Abstract
Recent work on the nature of mass polarization has revealed that individuals perceive more polarization than actually exists, meaning they assume that out-party members are farther from them on the liberal-conservative continuum than they actually are according to measures of their personal preferences. But what are the consequences of this biased perception, and how do they differ from the consequences of actual polarization? In this paper, we use American National Election Studies data to estimate actual and perceived polarization at the individual level from 1972–2012. We find that the two types of polarization, while related themselves, are differentially related to other attitudinal and behavioral outcomes of normative interest. Namely, we find that perceived polarization is more strongly related to negative affective evaluations of out-parties and out-party candidates, voting, participation, trust, and efficacy than is actual polarization, which shares much weaker relationships with these constructs.
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Notes
We use these issues because they are available in each year of our analysis.
Because a latent variable lacks an inherent metric, one of the observed variables must be used to scale the latent variables. Since the perceived and actual ideological identification items were available across all surveys, we use those items to scale the perceived and actual polarization latent variables. That is, we constrain those factor loadings to equal 1 across all years. We also constrained the constants for the ideological identification items across years to the 1972 constants for those items. The combination of these two constraints allows for estimation of the perceived and actual polarization scales on the same metric across years. In other words, loadings of observed variables and levels of the latent variables—perceived and actual polarization—can both be compared across time.
See the Supplemental Appendix for more information about the factor models, such as variable factor loadings and the proportion of variance explained by the first factor.
Still, we admit that our measure of perceived polarization is a potentially liberal one. If people are thinking of the parties as elites, then we might expect that perceived polarization is higher than it would otherwise be if they were thinking of members of the out-party in the electorate. Yet, there is a great deal of evidence that partisans in the electorate are quite socially (indeed, even physically) “distant” from one another. Members of the two parties live in increasingly more homogenous neighborhoods (Bishop 2009), decreasingly marry across party lines (Alford et al. 2011), discriminate against each other to a greater extent than they do based on race (Iyengar and Westwood 2015), can scarcely agree on where to shop (Chapin 2015), and even smell differences between in- and out-party members (McDermott et al. 2014)! If all of these seemingly politically unrelated, yet sharp, social divides persist among members of the parties in the electorate, we have little reason to believe that the “true” reference group (i.e., party elites, or party members in the electorate) is of any consequence when it comes to our measure of perceived polarization. Our measure of perceived polarization is but a particular operationalization of social polarization, just like the others mentioned above.
As a result of using the average position of the out-party, true Independents are omitted from our analyses. We also constructed the actual polarization measure using the median, rather than mean, out-party identifier as a robustness check. The correlation between this alternative operationalization and the one we employ below is 0.98, and no substantive results from any analysis differs employing the median measure.
Though we technically employ measures of both symbolic ideological orientations (ideological self-identifications) and operational ideological orientations (issue positions), we have little reason to believe that this influences our actual and perceived polarization measures. First and foremost, we wish to emphasize that the items scale together very well. Additionally, from a measurement perspective, we are not merely utilizing measures of ideological self-identification and issue attitudes. Instead, the individual polarization items are measures of individuals’ orientations toward out-party members. Thus, the major source of variance in the items is no longer symbolic or operational ideological concerns, but conceptual distances along a general ideological dimension. Since these distances are presumably—and, in fact, empirically—similar across items, the items are highly correlated and can, therefore, be explained by a single latent factor. Our modeling strategy implies that the idiosyncrasies of a given item are of no concern; indeed, they are treated as measurement error. Rather, our strategy focuses on capturing the average perceived and actual conceptual distances between individuals and out-party members along the liberal-conservative ideological dimension.
To create the political sophistication variable, we estimated an exploratory factor analysis of interest in politics, knowledge as ascertained by the interviewer, and an index of participation in campaign activities and predicted individual scores along the first factor. A single factor accounts for 98% of the variance across the three indicators.
We measure elite polarization as the difference in party median DW-Nominate scores in the House for each congressional term.
The sample on which the full model was estimated included 13,320 individuals. The \(R^2\) values are 0.239 and 0.171 for the perceived polarization and actual polarization equations, respectively.
We also include dummy variables for all but one year so that we can control for any potential effects of time, or idiosyncrasies of a given year, in the model. This is true of all models presented. These estimates are not depicted in the table as they add no substantive value to our analysis.
The full question wording and coding details for these variables appears in the Supplemental Appendix. Each of the variables was rescaled to range from 0 to 1.
Sophistication is not included in the participation and voting models because campaign participation is a component of the composite sophistication measure. Instead, we included interest in politics – a proxy for sophistication that would not bias upward the effect of sophistication in these models. Note also that partisan and ideological self-identifications are employed in the trust model, rather than the mere strength of these orientations. This is because previous work has demonstrated a substantive partisan and ideological component to trust in the government (Hetherington and Rudolph, 2015).
Though actual and perceived polarization are related to voting in opposite ways, such an inconsistency should be taken with a grain of salt. Voting is an important political behavior to consider, but is grossly over-reported (upwards of 80% in some years) on the ANES and other high quality surveys, such as the General Social Survey. Additionally, we believe the countervailing effects are consistent with the results of the multivariate regression shown above. Specifically, perceived polarization is more strongly related to sophistication and positively related to education; education has the opposite effect on actual polarization. Education and political sophistication are long known correlates of vote behavior (Palfrey and Poole 1987).
These findings deviate from those found in Moral (2017). We believe these discrepancies are of little concern and are likely a result of differences in (a) political contexts and (b) measurement. Moral investigates multiparty systems, where those with moderate preferences are less likely than those in the American two-party system to be entirely disaffected (i.e., parties that represent them may still exist). Further, he measures polarization as the perceived distance between parties, whereas our measures are anchored to the individual.
We also include dummy variables for all but one year so that we can control for any potential effects of time, just as we did in the analyses presented in Table 1.
Alterations in the cutoffs for “low” and “high” do not alter substantive results.
We are careful not to suggest that those low in actual polarization are low in perceived polarization, or vice versa. Indeed, the modest linear relationship between these two constructs suggests this is not necessarily the case. Instead, we highlight the effects of each when controlling for the other relevant type of polarization, which deviates from previous studies.
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Enders, A.M., Armaly, M.T. The Differential Effects of Actual and Perceived Polarization. Polit Behav 41, 815–839 (2019). https://doi.org/10.1007/s11109-018-9476-2
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DOI: https://doi.org/10.1007/s11109-018-9476-2