Kleiner Reference Kleiner2016, Reference Kleiner2018; Lachat Reference Lachat2008; Moral Reference Moral2017). Hence, this is one of the first truly comparative accounts of the impact of polarization on voter turnout worldwide (most recently Dassonneville and Çakır Reference Dassonneville, Çakır and Dalton2021; see also Lupu Reference Lupu2015 or Ezrow et al. Reference Ezrow, Tavits and Homola2014, but both with a focus on partisanship). The original data set contains information on 144,381 respondents from 95 election studies in 47 countries; 11 additional studies were excluded as their questionnaires lacked decisive questions.Footnote ² Belarus (2008) was also dropped because most candidates were not affiliated with any party. On the individual level, we excluded respondents with missing values on the dependent or core predictor variables. As a result, the analyses are based on 88,397 individuals from 84 election studies in 43 countries (see Table A1 in the Supplementary Material for final case numbers per country and election year included in this study).Footnote ³

The dependent variable is a dummy that differentiates between respondents who self-reported either participating in (1) or abstaining from (0) the election. Our main predictor variables are the two measures of polarization: party-system polarization and individual perceptions of party differences. There are several options for measuring polarization.Footnote ⁴ We decided in favour of the formula developed by Russell Dalton (Reference Dalton2008; similar Ezrow Reference Ezrow2008; Lupu Reference Lupu2015). First, it integrates parties' positions on the left–right continuum and accounts for party size (see also Esteban and Ray Reference Esteban and Ray1994; Van der Meer et al. Reference Van der Meer, van Deth and Scheepers2009). We believe that it makes a difference in terms of the degree of polarization whether parties at the extreme poles of the ideological spectrum are tiny or large (Alvarez and Nagler Reference Alvarez and Nagler2004; Dassonneville and Çakır Reference Dassonneville, Çakır and Dalton2021; Ezrow and Xezonakis Reference Ezrow and Xezonakis2011). Vice versa, the size of centre parties is also crucial. If centre parties encompass a majority of the popular vote, party systems are less polarized even if there are niche parties at the extremes (see also Lupu Reference Lupu2015; Moral Reference Moral2017). By contrast, if extreme parties are large and centre parties are negligible in terms of voter support, a party system is heavily polarized. Second, unlike measures based on the mean absolute differences between parties, this model does not suffer from biases originating from differences in the absolute number of parties (Schmitt and Franzmann Reference Schmitt and Franzmann2017). Finally, it accounts for parties in the ideological centre of a party system, in contrast to measures based on the ideological range of a party system or the mere number of – subjectively defined – extreme parties (e.g. Abedi Reference Abedi2002; Crepaz Reference Crepaz1990).Footnote ⁵ Among the marginally different calculation formulas available that acknowledge dispersion and size, all produce highly similar results (see Moral Reference Moral2017 for a comparison). The formula thus is as follows:Footnote ⁶

$$\eqalign{{\rm Party\ System\ Polarization} = & {\rm \;SQRT} \bigg \{{\sum ( {{\rm party\ vote\ shar}{\rm e}_i} ) } \cr & \quad\times {( [ {{\rm party}\;L/R\;{\rm scor}{\rm e}_i} } \cr & \quad{{ { {- {\rm \;weighted\ party\ system\ average}\;L/R\;{\rm score}} ] /5} ) }^2} \bigg \} , \; } $$

where i represents individual parties.

The measure of individual perceptions of party differentiation builds on individual perceptions of party positions. All respondents were asked to position their country's parties on the left–right continuum. We thus calculate an individual score of perceived party differences for each respondent. Although there are more dimensions that organize party competition,Footnote ⁷ the left–right measure is still the most general measure of party conflict that encompasses, at least partially, other potentially important divides (see, e.g. Freire Reference Freire2008; Fuchs and Klingemann Reference Fuchs, Klingemann and Jennings1990; Huber Reference Huber1989; Knutsen Reference Knutsen1995; Roßteutscher and Scherer Reference Roßteutscher, Scherer, Weßels, Schoen and Gabriel2013). Moreover, it is the only dimension for which expert and individual party ratings are available. Respondents could not always position every party, and there were particularly high rates of missing values for the smaller parties. Hence, we consider only parties with a vote share of 4% or more.Footnote ⁸ Note that we decided against imputing mean values because we assume that parties that individuals cannot rate are also not part of their choice set.Footnote ⁹ All respondents with only one or no single party estimate were dropped from the analyses. Since there is no measure of perceived party size available, we used the relative vote shares of the positioned parties. As CSES surveys are conducted directly after an election, we assume that respondents possess a fairly correct idea about the relative strength of their national parties. That said, using an index formula with unweighted party positions yielded identical results (see section on robustness below).Footnote ¹⁰ Hence our findings are independent of whether this assumption holds or not.

As we argue above, party-system polarization is essentially different from individual perceptions of party differences. Due to a lack of independent sources, scholars often use the aggregated individual perceptions as a measure of system polarization.Footnote ¹¹ We believe this is a poor idea because individuals draw on different, hardly comparable, sources to estimate their parties (Lupu Reference Lupu2015: 338; Rogowski Reference Rogowski2014: 481). Country means only reflect a valid measure of system polarization if most individuals share a minimal understanding of party positions. If, however, some believe that party differences are large, while others think there are no differences, the mean value hardly reflects reality. Such differences emerge naturally if individuals' perceptions of party differences are based on the very diverse and idiosyncratic sources spelled out above. Moreover, if the perception of party differences is systematically related to other theoretically relevant variables, aggregation may simply produce a distorted measure. This is exactly the case here. Individual perceptions of party differences correlate positively with extreme positions on the ideological scale (Pearson's r = 0.27, p < 0.001) and partisanship (strong partisanship r = 0.15, p < 0.001, weak partisanship r = 0.06, p < 0.001; no partisanship r = −0.16, p < 0.001).Footnote ¹² In short, country means are highly dependent on the distribution of these characteristics in the respective sample.Footnote ¹³

Therefore, to arrive at a measure of party-system polarization, we refer to the expert ratings obtained by CSES (see also Lupu Reference Lupu2015). The experts include the members of the consortium or the academic project team responsible for data collection in a given country, who, like respondents, were asked to position their country's political parties on the left–right continuum. These experts, all of them scholars of political science and electoral research, are politically highly sophisticated and, we assume, closely familiar with the state of their respective party system. Hence, the expert ratings are used as an exogenous measure of party-system polarization – that is, all respondents in a given country/election year are coded with an identical party-system polarization score based on the country's expert ratings.Footnote ¹⁴

Applying the Dalton formula to both, we now have an expert measure for party-system polarization and an individual measure for the subjective perception of party differences (for distributions see Figures A3 and A4 in the Supplementary Material). Hypothetically, the two measures stretch from 0 (all parties on the exact same spot of the left–right continuum) to 10 (two identically large parties positioned at the extremes).

With regard to our core independent variables on the individual level, we distinguish moderate voters (self-assessed 4, 5, 6 on the 0 to 10 left–right scale) from extreme voters (0–3 and 7–10).Footnote ¹⁵ We further differentiate between non-partisans, those who weakly identify with a party (‘leaners’), and those who strongly identify with a party, by constructing three corresponding dummy variables. Based on the rich literature on turnout (e.g. Abendschön and Roßteutscher Reference Abendschön, Roßteutscher, Poguntke, Roßteutscher, Schmitt-Beck and Zmerli2015; Armingeon and Schädel Reference Armingeon and Schädel2015; Franklin Reference Franklin2004; Gallego Reference Gallego2015; Leighley and Nagler Reference Leighley and Nagler2014; Lijphart Reference Lijphart1997; Schäfer et al. Reference Schäfer, Roßteutscher and Abendschön2020), we include the following variables as controls because we know that they also explain electoral participation: we use educational levels as a proxy for political sophistication, since no better suited variables are available.Footnote ¹⁶ We distinguish those who have completed higher education (upper secondary or higher), coded as 1, from those with less than upper secondary education (0). We differentiate first-time voters (depending on voting age regulations, i.e. roughly 21 or younger), from more experienced voters (coded 0). Further, we include age in years, gender (1 for male, 0 for women), current employment status (1 for unemployed, 0 for not unemployed), satisfaction with democracy measured on a scale ranging from 1 ‘not at all satisfied’ to 4 ‘very satisfied’ and individual self-placement on the left–right scale (0 to 10). All continuous variables are z-transformed.

On the country level, we control for the share of moderate and non-partisan citizens – that is, the relative share of the electorate in a given country that self-assesses as a 4, 5, or 6 on the left–right scale and the relative share that claims to be unaffiliated to a party, respectively (for distributions see Figures A8 and A9 in the Supplementary Material). As further control variables, we include election year and, from the World Bank, the general unemployment rate and the GDP per capita one year before the election. Democratic regime age is extracted from the Polity IV project to account for the possibility that long-standing democracies have higher (or lower) turnout. To control for party-system fragmentation, we include the Corrected Effective Number of Electoral Parties (Taagepera Reference Taagepera1997). As voting is compulsory in some of the countries, we include a binary variable for enforced compulsory voting schemes. Finally, to account for the fact that survey data are heavily biased with regard to turnout because of over-reporting and selection bias in particular (Sciarini and Goldberg Reference Sciarini and Goldberg2016), we include a macro-level variable that represents the difference between aggregated turnout based on survey responses and the officially registered turnout.Footnote ¹⁷ All continuous variables were once again z-transformed. To account for the multilevel data structure and the nature of the dichotomous dependent variable, we subsequently conduct multilevel logistic regressions with two levels – the individual and the election level. Although our main interest is in the hypothetically divergent impact of party-system polarization and the subjectively perceived party differences (H1 and H2), our further hypotheses call for interaction terms: between individually perceived party differences and moderate/non-partisan positions (H3) and party-system polarization and moderate/non-partisan positions (H4). Table A2 in the Supplementary Material describes all of the variables involved.

Empirical findings

We claim that party-system polarization and individual perceptions of party differences are conceptually and empirically distinct. The correlation between political polarization based on expert ratings and individual (not aggregated) perceptions of party differences is very low (Pearson's r = 0.17, p < 0.001, see also Figures A3–A5 in the Supplementary Material for the correlation and univariate distributions).Footnote ¹⁸ Hence, operationalizing polarization by aggregating country means reveals very distinct results compared to expert judgements. Thus, aggregating individual perceptions is a suboptimal measure of system polarization. We subsequently use the expert ratings as an exogenous measure of party-system polarization.

Now we test our hypotheses with multivariate analyses. Table A7 (in the Supplementary Material) presents the full model estimates. First, we present a main effects model and then test our hypotheses based on interaction terms. As for the main effects hypotheses (H1 and H2), both are statistically and substantially significant. Party-system polarization depresses turnout (odds ratio of 0.78**), but an individual's perception of party differences increases the likelihood of voting (odds ratio of 1.21***). Figure 1 presents the probabilities of individual turnout (a) under the condition of party-system polarization and (b) under the condition of individually perceived party differences.

Figure 1.

Direct Turnout Effect of Party System Polarization (Left) and Individuals' Perceptions of Party Differences (Right) with 95% Confidence Intervals

Note: x-axis scale units denote changes in standard deviation from mean.

In both instances, the effect size is not trivial and, as assumed, it runs in different directions. In the case of low party-system polarization (left panel), turnout rates are more than 90%, compared to a maximally polarized system where turnout is roughly 12 percentage points lower.Footnote ¹⁹ In terms of individual perceptions of party differences, the probability of turnout changes by about 10%, depending on an individual's ability to perceive clear party differences. Hence, system polarization relates to lower turnout and individual difference perceptions correlate with higher participation.Footnote ²⁰ Other variables in the model (see M1 in Table A7) reveal that moderate citizens have a lower turnout rate than extreme citizens (see also Dassonneville and Çakır Reference Dassonneville, Çakır and Dalton2021) – and there is no difference between a left-leaning and a right-leaning respondent (for similar findings, see Van der Meer et al. Reference Van der Meer, van Deth and Scheepers2009: 1439). We also find that partisans vote at much higher rates than non-partisans. Moreover, as an electorate's share of moderate citizens increases, so too does abstention; as the share of non-partisans increases, so too does the turnout.Footnote ²¹ As for our controls, evidence confirms the results of turnout research. Education increases turnout and newly enfranchised cohorts tend to abstain. Older citizens vote at higher rates, while unemployment decreases participation, and there is no gender gap. However, satisfaction with democracy exerts clear positive effects. Looking at macro-level controls, neither regime age, GDP per capita, unemployment rate, turnout over-reporting nor survey year impact electoral participation. Compulsory voting exerts a strong positive effect and the corrected effective number of electoral parties a negative effect.

In H3, we expected that difference perceptions would increase turnout among all voters – moderate and extreme voters, partisans and non-partisans alike. Figure 2 displays the interaction between difference perceptions moderated by individual ideological position and partisanship (see Table A7, Models M2 and M3). Looking at individuals' ideological positions (left panel in Figure 2), extreme citizens always show a slightly higher turnout rate than moderates. Note, however, that confidence intervals overlap. Hence, this difference is not statistically significant. However, in both groups, turnout increases continuously with higher difference perceptions. The change in turnout probability amounts to roughly 10 percentage points, comparing citizens with the lowest and the highest difference perceptions. Turning to partisanship (right panel in Figure 2), non-partisans benefit more from increasing difference perceptions (an increase in turnout of about 11 percentage points) than partisans. Note, however, that partisans, particularly those who identify strongly, turn out at very high rates either way. Even those who perceive no differences whatsoever have a turnout rate close to 90%.

Figure 2.

The Effect of Perceived Party Differences by Moderate/Extreme Position (Left) and Different Levels of Partisanship (Right) with 95% Confidence Intervals

Note: x-axis scale units denote changes in standard deviation from mean.

Identifying with a party is a sufficient motivation for voting (for similar findings, see Lachat Reference Lachat2008: 694), and because of ceiling effects, difference perceptions can only moderately increase partisans' turnout rates. In summary, increases in perceptions of differences between parties increase turnout among all citizens alike, as predicted in H3.

Subsequently, we test our hypotheses concerning the moderating effects of party-system polarization. H4 assumes that system polarization decreases turnout among moderates and non-partisans (compared to extreme citizens and partisans). Figure 3 visualizes the interaction effects from the multilevel logistic regression (see models M4 and M5 in Table A7). Beginning with ideological positions (left panel), both moderates and extremists have the highest predicted turnout (above 90%) when system polarization is low. Turnout decreases steadily with increasing levels of polarization. Despite being true for both groups, the turnout decline is stronger among moderates. Turnout declines among moderates by 15 percentage points when comparing a party system with the lowest polarization level to one with the highest polarization level. Among extreme citizens, the decrease amounts to roughly 10 percentage points. The difference between both groups is statistically significant, yet confidence intervals overlap (see model M4 in Table A7). Party-system polarization suppresses turnout in both groups, but to a much lower degree among extremists than moderates.

Figure 3.

The Effect of Party System Polarization by Moderate/Extreme Position (Left) and Levels of Partisanship (Right) with 95% Confidence Intervals

Note: x-axis scale units denote changes in standard deviation from mean.

A different picture emerges for partisanship (right panel). Strong partisans remain unaffected by system polarization. In every constellation, their turnout is clearly above 90%. By contrast, persons with weak party identification react to increasing system polarization with declining turnout. However, the decline is modest (clearly above 90% to slightly below 90%) compared to non-partisans. When party-system polarization is low, non-partisans vote at a rate of 88% – that is, with only slightly lower levels than both partisan groups. From low to high levels of system polarization, their turnout rate decreases by about ten percentage points. Hence, hostile party environments lead non-partisans to disengage, while partisans are unaffected by centrist party systems. Although Figure 3 tells a rather clear story, this effect is statistically insignificant (model M5 in Table A7), hence we cannot be certain that our hypothesis is confirmed.

Robustness

To verify whether our results are robust, we tested alternatives concerning case selection, variable construction and model estimation. To address case selection, we reran the models with five specifications: (1) excluding systems with compulsory voting schemesFootnote ²² (for the results see Table A10 in the Supplementary Material); (2) excluding non-OECD countries (see Table A11); (3) excluding two-party systems (see Table A12); (4) excluding party-system polarization outliers – that is, with index values lower than 2 and higher than 6Footnote ²³ (see Table A13) – and (5) excluding countries that Freedom House rates either non-free or partially free (i.e. with a score above 2.5)Footnote ²⁴ (see Table A14). On the variable level, we model seven variants: (1) we include parties with a vote share of above 2% (instead of 4%) (see Table A15); (2) we impute means for missing individuals' party ratings (compared to treating them as missing values) (see Table A16); (3) we operationalize ‘moderateness’ on a continuum (instead of the dummy variable) (see Table A17); (4) we use polarization indices unweighted by party size (see Table A18); (5) we use the ideological distance between the most extreme parties as polarization indices (see Table A19); (6) we use aggregated perceived party differences as a party-system polarization measure (see Table A20); and (7) we use our party-system polarization measure but control for aggregated perceived party differences (see Table A21). Regarding model estimation, we rerun the analyses using election-fixed effects (see Table A22) and country-fixed effects (see Table A23). Table 1 summarizes the results.

Table 1.

Robustness Checks

Note: x indicates the confirmation of the hypothesis. ⁺Indicates p < 0.1 significance level.

Regarding the main effects of system polarization and individual difference perceptions (H1 and H2), which are this article's focus, every possible variant results in identical findings: strongly depressing effects of system polarization and strong positive effects of perceived party differences. Hence, our core hypotheses concerning the divergent impact of those two conceptually distinct aspects are highly robust. The same applies to our assumption regarding the participatory impact of perceived party differences among all groups of an electorate (H3), although the effect is significantly smaller for strong partisans than for non-partisans in some instances. Regarding the cross-level interaction between system polarization and moderate and non-partisan citizens (H4), robustness checks support our results. The significant effect of moderateness holds in nine out of fourteen model specifications; the non-significant effect of partisanship repeats in all but one of the models. Thus, H4 is confirmed for moderateness but rejected for partisanship.Footnote ²⁵