Affective Polarization or Partisan Disdain?: Untangling a Dislike for the Opposing Party from a Dislike of Partisanship

Affective Polarization or Partisan Disdain?: Untangling a Dislike for the Opposing Party from a... Abstract Recent scholarship suggests that American partisans dislike other party members so much that partisanship has become the main social divide in modern politics. We argue that at least one measure of this “affective polarization” conflates a dislike for members of the other party with a dislike for partisanship in general. The measure asks people how they feel about their child marrying someone from another party. What seems like negative affect toward the other party is, in fact, negative affect toward partisans from either side of the aisle and political discussion in general. Relying on two national experiments, we demonstrate that although some Americans are politically polarized, more simply want to avoid talking about politics. In fact, many people do not want their child to marry someone from their own party if that hypothetical in-law were to discuss politics frequently. Supplementary analyses using ANES feeling thermometers show that inparty feeling thermometer ratings have decreased in recent years among weak and leaning partisans. As a result, the feeling thermometer results confirm the conclusion from the experiments. Polarization is a phenomenon concentrated in the one-third of Americans who consider themselves strong partisans. More individuals are averse to partisan politics. The analyses demonstrate how affective polarization exists alongside weakening partisan identities. Contemporary scholars and journalists show great interest in the growing partisan divide among Americans. Although scholars debate whether this divide is based on actual differences in issue preferences or merely on perceived differences (Mason 2015; Levendusky and Malhotra 2016), most acknowledge that “affective” polarization—“view[ing] opposing partisans negatively and copartisans positively” (Iyengar and Westwood 2015, p. 691)—appears to be increasing. In this study, however, we find that at least one widely cited measure of affective polarization overstates the amount of affective polarization.1 The measure asks partisans how they would feel if their child were to marry someone from the other political party. As many as half of respondents report that their child marrying someone from the other party would make them unhappy, indicating, according to Iyengar, Sood, and Lelkes (2012), a large amount of affective polarization. Among the potential issues with this measure, the most problematic is that it conflates two distinct phenomena: (1) affective polarization; and (2) a dislike for political parties generally (Klar and Krupnikov 2016). Further, the measure is included alongside other political questions, heightening the salience of partisan considerations. Finally, respondents hear only about the hypothetical in-law’s partisanship, but not other traits or descriptors, implying that partisanship is a particularly important aspect of the potential in-law’s identity. To examine the effects of these features on measured affective polarization, we conduct a survey experiment on two nationally representative samples at two points in time during 2016, with some respondents interviewed in both samples. The experiment separates a dislike for parties in general from a specific dislike for the outparty by asking respondents how they would feel about a child marrying an individual from both parties. Further, the experiment is part of a survey that does not ask other political questions. Finally, we tell respondents how frequently the hypothetical in-law discusses politics, thereby providing context about how important partisan politics is to the hypothetical in-law. Results show that affective polarization is a dominant trait only among the one-third of respondents who identify as strong partisans, and they become more polarized during the 2016 presidential campaign. The majority of individuals are not “affectively polarized”; rather, many are averse to partisan politics. Because this question is only one possible measure of affective polarization, in the Online Appendix we use ANES feeling thermometers to confirm that affective polarization is largely confined to strong partisans. Affective Polarization We suggest that studies measuring affective polarization inadvertently measure two distinct concepts: (1) dislike for the outparty; and (2) dislike for partisanship in general. As affective polarization has ostensibly grown, the percentage of people reporting that they dislike both parties also has increased (Smith 2015). Moreover, both a dislike of partisanship and a genuine dislike of the outparty manifest themselves in lower ratings of the outparty because people are more willing to publicly denigrate the outparty even if they actually dislike both parties (Groenendyk 2013). Of primary interest in this paper is a measure of affective polarization based on the Social Distance Scale, originally developed by Bogardus (1926) to measure social distance between racial and ethnic groups. The original scale included seven items, with willingness to marry a member of a particular group indicating the least social distance. Almond and Verba (1963) adapted the scale to political partisanship, asking respondents if they would feel pleased, displeased, or indifferent if their child were to marry “across party lines.” They found that about 5 percent of partisans would be displeased and a similar number reported they would be pleased. Hence, 90 percent of partisans were “indifferent” about their child marrying someone of the outparty. One problem with this question as a measure of affective polarization is that it asks only about the child marrying someone from the outparty. In order to measure affective polarization properly, one must identify those who both dislike the outparty and like their inparty. When researchers ask only about dislike for the other party, they run the risk of overestimating affective polarization. For example, Klar and Krupnikov (2016) find that respondents dislike working with someone who talks about politics even when that hypothetical colleague agrees with their political views. Further, an often overlooked confound arises in surveys when respondents infer omitted information (Dafoe, Zhang, and Caughey 2016). Some respondents will assume that partisanship is an important identity for the hypothetical child-in-law if the question mentions only partisanship in describing this individual. This can increase the probability that the respondent is unhappy, because the majority of Americans dislike strong partisans (Klar and Krupnikov 2016). Finally, measuring affective polarization in the context of a larger political survey can prime partisan considerations. In particular, questions about partisan politics may bring to mind political polarization, leading respondents to believe that the hypothetical in-law is more extreme, as Americans tend to overestimate the ideological extremity of the other party’s members (Levendusky and Malhotra 2016). Methods DATA Respondents to our two survey experiments were members of the nationally representative GfK sample via Time-Sharing Experiments for Social Sciences. The first survey took place from January 21 to February 1, 2016. The second began July 19 and finished on August 10, 2016 (during the Republican and Democratic conventions). The first sample consisted of 2,030 adult Americans; and the second sample of 2,136 adult Americans, 1,428 of whom had participated in the first survey.2 On the first survey, respondents also participated in an unrelated experiment about wages and the stock market. No partisan political actors were mentioned. In the second survey, respondents answered no questions prior to our experiment. Hence, nothing should prime partisan considerations. EXPERIMENT Both samples were randomly assigned to one of three groups. Respondents who participated in both surveys were assigned to the same treatment, allowing us to look at changes over time. First, following Iyengar, Sood, and Lelkes (2012), one-third of the sample was asked both of the following questions: “How would you feel if you had a son or daughter who married someone who votes for the Democratic Party? Would you feel unhappy or happy?” and “How would you feel if you had a son or daughter who married someone who votes for the Republican Party? Would you feel unhappy or happy?”3 Responses fell on a five-point scale ranging from “very unhappy” to “very happy.” The second group received the same questions but with one important change: the hypothetical child-in-law was described as someone who “talks about politics rarely.” The final third of the sample read about a child-in-law who discusses politics “frequently.” These treatments eliminate the need for the respondent to infer the importance of partisanship to the child-in-law. Within each of the three experimental groups, we randomly assigned respondents to read about the hypothetical child-in-law’s partisan affiliation in one of two different ways. Half read about an in-law who either “supports the Democratic Party [Republican Party],” and the other half read about an in-law who “supports local Democratic [Republican] candidates.” This allows us to distinguish dislike for national-level parties from a dislike for any candidate affiliated with that party. In addition to the experimental measures, the instrument included items related to partisanship, education levels, gender, race, and census region. These measures allow for the identification of types of individuals who are more prone toward affectively polarization. Results HAPPINESS In figure 1, the left side of both the top panel (Wave 1, January 2016) and bottom panel (Wave 2, July/August 2016) displays the measure reported in previous studies: unhappiness with a child-in-law who supports the other party. Considering the control group in both waves, slightly more than 30 percent of respondents reported unhappiness at the notion of their child marrying someone from the outparty. This proportion is slightly greater than the percentage that Iyengar, Sood, and Lelkes (2012) report for 2008, but about 10 percentage points less than their 2010 results. Figure 1. View largeDownload slide Measuring respondent unhappiness with their child marrying someone from the other party and happiness with child marrying someone from their own party. All estimates adjusted using probability weights. Figure 1. View largeDownload slide Measuring respondent unhappiness with their child marrying someone from the other party and happiness with child marrying someone from their own party. All estimates adjusted using probability weights. When the hypothetical in-law rarely discusses politics (light gray bars), the percentage of reported unhappiness drops by about five percentage points (p < .05, one-tailed test). When the hypothetical in-law discusses politics frequently (dark gray bars), however, there is a larger and statistically significant difference in reported unhappiness (about 10 percentage points). This suggests that many individuals are more averse to disagreeable political discussion in their family than they are toward members of the other party generally. Important patterns emerge when the analysis considers partisan strength. Not surprisingly, weak/leaning respondents in the control group are about 30 percentage points less unhappy about partisan intermarriage than are strong partisans. Differences in treatment effects become apparent as well: while weak/leaning partisans are affected only when the child-in-law discusses politics frequently, strong partisans are more affected when discussion is rare (the decrease is statistically significant in Wave 1 but not Wave 2). The second panel in both graphs illustrates a previously underexplored aspect of affective polarization: happiness with their child marrying someone from their own party. The vast majority of people do not care if their child marries someone from their own party: only about 35 percent of respondents in the control group would be happy if this occurred. That number drops below 30 percent in both treatment groups in both surveys (p < .05, two-tailed tests). The treatment effects are largest among strong partisans. Compared with the control group, both experimental conditions lower strong partisans’ reported happiness with their child marrying a copartisan. Strong partisans are less happy if their child marries someone from their party who rarely talks about politics, presumably because in that situation partisanship is irrelevant. But they are also less happy if their child marries someone from their party who frequently talks about politics. Even strong partisans dislike too much political discussion—even agreeable discussion. POLARIZATION The dependent variable in figure 2 is our best measure of affective polarization. It is a dummy variable coded 1 if a respondent is happy about his/her child marrying a copartisan and unhappy about his/her child marrying an opposing partisan and 0 in all other cases.4 In the control group, about 25 percent of respondents in both Wave 1, January 2016 (top panel) and Wave 2, July/August 2016 (bottom panel) are affectively polarized—that is, they are happier when their child marries someone from the inparty than the outparty. The control group, however, cannot distinguish people emotionally invested in partisanship from those who want to engage in only agreeable political discussion (see Huckfeldt and Mendez [2008]). Figure 2. View largeDownload slide Subjects who are affectively polarized by partisan strength and treatment. All estimates adjusted using probability weights. Figure 2. View largeDownload slide Subjects who are affectively polarized by partisan strength and treatment. All estimates adjusted using probability weights. Hence, it is important to look at the Rarely treatment. If a partisan respondent gives polarized responses even when they know they will rarely have to engage in political discussion with the opposing partisan, then that person is affectively polarized. Only about 15 percent of all respondents are affectively polarized in both surveys. That number is less than 10 percent among weak/leaning respondents and about 25 percent of strong partisans. The Online Appendix includes an ordered logit model that estimates an individual’s level of polarization. A few consistent findings emerged from this model. First, strength of partisanship increases polarization. Second, Republicans are more polarized, but only if they are not educated. Third, college graduates are more polarized, but only if they are Democrats. The most important result confirms that previous polarization results are driven by a fear of disagreeable conversation and not pure affect, as the Rarely treatment always reduces polarization. In Wave 2 only, the Local Candidates treatment lowers the level of affective polarization, suggesting that some of the polarization in August is a reaction to a dislike of supporters of Donald Trump and Hillary Clinton and not necessarily the parties themselves. Depending on how one conceptualizes affective polarization, individuals who are responding only to presidential candidates might not be polarized because they are responding to specific political figures and not political groups. STABILITY OF POLARIZATION The final analysis examines the stability of polarization using the measure constructed for figure 2’s analysis. Across all treatments, about 65 percent of respondents had the same level of polarization in both surveys. Of those who gave different responses, 57 percent became more polarized in the summer and 43 percent became less polarized. Table 1’s multinomial logit provides a closer look at these differences. The dependent variable has three categories: (1) the respondent is less polarized in the summer; (2) the respondent is more polarized in the summer; (3) there is no change in polarization (the reference category). The main independent variables are the treatment variables with partisan strength interacted with both Frequently and Rarely. We control for polarization level in Wave 1. We also include a series of control variables measured by GfK prior to our survey. Inclusion of control variables is necessary to avoid omitted variable bias with the partisan strength variable (Kam and Trussler forthcoming), but no substantive conclusions change if they were omitted (see Online Appendix A3). Table 1. Predicting changes in polarization levels   Less polarized  More polarized  b (SE)  b (SE)  Rarely  0.46 (0.34)  –0.52 (0.27)#  Frequently  0.23 (0.39)  –0.05 (0.25)  Strong partisan  –0.96 (0.46)*  0.43 (0.32)  Rarely*Strong partisan  0.52 (0.57)  1.07 (0.41)**  Frequently*Strong partisan  1.01 (0.59)#  0.35 (0.40)  Local treatment  0.67 (0.22)**  –0.06 (0.16)  January polarization  1.24 (0.09)**  0.11 (0.09)  Republican  –0.52 (0.31)  0.31 (0.21)  College degree  –0.56 (0.32)#  0.66 (0.22)**  Republican*College  0.74 (0.46)  –0.92 (0.33)**  Male  0.49 (0.23)*  0.06 (0.16)  White  –0.38 (0.44)  0.38 (0.41)  Black  –0.85 (0.65)  0.06 (0.49)  Hispanic  –0.86 (0.60)  0.16 (0.48)  Midwest  –0.31 (0.37)  –0.50 (0.24)*  South  –0.08 (0.32)  –0.32 (0.21)  West  0.27 (0.35)  0.03 (0.24)  Constant  –3.05 (0.65)**  –1.76 (0.44)**  Number of respondents  1,336    A.I.C.  1914.97      Less polarized  More polarized  b (SE)  b (SE)  Rarely  0.46 (0.34)  –0.52 (0.27)#  Frequently  0.23 (0.39)  –0.05 (0.25)  Strong partisan  –0.96 (0.46)*  0.43 (0.32)  Rarely*Strong partisan  0.52 (0.57)  1.07 (0.41)**  Frequently*Strong partisan  1.01 (0.59)#  0.35 (0.40)  Local treatment  0.67 (0.22)**  –0.06 (0.16)  January polarization  1.24 (0.09)**  0.11 (0.09)  Republican  –0.52 (0.31)  0.31 (0.21)  College degree  –0.56 (0.32)#  0.66 (0.22)**  Republican*College  0.74 (0.46)  –0.92 (0.33)**  Male  0.49 (0.23)*  0.06 (0.16)  White  –0.38 (0.44)  0.38 (0.41)  Black  –0.85 (0.65)  0.06 (0.49)  Hispanic  –0.86 (0.60)  0.16 (0.48)  Midwest  –0.31 (0.37)  –0.50 (0.24)*  South  –0.08 (0.32)  –0.32 (0.21)  West  0.27 (0.35)  0.03 (0.24)  Constant  –3.05 (0.65)**  –1.76 (0.44)**  Number of respondents  1,336    A.I.C.  1914.97    Note.—Estimates from a multinomial logit model. Dependent variable has three categories: (-1) respondent is less polarized in the summer than January; (0) respondent has no change in polarization levels; and (1) respondent is more polarized in the summer than January. The no change category is excluded as the reference. All estimates adjusted using probability weights. #p < .10; *p < .05; **p < .01 in two-tailed tests. View Large Table 1. Predicting changes in polarization levels   Less polarized  More polarized  b (SE)  b (SE)  Rarely  0.46 (0.34)  –0.52 (0.27)#  Frequently  0.23 (0.39)  –0.05 (0.25)  Strong partisan  –0.96 (0.46)*  0.43 (0.32)  Rarely*Strong partisan  0.52 (0.57)  1.07 (0.41)**  Frequently*Strong partisan  1.01 (0.59)#  0.35 (0.40)  Local treatment  0.67 (0.22)**  –0.06 (0.16)  January polarization  1.24 (0.09)**  0.11 (0.09)  Republican  –0.52 (0.31)  0.31 (0.21)  College degree  –0.56 (0.32)#  0.66 (0.22)**  Republican*College  0.74 (0.46)  –0.92 (0.33)**  Male  0.49 (0.23)*  0.06 (0.16)  White  –0.38 (0.44)  0.38 (0.41)  Black  –0.85 (0.65)  0.06 (0.49)  Hispanic  –0.86 (0.60)  0.16 (0.48)  Midwest  –0.31 (0.37)  –0.50 (0.24)*  South  –0.08 (0.32)  –0.32 (0.21)  West  0.27 (0.35)  0.03 (0.24)  Constant  –3.05 (0.65)**  –1.76 (0.44)**  Number of respondents  1,336    A.I.C.  1914.97      Less polarized  More polarized  b (SE)  b (SE)  Rarely  0.46 (0.34)  –0.52 (0.27)#  Frequently  0.23 (0.39)  –0.05 (0.25)  Strong partisan  –0.96 (0.46)*  0.43 (0.32)  Rarely*Strong partisan  0.52 (0.57)  1.07 (0.41)**  Frequently*Strong partisan  1.01 (0.59)#  0.35 (0.40)  Local treatment  0.67 (0.22)**  –0.06 (0.16)  January polarization  1.24 (0.09)**  0.11 (0.09)  Republican  –0.52 (0.31)  0.31 (0.21)  College degree  –0.56 (0.32)#  0.66 (0.22)**  Republican*College  0.74 (0.46)  –0.92 (0.33)**  Male  0.49 (0.23)*  0.06 (0.16)  White  –0.38 (0.44)  0.38 (0.41)  Black  –0.85 (0.65)  0.06 (0.49)  Hispanic  –0.86 (0.60)  0.16 (0.48)  Midwest  –0.31 (0.37)  –0.50 (0.24)*  South  –0.08 (0.32)  –0.32 (0.21)  West  0.27 (0.35)  0.03 (0.24)  Constant  –3.05 (0.65)**  –1.76 (0.44)**  Number of respondents  1,336    A.I.C.  1914.97    Note.—Estimates from a multinomial logit model. Dependent variable has three categories: (-1) respondent is less polarized in the summer than January; (0) respondent has no change in polarization levels; and (1) respondent is more polarized in the summer than January. The no change category is excluded as the reference. All estimates adjusted using probability weights. #p < .10; *p < .05; **p < .01 in two-tailed tests. View Large Figure 3 presents the treatment probabilities by strength of partisanship. In the left panel, among the weak/leaning partisans, people in Control and Frequently were equally likely to be less polarized as more polarized, indicating no aggregate change. In the Rarely treatment, however, respondents were twice as likely to become less polarized as more polarized. Strong partisans in the right panel were more likely to be polarized in all three treatments. Figure 3. View largeDownload slide Polarization effects by partisan strength and treatment. Predicted probabilities calculated using values in table 1. Figure 3. View largeDownload slide Polarization effects by partisan strength and treatment. Predicted probabilities calculated using values in table 1. Interestingly, subjects who were not in the Local Candidates treatment were more likely to become more polarized. Again, this may suggest that measures of affective polarization often capture dislike for the partisan politics respondents see in the news instead of dislike for citizens who are Democrats or Republicans. This could also explain why weak/leaning partisans became less partisan in the Rarely treatment. Spending time with anyone—even someone from the other party—who will not talk about politics is preferred. Conclusion In this paper, we argue that the extent to which modern Americans are “affectively” polarized may be overstated. Rather, there are two distinct phenomena that are easily conflated: affective polarization and a desire to avoid partisan politics. Many strong partisans are affectively polarized. This can make it appear that everyone is polarized, because ideologically extreme partisans are the most politically engaged (Klar 2013). In the presidential campaign, they became even more polarized. But for many Americans—the weak/leaning partisans—the thought of having to discuss politics with even someone from their own party is unappetizing. It is important to note that these results do not imply that affective polarization has not increased—indeed, it has increased. It also does not mean that many people actually like the other party. Rather, scholars are underestimating how much people dislike their own party. In the Online Appendix, we analyze ANES feeling thermometers and find an increasing dislike of the inparty in recent years. These data are consistent with a theory of “negative partisanship” (Abramowitz and Webster 2016)—individuals support their own party mainly because they dislike the other party—but not consistent with affective polarization. The results in this study help improve our understanding of how affective polarization exists alongside weakening partisan identities (Klar and Krupnikov 2016). The implications of these results extend beyond how to measure polarization. Respondents in our surveys appear willing to spend time with individuals with whom they disagree as long as they do not talk about politics. The frequency of disagreement is one of the key variables in the social networks literature (e.g., Huckfeldt, Johnson, and Sprague 2004; Mutz 2006; Mutz and Mondak 2006; Ahn, Huckfeldt, and Ryan 2014). This study further demonstrates the difficulties with the conceptualization and measurement of social network disagreement (Klofstad, Sokhey, and McClurg 2013). Supplementary Data Supplementary data are freely available at Public Opinion Quarterly online. This research was conducted via Time-Sharing Experiments in the Social Sciences (TESS3 197-Klar and TESS3 221 Klar) to S.K. Footnotes 1 As of September 2017, Iyengar, Sood, and Lelkes (2012), who include the measure, have been cited between 150 (Scopus) and 408 (Google Scholar). Not all citations are to the measure. 2 GfK recruits panel members using address-based sampling. Panel members are emailed when they have been assigned to a study, which may be completed online. The first survey’s completion rate was 62 percent. The cumulative response rate is 5.3 percent considering panel recruitment and retention (Callegaro and DiSogra 2008). For the second survey, GfK contacted 1,921 of the first survey’s respondents (74.3 percent completion rate) and 1,208 new respondents (58.6 percent completion rate). This study had a 6.0 percent cumulative response rate. Pure independents are excluded from analyses. 3 The question order was randomized. 4 We replicate figure 2’s analysis by age cohort in Online Appendix A4. 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Google Scholar CrossRef Search ADS   Iyengar, Shanto, Gaurav Sood, and Yphtach Lelkes. 2012. “ Affect, Not Ideology: A Social Identity Perspective on Polarization.” Public Opinion Quarterly  76: 405– 31. Google Scholar CrossRef Search ADS   Iyengar, Shanto, and Sean J. Westwood. 2015. “ Fear and Loathing across Party Lines: New Evidence on Group Polarization.” American Journal of Political Science  59: 690– 707. Google Scholar CrossRef Search ADS   Kam, Cindy D., and Marc J. Trussler. Forthcoming. “ At the Nexus of Experimental and Observational Research: Theory, Specification, and Analysis of Experiments with Heterogeneous Treatment Effects.” Political Behavior . doi: 10.1007/s11109-016-9379-z. Klar, Samara. 2013. “ Identity and Engagement among Political Independents in America.” Political Psychology  35: 577– 91. Google Scholar CrossRef Search ADS   Klar, Samara, and Yanna Krupnikov. 2016. Independent Politics: How American Disdain for Parties Leads to Political Inaction . New York: Cambridge University Press. Google Scholar CrossRef Search ADS   Klofstad, Casey A., Anand Edward Sokhey, and Scott D. McClurg. 2013. “ Disagreeing about Disagreement: How Conflict in Social Networks Affects Political Behavior.” American Journal of Political Science  57: 120– 34. Google Scholar CrossRef Search ADS   Levendusky, Matthew S., and Neil Malhotra. 2016. “ (Mis)Perceptions of Partisan Polarization in the American Public.” Public Opinion Quarterly  80: 387– 91. Google Scholar CrossRef Search ADS   Mason, Lilliana. 2015. “ ‘I Disrespectfully Agree’: The Differential Effects of Partisan Sorting on Social and Issue Polarization.” American Journal of Political Science  59: 128– 45. Google Scholar CrossRef Search ADS   Mutz, Diana C. 2006. Hearing the Other Side: Deliberative versus Participatory Democracy . New York: Cambridge University Press. Google Scholar CrossRef Search ADS   Mutz, Diana C., and Jeffery J. Mondak. 2006. “ The Workplace as a Context for Cross-Cutting Political Discourse.” Journal of Politics  68: 140– 55. Google Scholar CrossRef Search ADS   Smith, Samantha. 2015. “ 24 Percent of Americans Now View Both GOP and Democratic Party Unfavorably.” Pew Research Center , August 21. http://www.pewresearch.org/fact-tank/2015/08/21/24-of-americans-now-view-both-gop-and-democratic-party-unfavorably/. © The Author(s) 2018. Published by Oxford University Press on behalf of the American Association for Public Opinion Research. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Public Opinion Quarterly Oxford University Press

Affective Polarization or Partisan Disdain?: Untangling a Dislike for the Opposing Party from a Dislike of Partisanship

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Abstract

Abstract Recent scholarship suggests that American partisans dislike other party members so much that partisanship has become the main social divide in modern politics. We argue that at least one measure of this “affective polarization” conflates a dislike for members of the other party with a dislike for partisanship in general. The measure asks people how they feel about their child marrying someone from another party. What seems like negative affect toward the other party is, in fact, negative affect toward partisans from either side of the aisle and political discussion in general. Relying on two national experiments, we demonstrate that although some Americans are politically polarized, more simply want to avoid talking about politics. In fact, many people do not want their child to marry someone from their own party if that hypothetical in-law were to discuss politics frequently. Supplementary analyses using ANES feeling thermometers show that inparty feeling thermometer ratings have decreased in recent years among weak and leaning partisans. As a result, the feeling thermometer results confirm the conclusion from the experiments. Polarization is a phenomenon concentrated in the one-third of Americans who consider themselves strong partisans. More individuals are averse to partisan politics. The analyses demonstrate how affective polarization exists alongside weakening partisan identities. Contemporary scholars and journalists show great interest in the growing partisan divide among Americans. Although scholars debate whether this divide is based on actual differences in issue preferences or merely on perceived differences (Mason 2015; Levendusky and Malhotra 2016), most acknowledge that “affective” polarization—“view[ing] opposing partisans negatively and copartisans positively” (Iyengar and Westwood 2015, p. 691)—appears to be increasing. In this study, however, we find that at least one widely cited measure of affective polarization overstates the amount of affective polarization.1 The measure asks partisans how they would feel if their child were to marry someone from the other political party. As many as half of respondents report that their child marrying someone from the other party would make them unhappy, indicating, according to Iyengar, Sood, and Lelkes (2012), a large amount of affective polarization. Among the potential issues with this measure, the most problematic is that it conflates two distinct phenomena: (1) affective polarization; and (2) a dislike for political parties generally (Klar and Krupnikov 2016). Further, the measure is included alongside other political questions, heightening the salience of partisan considerations. Finally, respondents hear only about the hypothetical in-law’s partisanship, but not other traits or descriptors, implying that partisanship is a particularly important aspect of the potential in-law’s identity. To examine the effects of these features on measured affective polarization, we conduct a survey experiment on two nationally representative samples at two points in time during 2016, with some respondents interviewed in both samples. The experiment separates a dislike for parties in general from a specific dislike for the outparty by asking respondents how they would feel about a child marrying an individual from both parties. Further, the experiment is part of a survey that does not ask other political questions. Finally, we tell respondents how frequently the hypothetical in-law discusses politics, thereby providing context about how important partisan politics is to the hypothetical in-law. Results show that affective polarization is a dominant trait only among the one-third of respondents who identify as strong partisans, and they become more polarized during the 2016 presidential campaign. The majority of individuals are not “affectively polarized”; rather, many are averse to partisan politics. Because this question is only one possible measure of affective polarization, in the Online Appendix we use ANES feeling thermometers to confirm that affective polarization is largely confined to strong partisans. Affective Polarization We suggest that studies measuring affective polarization inadvertently measure two distinct concepts: (1) dislike for the outparty; and (2) dislike for partisanship in general. As affective polarization has ostensibly grown, the percentage of people reporting that they dislike both parties also has increased (Smith 2015). Moreover, both a dislike of partisanship and a genuine dislike of the outparty manifest themselves in lower ratings of the outparty because people are more willing to publicly denigrate the outparty even if they actually dislike both parties (Groenendyk 2013). Of primary interest in this paper is a measure of affective polarization based on the Social Distance Scale, originally developed by Bogardus (1926) to measure social distance between racial and ethnic groups. The original scale included seven items, with willingness to marry a member of a particular group indicating the least social distance. Almond and Verba (1963) adapted the scale to political partisanship, asking respondents if they would feel pleased, displeased, or indifferent if their child were to marry “across party lines.” They found that about 5 percent of partisans would be displeased and a similar number reported they would be pleased. Hence, 90 percent of partisans were “indifferent” about their child marrying someone of the outparty. One problem with this question as a measure of affective polarization is that it asks only about the child marrying someone from the outparty. In order to measure affective polarization properly, one must identify those who both dislike the outparty and like their inparty. When researchers ask only about dislike for the other party, they run the risk of overestimating affective polarization. For example, Klar and Krupnikov (2016) find that respondents dislike working with someone who talks about politics even when that hypothetical colleague agrees with their political views. Further, an often overlooked confound arises in surveys when respondents infer omitted information (Dafoe, Zhang, and Caughey 2016). Some respondents will assume that partisanship is an important identity for the hypothetical child-in-law if the question mentions only partisanship in describing this individual. This can increase the probability that the respondent is unhappy, because the majority of Americans dislike strong partisans (Klar and Krupnikov 2016). Finally, measuring affective polarization in the context of a larger political survey can prime partisan considerations. In particular, questions about partisan politics may bring to mind political polarization, leading respondents to believe that the hypothetical in-law is more extreme, as Americans tend to overestimate the ideological extremity of the other party’s members (Levendusky and Malhotra 2016). Methods DATA Respondents to our two survey experiments were members of the nationally representative GfK sample via Time-Sharing Experiments for Social Sciences. The first survey took place from January 21 to February 1, 2016. The second began July 19 and finished on August 10, 2016 (during the Republican and Democratic conventions). The first sample consisted of 2,030 adult Americans; and the second sample of 2,136 adult Americans, 1,428 of whom had participated in the first survey.2 On the first survey, respondents also participated in an unrelated experiment about wages and the stock market. No partisan political actors were mentioned. In the second survey, respondents answered no questions prior to our experiment. Hence, nothing should prime partisan considerations. EXPERIMENT Both samples were randomly assigned to one of three groups. Respondents who participated in both surveys were assigned to the same treatment, allowing us to look at changes over time. First, following Iyengar, Sood, and Lelkes (2012), one-third of the sample was asked both of the following questions: “How would you feel if you had a son or daughter who married someone who votes for the Democratic Party? Would you feel unhappy or happy?” and “How would you feel if you had a son or daughter who married someone who votes for the Republican Party? Would you feel unhappy or happy?”3 Responses fell on a five-point scale ranging from “very unhappy” to “very happy.” The second group received the same questions but with one important change: the hypothetical child-in-law was described as someone who “talks about politics rarely.” The final third of the sample read about a child-in-law who discusses politics “frequently.” These treatments eliminate the need for the respondent to infer the importance of partisanship to the child-in-law. Within each of the three experimental groups, we randomly assigned respondents to read about the hypothetical child-in-law’s partisan affiliation in one of two different ways. Half read about an in-law who either “supports the Democratic Party [Republican Party],” and the other half read about an in-law who “supports local Democratic [Republican] candidates.” This allows us to distinguish dislike for national-level parties from a dislike for any candidate affiliated with that party. In addition to the experimental measures, the instrument included items related to partisanship, education levels, gender, race, and census region. These measures allow for the identification of types of individuals who are more prone toward affectively polarization. Results HAPPINESS In figure 1, the left side of both the top panel (Wave 1, January 2016) and bottom panel (Wave 2, July/August 2016) displays the measure reported in previous studies: unhappiness with a child-in-law who supports the other party. Considering the control group in both waves, slightly more than 30 percent of respondents reported unhappiness at the notion of their child marrying someone from the outparty. This proportion is slightly greater than the percentage that Iyengar, Sood, and Lelkes (2012) report for 2008, but about 10 percentage points less than their 2010 results. Figure 1. View largeDownload slide Measuring respondent unhappiness with their child marrying someone from the other party and happiness with child marrying someone from their own party. All estimates adjusted using probability weights. Figure 1. View largeDownload slide Measuring respondent unhappiness with their child marrying someone from the other party and happiness with child marrying someone from their own party. All estimates adjusted using probability weights. When the hypothetical in-law rarely discusses politics (light gray bars), the percentage of reported unhappiness drops by about five percentage points (p < .05, one-tailed test). When the hypothetical in-law discusses politics frequently (dark gray bars), however, there is a larger and statistically significant difference in reported unhappiness (about 10 percentage points). This suggests that many individuals are more averse to disagreeable political discussion in their family than they are toward members of the other party generally. Important patterns emerge when the analysis considers partisan strength. Not surprisingly, weak/leaning respondents in the control group are about 30 percentage points less unhappy about partisan intermarriage than are strong partisans. Differences in treatment effects become apparent as well: while weak/leaning partisans are affected only when the child-in-law discusses politics frequently, strong partisans are more affected when discussion is rare (the decrease is statistically significant in Wave 1 but not Wave 2). The second panel in both graphs illustrates a previously underexplored aspect of affective polarization: happiness with their child marrying someone from their own party. The vast majority of people do not care if their child marries someone from their own party: only about 35 percent of respondents in the control group would be happy if this occurred. That number drops below 30 percent in both treatment groups in both surveys (p < .05, two-tailed tests). The treatment effects are largest among strong partisans. Compared with the control group, both experimental conditions lower strong partisans’ reported happiness with their child marrying a copartisan. Strong partisans are less happy if their child marries someone from their party who rarely talks about politics, presumably because in that situation partisanship is irrelevant. But they are also less happy if their child marries someone from their party who frequently talks about politics. Even strong partisans dislike too much political discussion—even agreeable discussion. POLARIZATION The dependent variable in figure 2 is our best measure of affective polarization. It is a dummy variable coded 1 if a respondent is happy about his/her child marrying a copartisan and unhappy about his/her child marrying an opposing partisan and 0 in all other cases.4 In the control group, about 25 percent of respondents in both Wave 1, January 2016 (top panel) and Wave 2, July/August 2016 (bottom panel) are affectively polarized—that is, they are happier when their child marries someone from the inparty than the outparty. The control group, however, cannot distinguish people emotionally invested in partisanship from those who want to engage in only agreeable political discussion (see Huckfeldt and Mendez [2008]). Figure 2. View largeDownload slide Subjects who are affectively polarized by partisan strength and treatment. All estimates adjusted using probability weights. Figure 2. View largeDownload slide Subjects who are affectively polarized by partisan strength and treatment. All estimates adjusted using probability weights. Hence, it is important to look at the Rarely treatment. If a partisan respondent gives polarized responses even when they know they will rarely have to engage in political discussion with the opposing partisan, then that person is affectively polarized. Only about 15 percent of all respondents are affectively polarized in both surveys. That number is less than 10 percent among weak/leaning respondents and about 25 percent of strong partisans. The Online Appendix includes an ordered logit model that estimates an individual’s level of polarization. A few consistent findings emerged from this model. First, strength of partisanship increases polarization. Second, Republicans are more polarized, but only if they are not educated. Third, college graduates are more polarized, but only if they are Democrats. The most important result confirms that previous polarization results are driven by a fear of disagreeable conversation and not pure affect, as the Rarely treatment always reduces polarization. In Wave 2 only, the Local Candidates treatment lowers the level of affective polarization, suggesting that some of the polarization in August is a reaction to a dislike of supporters of Donald Trump and Hillary Clinton and not necessarily the parties themselves. Depending on how one conceptualizes affective polarization, individuals who are responding only to presidential candidates might not be polarized because they are responding to specific political figures and not political groups. STABILITY OF POLARIZATION The final analysis examines the stability of polarization using the measure constructed for figure 2’s analysis. Across all treatments, about 65 percent of respondents had the same level of polarization in both surveys. Of those who gave different responses, 57 percent became more polarized in the summer and 43 percent became less polarized. Table 1’s multinomial logit provides a closer look at these differences. The dependent variable has three categories: (1) the respondent is less polarized in the summer; (2) the respondent is more polarized in the summer; (3) there is no change in polarization (the reference category). The main independent variables are the treatment variables with partisan strength interacted with both Frequently and Rarely. We control for polarization level in Wave 1. We also include a series of control variables measured by GfK prior to our survey. Inclusion of control variables is necessary to avoid omitted variable bias with the partisan strength variable (Kam and Trussler forthcoming), but no substantive conclusions change if they were omitted (see Online Appendix A3). Table 1. Predicting changes in polarization levels   Less polarized  More polarized  b (SE)  b (SE)  Rarely  0.46 (0.34)  –0.52 (0.27)#  Frequently  0.23 (0.39)  –0.05 (0.25)  Strong partisan  –0.96 (0.46)*  0.43 (0.32)  Rarely*Strong partisan  0.52 (0.57)  1.07 (0.41)**  Frequently*Strong partisan  1.01 (0.59)#  0.35 (0.40)  Local treatment  0.67 (0.22)**  –0.06 (0.16)  January polarization  1.24 (0.09)**  0.11 (0.09)  Republican  –0.52 (0.31)  0.31 (0.21)  College degree  –0.56 (0.32)#  0.66 (0.22)**  Republican*College  0.74 (0.46)  –0.92 (0.33)**  Male  0.49 (0.23)*  0.06 (0.16)  White  –0.38 (0.44)  0.38 (0.41)  Black  –0.85 (0.65)  0.06 (0.49)  Hispanic  –0.86 (0.60)  0.16 (0.48)  Midwest  –0.31 (0.37)  –0.50 (0.24)*  South  –0.08 (0.32)  –0.32 (0.21)  West  0.27 (0.35)  0.03 (0.24)  Constant  –3.05 (0.65)**  –1.76 (0.44)**  Number of respondents  1,336    A.I.C.  1914.97      Less polarized  More polarized  b (SE)  b (SE)  Rarely  0.46 (0.34)  –0.52 (0.27)#  Frequently  0.23 (0.39)  –0.05 (0.25)  Strong partisan  –0.96 (0.46)*  0.43 (0.32)  Rarely*Strong partisan  0.52 (0.57)  1.07 (0.41)**  Frequently*Strong partisan  1.01 (0.59)#  0.35 (0.40)  Local treatment  0.67 (0.22)**  –0.06 (0.16)  January polarization  1.24 (0.09)**  0.11 (0.09)  Republican  –0.52 (0.31)  0.31 (0.21)  College degree  –0.56 (0.32)#  0.66 (0.22)**  Republican*College  0.74 (0.46)  –0.92 (0.33)**  Male  0.49 (0.23)*  0.06 (0.16)  White  –0.38 (0.44)  0.38 (0.41)  Black  –0.85 (0.65)  0.06 (0.49)  Hispanic  –0.86 (0.60)  0.16 (0.48)  Midwest  –0.31 (0.37)  –0.50 (0.24)*  South  –0.08 (0.32)  –0.32 (0.21)  West  0.27 (0.35)  0.03 (0.24)  Constant  –3.05 (0.65)**  –1.76 (0.44)**  Number of respondents  1,336    A.I.C.  1914.97    Note.—Estimates from a multinomial logit model. Dependent variable has three categories: (-1) respondent is less polarized in the summer than January; (0) respondent has no change in polarization levels; and (1) respondent is more polarized in the summer than January. The no change category is excluded as the reference. All estimates adjusted using probability weights. #p < .10; *p < .05; **p < .01 in two-tailed tests. View Large Table 1. Predicting changes in polarization levels   Less polarized  More polarized  b (SE)  b (SE)  Rarely  0.46 (0.34)  –0.52 (0.27)#  Frequently  0.23 (0.39)  –0.05 (0.25)  Strong partisan  –0.96 (0.46)*  0.43 (0.32)  Rarely*Strong partisan  0.52 (0.57)  1.07 (0.41)**  Frequently*Strong partisan  1.01 (0.59)#  0.35 (0.40)  Local treatment  0.67 (0.22)**  –0.06 (0.16)  January polarization  1.24 (0.09)**  0.11 (0.09)  Republican  –0.52 (0.31)  0.31 (0.21)  College degree  –0.56 (0.32)#  0.66 (0.22)**  Republican*College  0.74 (0.46)  –0.92 (0.33)**  Male  0.49 (0.23)*  0.06 (0.16)  White  –0.38 (0.44)  0.38 (0.41)  Black  –0.85 (0.65)  0.06 (0.49)  Hispanic  –0.86 (0.60)  0.16 (0.48)  Midwest  –0.31 (0.37)  –0.50 (0.24)*  South  –0.08 (0.32)  –0.32 (0.21)  West  0.27 (0.35)  0.03 (0.24)  Constant  –3.05 (0.65)**  –1.76 (0.44)**  Number of respondents  1,336    A.I.C.  1914.97      Less polarized  More polarized  b (SE)  b (SE)  Rarely  0.46 (0.34)  –0.52 (0.27)#  Frequently  0.23 (0.39)  –0.05 (0.25)  Strong partisan  –0.96 (0.46)*  0.43 (0.32)  Rarely*Strong partisan  0.52 (0.57)  1.07 (0.41)**  Frequently*Strong partisan  1.01 (0.59)#  0.35 (0.40)  Local treatment  0.67 (0.22)**  –0.06 (0.16)  January polarization  1.24 (0.09)**  0.11 (0.09)  Republican  –0.52 (0.31)  0.31 (0.21)  College degree  –0.56 (0.32)#  0.66 (0.22)**  Republican*College  0.74 (0.46)  –0.92 (0.33)**  Male  0.49 (0.23)*  0.06 (0.16)  White  –0.38 (0.44)  0.38 (0.41)  Black  –0.85 (0.65)  0.06 (0.49)  Hispanic  –0.86 (0.60)  0.16 (0.48)  Midwest  –0.31 (0.37)  –0.50 (0.24)*  South  –0.08 (0.32)  –0.32 (0.21)  West  0.27 (0.35)  0.03 (0.24)  Constant  –3.05 (0.65)**  –1.76 (0.44)**  Number of respondents  1,336    A.I.C.  1914.97    Note.—Estimates from a multinomial logit model. Dependent variable has three categories: (-1) respondent is less polarized in the summer than January; (0) respondent has no change in polarization levels; and (1) respondent is more polarized in the summer than January. The no change category is excluded as the reference. All estimates adjusted using probability weights. #p < .10; *p < .05; **p < .01 in two-tailed tests. View Large Figure 3 presents the treatment probabilities by strength of partisanship. In the left panel, among the weak/leaning partisans, people in Control and Frequently were equally likely to be less polarized as more polarized, indicating no aggregate change. In the Rarely treatment, however, respondents were twice as likely to become less polarized as more polarized. Strong partisans in the right panel were more likely to be polarized in all three treatments. Figure 3. View largeDownload slide Polarization effects by partisan strength and treatment. Predicted probabilities calculated using values in table 1. Figure 3. View largeDownload slide Polarization effects by partisan strength and treatment. Predicted probabilities calculated using values in table 1. Interestingly, subjects who were not in the Local Candidates treatment were more likely to become more polarized. Again, this may suggest that measures of affective polarization often capture dislike for the partisan politics respondents see in the news instead of dislike for citizens who are Democrats or Republicans. This could also explain why weak/leaning partisans became less partisan in the Rarely treatment. Spending time with anyone—even someone from the other party—who will not talk about politics is preferred. Conclusion In this paper, we argue that the extent to which modern Americans are “affectively” polarized may be overstated. Rather, there are two distinct phenomena that are easily conflated: affective polarization and a desire to avoid partisan politics. Many strong partisans are affectively polarized. This can make it appear that everyone is polarized, because ideologically extreme partisans are the most politically engaged (Klar 2013). In the presidential campaign, they became even more polarized. But for many Americans—the weak/leaning partisans—the thought of having to discuss politics with even someone from their own party is unappetizing. It is important to note that these results do not imply that affective polarization has not increased—indeed, it has increased. It also does not mean that many people actually like the other party. Rather, scholars are underestimating how much people dislike their own party. In the Online Appendix, we analyze ANES feeling thermometers and find an increasing dislike of the inparty in recent years. These data are consistent with a theory of “negative partisanship” (Abramowitz and Webster 2016)—individuals support their own party mainly because they dislike the other party—but not consistent with affective polarization. The results in this study help improve our understanding of how affective polarization exists alongside weakening partisan identities (Klar and Krupnikov 2016). The implications of these results extend beyond how to measure polarization. Respondents in our surveys appear willing to spend time with individuals with whom they disagree as long as they do not talk about politics. The frequency of disagreement is one of the key variables in the social networks literature (e.g., Huckfeldt, Johnson, and Sprague 2004; Mutz 2006; Mutz and Mondak 2006; Ahn, Huckfeldt, and Ryan 2014). This study further demonstrates the difficulties with the conceptualization and measurement of social network disagreement (Klofstad, Sokhey, and McClurg 2013). Supplementary Data Supplementary data are freely available at Public Opinion Quarterly online. This research was conducted via Time-Sharing Experiments in the Social Sciences (TESS3 197-Klar and TESS3 221 Klar) to S.K. Footnotes 1 As of September 2017, Iyengar, Sood, and Lelkes (2012), who include the measure, have been cited between 150 (Scopus) and 408 (Google Scholar). Not all citations are to the measure. 2 GfK recruits panel members using address-based sampling. Panel members are emailed when they have been assigned to a study, which may be completed online. The first survey’s completion rate was 62 percent. The cumulative response rate is 5.3 percent considering panel recruitment and retention (Callegaro and DiSogra 2008). For the second survey, GfK contacted 1,921 of the first survey’s respondents (74.3 percent completion rate) and 1,208 new respondents (58.6 percent completion rate). This study had a 6.0 percent cumulative response rate. Pure independents are excluded from analyses. 3 The question order was randomized. 4 We replicate figure 2’s analysis by age cohort in Online Appendix A4. 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Mondak. 2006. “ The Workplace as a Context for Cross-Cutting Political Discourse.” Journal of Politics  68: 140– 55. Google Scholar CrossRef Search ADS   Smith, Samantha. 2015. “ 24 Percent of Americans Now View Both GOP and Democratic Party Unfavorably.” Pew Research Center , August 21. http://www.pewresearch.org/fact-tank/2015/08/21/24-of-americans-now-view-both-gop-and-democratic-party-unfavorably/. © The Author(s) 2018. Published by Oxford University Press on behalf of the American Association for Public Opinion Research. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices)

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Public Opinion QuarterlyOxford University Press

Published: May 14, 2018

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