Abstract Although Prior Research Demonstrates That Strong Partisans

Abstract

Although prior research demonstrates that strong partisans are less likely to cast a split-ticket, recent scholarly work hints that partisan-ideological sorting—the matching of an individual’s partisan and ideological identities—may play a comparatively stronger role in shaping this voting behavior. Simply, if a high degree of congruence between identities underscores psychological orientations that prevent association with an out-group, then highly-sorted voters should be less likely to cross-party lines within the voting booth. Using 1972-2012 ANES Time-Series and 2010 CCES surveys, we provide evidence that demonstrates that a high degree of partisan-ideological sorting produces the strongest negative effect on split-ticket voting across national and subnational ticket pairings. We then supplement these analyses with 1992-1996 ANES Panel data to demonstrate how changes in sorting over time affect this type of voting behavior. Our results indicate that although an increase in partisan strength alone is insufficient to reduce an individual’s propensity to cast a split-ticket, an increase in identity sorting over time has a strong negative and significant effect on split-ticket voting. We conclude with a brief discussion about the consequences of identity convergence; namely, that sorting fosters a unique form of “electoral polarization.”

Abstract word count: 192

Manuscript word count (excluding references, tables, figures): 6,099

Key words: sorting, ticket-splitting, electoral polarization Sorting and the Split-Ticket

Although the study of split-ticket voting has received generous scholarly attention

(Campbell and Miller, 1957; Beck, Baum, Clausen, Smith, 1992; Burden and Kimball,

2004; Davis, 2014), relatively little research has considered how the effects of mass sorting have contributed to the prevalence of split-tickets.1 However, recent work on partisan-ideological sorting—the process whereby an individual’s partisan and ideological identities converge—has clear implications for how scholars traditionally consider “motivated” explanations of ticket-splitting behavior. Simply, if a high degree of integration between congruent identities underscores significant psychological orientations that prevent association with an out-group (Roccas and Brewer, 2002;

Brewer and Pierce, 2005), then highly-sorted voters should be less likely to cross party lines within the voting booth. As a result of these increasingly tribal attachments

(Mason, 2015), we argue that split-ticket voting should be extraordinarily rare for individuals with strong, overlapping ideological and partisan identities.

Using the 1972-2012 ANES Time-Series and 2010 CCES surveys, we provide firm evidence that partisan-ideological sorting routinely produces the strongest negative effect among a battery of alternative explanations for split-ticket voting at both the national and subnational level. We then supplement these analyses with

1992-1996 ANES Panel data, which cover a period of time in which Americans’ political identities were in flux and partisan-ideological sorting rapidly accelerated

(Mason, 2015), to demonstrate the causal effects of changes in sorting on vote choice.

Interestingly, these results indicate that although an increase in partisan strength alone is insufficient to motivate a reduction in an individual’s propensity to cast a split-ticket, an increase in sorting over time has a strong negative and significant effect on this voting behavior. We conclude with a brief discussion about why previous work

1 Levendusky (2009) includes one analysis of how a form of policy sorting affects split- ticket voting in the narrow window of 1992 to 1996, but this form of sorting is appreciably different from our operationalization of partisan-ideological sorting, which we expound on below.

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that concentrates solely on the strength of partisan attachments undersells the role that multiple, consistent political identities play in creating a unique form of electoral

“polarization.”

Preferences, identity, and split-ticket voting

Although institutional conditions play a role in structuring the frequency of ticket- splitting (Burden, 2002), the earliest scholarly work on split-ticket voting clearly emphasizes the primary role that individuals’ partisan preference orientations play in producing straight or split-tickets (Campbell and Miller, 1957).2 Indeed, if electoral choice reflects the degree to which preference orientations are integrated into an individual’s partisan consciousness, then clear partisan preferences should result in straight-ticket voting (Lavine, Johnson, and Steenbergen, 2012). Conversely, for individuals with weakly-rooted or conflicted preference orientations, conflict between these preferences should introduce instability into electoral decision-making

(Campbell and Miller, 1957),3 while indifference—the utter lack of any meaningful partisan preferences—should generate the highest likelihood of casting a split-ticket because partisan attachments are only nominally integrated into the indifferent individual’s partisan self-image (Davis, 2014).

2 Two institutional conditions also worth considering are candidate incumbency and availability. First, incumbency increases the likelihood of split-tickets because the name recognition enjoyed by incumbent candidates is often significant enough to attract out- party voters (Beck et al. 1992; Burden 2002). Second, the actual availability of in-party candidates is related to ticket splitting insofar as candidates running unopposed may attract voters who would not, under other circumstances, cast a vote for that candidate (Burden and Kimball 2004) 3 The cross-pressured voter—who is “attracted to each party by one set of opinions and repelled by another” (Berelson, Lazarsfeld, and McPhee, 1954, 200)—is likely to cast a split-ticket as a function of this ambivalence (Mulligan 2011).

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However, although these preference orientations underscore the importance of singular, conflicted, or absent partisan motivations, it is the underlying partisan self- image—or political identity—that fundamentally shapes voting behavior generally

(Green, Palmquist, and Shickler, 2002; Greene, 2004) and ticket-splitting behavior specifically (Beck, Baum, Clausen and Smith, 1992). At its core, this is a social identity conceptualization of partisanship that suggests that electoral decision-making should primarily be a function of the strength of the affective connections that an individual shares with a particular party (Campbell, Converse, Miller, and Stokes,

1960; Greene, 1999, 2002, 2004; Iyengar et al., 2012; Huddy, Mason, and Aaroe, 2015).

Thus, if “partisanship is the most prominent political identity because parties are the groups that directly compete for power in the political realm, and competition between groups increases the salience of competing group identities” (Mason, 2015, 130), then individuals who strongly identify with a particular party should engage in behaviors that are consistent with or on behalf of those parties (Huddy, 2001; Tajfel and Turner,

1979).

Throughout the literature on split-ticket voting, the connection between the strength of partisan identity and an individual’s propensity to cast a split-ticket is consistently noted in both presidential and subpresidential elections (Campbell and

Miller, 1957; Beck et al., 1992; Burden and Kimball, 2004; Mulligan, 2011; Davis,

2014). However, recent research on partisan sorting—or the convergence of an individual’s partisan and ideological identities—suggests that accounting solely for an individual’s partisan identity may under-estimate the effect that strong and, importantly, consistent political attachments have on split-ticket voting. Indeed, we suspect that for those individuals with strong and congruent partisan and ideological identities (i.e. the highly-sorted), the likelihood of casting a split-ticket should be particularly low.

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The role of sorting

Recent research has shown that partisan-ideological sorting is driving increasing levels of social polarization, which includes partisan prejudice, political activism, and anger

(Mason, 2015). This is a distinct type of polarization, affecting judgment, action, and emotion, as opposed to the rational assessments of policy outcomes that characterize issue polarization. We believe this distinction is particularly useful because separating social polarization from issue polarization allows scholars to more precisely examine the nature of polarization generally, and the outcomes of sorting specifically. In particular, it runs contrary to claims that partisan-ideological sorting and polarization are one and the same (Fiorina, Abrams, Pope, 2005, 2008; Fiorina and Levendusky,

2006), while also addressing ongoing debates in political science literature over the very nature of polarization itself (e.g. see Abramowitz, 2010 versus Fiorina, Abrams and Pope, 2005, 2008). We argue here that the social elements of this polarization are capable of motivating voters to purify their voting choices, leading to a form of

“electoral polarization” in which increasingly sorted voters are decreasingly capable of bringing themselves to vote for a member of the opposing party, no matter how familiar they may be.4

It should be made clear, here, that this view of partisan-ideological sorting also relies on a conceptualization of ideological identities as social identities, distinct from a purely rational set of policy positions. This view is consistent with and draws upon recent research by Ellis and Stimson (2012), who differentiate between symbolic ideology—what we like to call ourselves—and operational ideology—our actual set of policy positions. By treating (symbolic) ideology and partisanship as social identities,

4 Iyengar, Sood, and Lelkes (2012) go so far as to characterize the significant out-group antipathy that some partisans have for their counterparts as “loathing.” If partisans come to think of out-group members with this kind of distaste, then there should be meaningful practical (electoral) repercussions.

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we argue that the polarizing effects of partisan-ideological sorting can be understood apart from traditional conceptualizations of “mass polarization” that rely, for example, upon assessments of issue positions (e.g. Abramowitz, 2010; Fiorina, Abrams and Pope

2005, 2008). As partisan and ideological identities grow more aligned, individual voters experience a psychological imperative to cling to their partisan groups, independently of rational influences such as policy attitudes or the incumbent status of a candidate.

Roccas and Brewer (2002) have found that when two social identities (such as party and ideology) are well-aligned, or sorted, the perceived difference between one’s own group and outsiders increases. As identities move into alignment, individuals feel defined by a more narrow range of groups, and find it more difficult to view outsiders in an unbiased way. With this research in mind, incidents of ticket-splitting should be far less common among highly sorted individuals, as sorting causes partisans to view the opposing party in increasingly biased ways. This bias should make it difficult for a sorted partisan to pull the lever for a candidate from the opposing party because their own party takes up a larger portion of their self-concept, which prevents any association, even a secret one behind the curtain of a ballot box, with an opposing party.5

What we propose to examine in this paper, then, is a third effect of sorting, a more specifically electoral polarization.6 The voting booth is arguably the distilled

5 Moreover, we expect to find this effect even in lower level elections, in which the relationship between voters and candidates has the potential to be more personal (Fenno, 1975). 6 Levendusky (2009) has demonstrated evidence of a link between sorting and ticket- splitting at the presidential level between 1992 and 1996. The present project departs from this work, however, on three fronts. First, the operationalization of sorting here accounts for more than just the overlap or matching of partisan and ideological identities; we treat sorting as a concept that comprises both the strength of those identities and the overlap between them. Second, we parcel out actual changes in sorting from 1992 to 1996 in order to demonstrate how different groups—those whose sorting increases, stays static, or decreases—modify their voting behavior. Third, we

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endpoint of the social and issue polarization that have been growing in American politics, and, by examining the effect of sorting on ticket-splitting, we attempt to assess the final, electoral result of sorting. Simply, as the number of voters who are willing to split their ballots declines, the bipartisanship of voters should decline as well. If sorting represents the integration of an individual’s partisan and ideological identities, then overlap between these identities should have clear electoral consequences related to candidate choice. Specifically, because these highly-integrated identities underscore significant, affective partisan attachments that preclude virtually any association with an opposition party, we argue that highly-sorted individuals should display an extraordinarily low propensity to cast a split-ticket for both the national and state- level ticket pairings. Further, because the sorting of political identities is an inherently dynamic phenomenon, we hypothesize that an increase in an individual’s degree of partisan-ideological sorting over time should translate to a decreased propensity to split a ticket.

Data and methods

The data employed in the following analyses are drawn from three sources: the

1972-2012 American National Election Studies (ANES) Time-Series surveys, the 2010

Cooperative Congressional Election Study (CCES) Common Core Content, and the

1992-1996 ANES Panel Study.

1972-2012 AN ES Time-Series

Dependent variable: The House-President split-ticket. We operationalize a split-ticket as casting a vote for different parties across two offices. Here, we use self- reported vote choice for the House of Representatives and President, where casting a

examine the effect of sorting on ticket-splitting across a far greater number of ticket- pairings at both the national and subnational levels.

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vote for a Republican House candidate and Democratic President or a Democratic

House candidate and Republican President is coded 1. A straight ticket—voting for the same party for both offices—is coded 0.

Independent variable: Partisan strength. The strength of an individual’s partisan identity is operationalized by folding the traditional seven-point partisan identification variable in half.7 Pure independents are coded 0, leaning partisans are coded 1, weak partisans are coded 2, and strong partisans are coded 3. This variable is then rescaled to range from 0 (pure independents) to 1 (strong partisans).8

Independent variable: Partisan-ideological sorting. Our measure of partisan- ideological sorting encompasses both the degree to which an individual’s ideological self-placement matches their partisanship and the strength of those identities.

Following Mason (2015), we operationalize sorting by first creating an identity alignment score. This measure is generated by taking the absolute difference between a respondent’s self-reported partisanship and ideology, which are both coded as seven- category variables ranging from 1 (extreme liberal / strong Democrat) to 7 (extreme conservative / strong Republican). Because this generates scores in which a high degree of overlap is represented by low values and a low degree of overlap by larger ones, this variable is reverse-coded so that a higher value represents a perfect ideology-party

7 Research by Huddy, Mason, and Aaroe (2015) demonstrates that this folded 7-point variable is a limited measure of a social identity, and underperforms a more complex social identity-based index measure in predicting political outcomes. Ideally, social identities should be measured using at least a four-item index as used in Huddy et al (2015), in order to provide insight into the great variance that exists between levels of identity strength. The measures available in our datasets force us to use the more limited measure of identity strength, and this almost certainly limits the power of the identity-based measures in predicting political outcomes. 8 We note that simple ideological strength is not included alongside partisan strength because the measure of policy balancing employed in the analyses provides a more accurate representation of the effect of ideological strength. To include a folded ideology measure would be redundant (c.f. Mulligan, 2011).

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match and low values represent discordant matches between partisanship and ideological self-placement. We then account for the strength of the attendant identities by multiplying a respondent’s identity alignment score by folded measures of her partisan and ideological strength. This is done to account for the fact that the alignment of identities is unimportant if the respondent does not identify with one or both groups. The result is that low values correspond to weak identities (e.g. moderate

/ Independent), medium values to conflicted or cross-cutting identities (e.g. weak conservative / weak Democrat), and high values to overlapping, strong identities (e.g. extremely conservative / strong Republican). In order to make the values on this scale more intuitive, we rescale this variable to range from 0 (unsorted) to 1 (fully sorted).9

Alternative explanations for ticket splitting. We employ a number of covariates that control for alternative explanations of ticket-splitting. First, Burden and Kimball

(2004) provide convincing evidence that out-party incumbency is positively related to the propensity of casting a split-ticket. Because incumbents enjoy name recognition, are often more professional, and are generally better funded (Hogan 2004), incumbency may function as a type of heuristic device that encourages some voters to defect.10 In other words, out-party incumbency should have a positive effect on the likelihood of casting a split-ticket. This variable is coded 1 for an incumbent who does not belong to the respondent’s party and otherwise 0.

Second, we include the Carsey and Layman (2004) measure of policy-balancing because prior research demonstrates that individuals may use spatial reasoning when

9 For illustrative purposes, the full distribution of this variable is presented in Figure A1 in the Appendix. 10 Relatedly, we note that out-party defection within the split-ticket vote pairing is largely driven by an out-party Congressperson vote. Table A4 in the Appendix demonstrates that respondents defect to an out-party House candidate at more than twice the rate than they defect to out-party presidential ones. In part this is undoubtedly due to the highly visible office of the presidency driving a unique form of party-voting. As the “leader” or “prototype” (Hogg 2001) of their partisan team, it is simply much less likely for individuals to eschew their in-party presidential candidate.

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approaching multiple vote choices. This measure conveys both the degree to which an individual is ideologically polarized and the degree to which an individual sees the parties as ideologically (dis)similar. Values range along a continuum of 0, where individuals perceive the parties as fully polarized and also locate themselves at one of the ideological extremes, to 1, where individuals see the parties as fully polarized yet locate themselves at the midpoint between two groups.11 As values transition from 0 to 1, the likelihood of splitting a ticket should increase because individuals do not perceive the parties as ideologically-divergent and are themselves ideologically moderate. Perceiving the parties as offering a difference without distinction, casting a vote for a candidate under these conditions would be done in a quasi-random manner

(which should, consequently, increase the prevalence of split-tickets).

Third, we include partisan indifference, which captures whether or not an individual possesses any form of positive or negative affect toward the parties. Prior research indicates that individuals who are indifferent toward the parties are likely to split-their tickets, having few affective attachments to anchor their preferences (Davis,

2014). Individuals who neither like nor dislike anything about the Democratic and

Republican parties are coded 1 and otherwise 0.

Fourth, individuals who are highly knowledgeable about politics are more likely to have distinct political preferences (Abramowitz and Saunders, 2008), and may, therefore, be less likely to split their ticket. The only acceptable political knowledge item available for each of the survey years over the entire span of the ANES is a

11 Specifically, the equation used to produce balancing scores is: Party balancing = abs(Rideo - Cideo) – abs(Rideo – (GOPideo + Dideo)/2), where: Rideo = respondent’s self- placement within ideological space; Dideo = respondent’s placement of Democratic

Party within ideological space; GOPideo = respondent’s placement of Republican Party within ideological space; Cideo = the value of Dideo or GOPideo that most closely approximates the respondents ideological self-placement, Rideo

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question that asks respondents to name the party that controls the House of

Representatives. Correctly answering this question is coded 1 and otherwise 0.12

Fifth, issue extremity—a form of attitudinal polarization that accounts for the strength of attitudes across a host of standard policy items (e.g. the propriety of government health insurance, aid to blacks, government provision of jobs, defense spending, and abortion) —is employed because individuals with stronger policy opinions are more likely to possess one-sided political preferences (Lavine, Johnston, and Steenbergen 2012). In turn, we expect these people to split their tickets with less frequency than individuals who possess extremely weak policy preferences. This variable folds respondents’ policy attitudes in half such that neutral opinions are coded

0, weak opinions are coded 1, and strong opinions are coded 2. These items are then added together, averaged, and recoded to form a scale that ranges from 0 “neutral opinions on policy issues” to 1 “very strong policy opinions.” Importantly, the inclusion of this variable allows us to test whether the effect of sorting is independent of strong policy attitudes, which gives us leverage over the social versus issue polarization debate.

Controls. We employ a number of control variables including dichotomous variables for race (white, black) and southern region (states included in the original

Confederacy), as well as yearly fixed effects to account for unmeasured variance over time. Finally, we control for education, which is a six-part variable that ranges from

“elementary,” coded 0, to “post-graduate,” coded 1.13

12 Clearly a more finely-tuned knowledge index would be preferable; unfortunately, however, office recognition items are not asked until 1988, which would significantly truncate the available data. Nevertheless, more limited analyses employing this alternative specification of knowledge return results that are effectively indistinguishable from those presented below. 13 In addition, we provide alternative specifications of the models using ANES Time- Series data in the Appendix that include additional demographic controls. The inclusion of these variables results in virtually no substantive changes to the effect of sorting across model specifications.

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2010 CCES

Dependent variable: The split-ticket. Again, we operationalize a split-ticket as a vote pairing wherein an individual casts a vote for a Democrat for one office and a

Republican for another; a split-ticket is coded 1 and otherwise 0. We model a number of different ticket pairings that include Governor-US Senator, Governor-US

Representative, Governor-State Representative, and Governor-State Senator split- tickets.

Independent variables: Partisan strength and sorting. Both the strength of an individual’s partisan identity and partisan-ideological sorting are operationalized using the schemes described in the previous section.

Alternative explanations for ticket splitting. The CCES provides limited incumbency data for Governors and US House and Senate legislators, so we include appropriate dummy variables for an incumbent office holder where applicable, coded

1, and otherwise coded 0. Next, although there is neither a knowledge nor a political interest item available for the Common Content data that we utilize, we employ a variable that captures interest in “news and public affairs” as a proxy for the degree to which an individual is aware of political environment (ostensibly more interested individuals should be less likely to cast accidental or thoughtless split-tickets). This variable ranges from 0 (“hardly any interest”) to 1 (“interested most of the time”).

Finally, we include a policy-balancing variable that is operationalized according to the operationalization in the previous section.

Controls. For these analyses, we include education (ranging from “no high school,” coded 0, to “post-graduate,” coded 1) and dichotomous variables for race

(white, black) and southern region (states included in the original Confederacy).

1992-1996 ANES Panel Study

Dependent variable: Change in ticket-splitting. Although our main thesis is that a high degree of sorting should be related to depressed ticket-splitting, our argument

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rests on the assumption that becoming more highly-sorted should cause individuals to split their tickets less often. To capture changes in ticket-splitting behavior over time, we subtract whether an individual split their House-President vote pairing in 1996 from whether they split their ticket in 1992. Resulting values are coded -1 for a change from a split-ticket to a straight ticket, 0 for “no change” in vote choice pairings across offices, and 1 for a change from a straight ticket to a split-ticket. Thus, a “decrease” in ticket-splitting will be associated with negative values, no change in ticket-splitting with zero, and an “increase” in ticket-splitting with positive values.

Independent variable: Change in partisan-ideological sorting. We have previously outlined that sorting ranges from 0, weak, unmatched partisan-ideological identities, to 1, strong, congruent partisan-ideological identities. We measure the change in an individual’s sorting from the 1992 to 1996 elections by subtracting at individual’s sorting score in 1996 from their score in 1992. Within the resulting distribution of scores, positive values represent an increase and negative values a decrease in sorting. To simplify this distribution and the interpretation of these scores, we code individuals whose sorting decreased -1, those whose sorting stayed unchanged

0, and individuals whose sorting increased 1. We then analyze simple difference of means on the change in ticket-splitting across these groups.14

Analysis and discussion

1972-2012 CANES: National-level ticket-splitting

When we examine a simple comparison of the rates of ticket-splitting across partisan strength and sorting scores (Figure 1), we find that split-ticket voting substantially decreases as individuals transition from independent identifiers to strong partisans.

14 Due to the panel nature of the data, demographic controls are unnecessary; however, we do provide a full regression in Table A6 in the Appendix that models changes in split-ticket voting as a function of a change in sorting plus controls. Predictably, the results replicate.

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About thirty-five percent of independents in this sample cast a split-ticket vote, while only 11 percent of strong partisans do so. This general pattern persists across values of sorting, where again roughly 35 percent of those with the most cross-cutting partisan and ideological identities cast a split-ticket vote, but only 5 percent of the most sorted do so. The main difference of note is that fully sorted individuals (extreme ideologues and strong partisans) appear to split their tickets almost 50 percent less than those who simply identify as strongly partisan.

To analyze the relative predictive capacity of these two variables, Table 1 presents two models where the likelihood of casting a split-ticket is modeled as a function of the strength of an individual’s partisan identity (Model 1), sorting (Model

2),15 and a series of controls.16 Beginning with the control variables, we find broad support for a number of prevailing explanations for ticket-splitting behavior. While incumbency and indifference have positive effects on an individual’s propensity to cast a split ticket, black individuals, those who see themselves as moderate and the parties as ideologically indistinguishable, more knowledgeable voters, and persons with strong issue opinions are less likely to cast a split ticket.

Turning to our main quantities of interest, Model 1 clearly indicates that the strength of an individual’s partisan identity has a large negative and statistically significant effect on the likelihood of a split-ticket vote. Because log-odds coefficients are not intuitively interpretable, however, Column 2 translates these logistic regression coefficients into marginal effect estimates. Here, the marginal effect of transitioning from being a pure independent (coded 0) to being a strong partisan (coded 1) results in a reduction of about 19 percentage points in the likelihood of casting a split-ticket,

15 Partisan strength and sorting must be included in separate models because it would make very little conceptual or empirical sense to attempt to hold “partisan strength” constant when examining the marginal effect of sorting, which itself is partially created from an individual’s partisan strength value. 16 Fixed effects variables for “year” are also included in these models, although they are not presented here. Table A2 in the Appendix presents these estimates.

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where the probability of casting a split-ticket at the strongest level of partisan identity is roughly 11 percent.

This is an impressive decrease, although it is roughly thirty percent smaller than the effect of moving across the full range of sorting values from completely unsorted (independent / moderate) to fully sorted (extreme ideologue / strong partisan). As Model 2 indicates, we observe that the coefficient for sorting has a stronger negative effect on an individual’s propensity to cast a split-ticket. This translates into a 28 percentage point reduction in the likelihood of casting a split-ticket when holding other covariates at their respective means. In comparison to strong partisans, these fully-sorted individuals are predicted to cast a split-ticket a scant four percent of the time. Simply put, the convergence between strong partisan and ideological identities produces far less split-ticket voting than does the strength of partisan identity alone.

2010 CCES: Sub-national ticket-splitting

Although we have demonstrated that sorting appears to be the strongest determinant of an individual’s propensity to engage in ticket-splitting in the context of the traditional House-President ticket-pairing, an individual does not exclusively vote for these two candidates in an election. Thus, we utilize the 2010 Cooperative

Congressional Election Study (CCES) to examine ticket-splitting behavior across a wider variety of ticket pairings at the subnational level. Table 2 presents four different ticket pairings that include Governor-US Senate, Governor-US House, Governor-State

Senate, and Governor-State House candidates. These sets of analyses are broken into two models that examine the effect of partisan strength (Model 1) and sorting (Model

2) on the likelihood of casting a split-ticket.

Consistent with the results presented in the pooled ANES Time Series analyses, we find that partisan strength has a consistently robust, negative and statistically significant effect across each set of ticket pairings. However, as the “Model 2” in each

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analysis indicates, the effect of sorting appears to be substantially larger than that of partisan strength. Figure 2 facilitates substantive comparisons between the effects of these coefficients by illustrating marginal effect changes in the dependent variable. As the results indicate, sorting has a substantially larger negative marginal effect on ticket- splitting than simple partisan strength for each pair of candidates. Transitioning from the minimum to maximum value of partisan strength results in a decrease in the likelihood of casting a split-ticket that ranges anywhere from about five and a half to eight points, while moving from the minimum to maximum value of sorting results in a decrease that ranges between ten and fourteen points. Thus, although the predicted probability of casting a split-ticket is relatively equal for both pure independents

(partisan strength = 0) and the unsorted (sorting = 0), transitioning to the maximum values on these variables produces clearly differentiated results. On average, the predicted probability of casting a split-ticket for fully-sorted individuals is about twice as low as for strong partisans across each pairing.17

1992-1996 ANES Panel Data

Across the analyses provided, we have demonstrated that an individual’s likelihood of casting a spilt-ticket is significantly related to their degree of partisan- ideological sorting. However, the underlying causal mechanism still remains unexplored insofar as we have not yet answered whether changes in convergence between an individual’s partisan and ideological identities is behaviorally-consequential.

Specifically, can changes in the degree to which an individual’s partisan and ideological identities are sorted explain ticket-splitting behavior?

17 The careful reader will observe that the likelihood of splitting a ticket is, in general, much lower than the ticket-splitting rates observed in the ANES Time-Series data. This is an interesting commentary on the state of bipartisanship within the ballot box insofar as even relatively unsorted individuals are extremely unlikely to cast split- tickets at the subnational level.

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To answer this question, we turn to the 1992-1996 ANES Panel Study,18 which covers a period of time in which political identities were in considerable flux and sorting significantly increased among the general public.19 Here, we analyze the change in ticket-splitting among different groups of individuals whose partisan strength and degree of sorting decreases, stays constant, or increases over time. Importantly, we mean-center these changes, to account for the average change in ticket-splitting that occurred across the sample as a whole during this time. The scores reported for each group therefore represent the difference between each group and average citizens in their ticket-splitting patterns, which allows us to demonstrate whether the changes among each group are simply indicative of a general trend across the population, or if they are meaningfully distinct.20 Figure 3 portrays the mean change in ticket-splitting for each group minus the average change in ticket-splitting that occurred between the two waves.

Looking first at the change in ticket-splitting across variation in the strength of an individual’s partisan identity, we observe that the confidence intervals for the estimated change in ticket-splitting for each of these groups include zero. Although we would hypothesize that a decrease in an individual’s partisan strength should translate to an increase in ticket-splitting and vice versa, these estimated changes are insignificant as they each contain zero within their respective 95 percent confidence

18 Ideally, we would also be able to use the later 2000-2004 ANES Panel Study. Unfortunately, however, the ideology item used to construct our sorting variable is inexplicably not included in the 2004 Panel Wave. 19 The mean sorting score in 1992 was 0.25; in 1996 this increased to 0.28, a substantively and statistically significant increase (t = -2.77). Sorting scores increased between 1992 and 1996 for 40 percent of the panel. 20 We also provide a full regression model of a “change” in split-ticket voting from 1992 to 1996 in Table A6 in the Appendix, which includes a full bevy of control variables. Predictably, an increase in sorting has a negative effect on the likelihood an individual will switch from straight to split-ticket voting.

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intervals. Moreover, the overlap between the confidence intervals of these groups is considerable, and additional t-tests bear out that the group changes in ticket-splitting are not significantly different from one another. Thus, these simple tests suggest that changes in ticket-splitting from 1992 to 1996 are not directly attributable to changes in the strength of an individual’s partisanship alone, once we account for general trends in ticket-splitting. In other words, changes in the strength of an individual’s partisan identity do not affect ticket-splitting beyond what is occurring in the population as a whole.

As we transition to the effect of a change in sorting on the change in ticket- splitting, however, the results closely match our expectations. Here, the positive effect of a decrease in sorting from 1992 to 1996 on ticket-splitting is weakly significant in addition to being significantly different from individuals who increased in sorting (a difference of roughly 20 percentage points between categories). Simply, as individuals become less sorted, their votes more easily cross party lines, and this effect goes significantly against the general trend in the public. Conversely, we find that an increase in partisan-ideological sorting from 1992 to 1996 is significantly related to an

11 percentage point decrease in ticket-splitting behavior. As we predicted, individuals whose partisan and ideological identities converged were less likely to split their tickets, and importantly, they were even less likely to do so than a typical voter, whose levels of ticket splitting had also declined during this period. The individuals whose partisan and ideological identities moved into alignment between 1992 and 1996 shifted toward electoral purity faster than the rest of the nation. In sum, when strong partisan and ideological identities coalesce, a citizen’s ability to understand herself as anything but a member of her own group is restrained and her self-concept reduced in size and scope.

This can lead to knee-jerk choices for the in-party candidate, even when many other forces would compel an individual to keep an open mind.

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Conclusion

Although interest in the phenomenon of split-ticket voting has created a cottage industry of research within both academia and the popular press, this literature has not fully considered how sorting may affect an individual’s propensity to cast a split- ticket. We find that although the probability that an average individual will cast a split-ticket is relatively low in general, ticket-splitting is remarkably rarer for the fully- sorted individual. Contextualizing these results within the larger literature on voting behavior, although the effect of simple partisan strength remains a strong determinant of ticket-splitting behavior at both the presidential and subpresidential levels, we find compelling evidence that sorting provides a more complete picture of how an individual's partisan and ideological identities work in concert to affect electoral decision-making. Moreover, we note that the size of sorting’s negative effect on ticket- splitting surpasses all other prevailing institutional or psychological explanations of this voting behavior (e.g. Burden and Kimball 2004; Mulligan 2011; Davis 2014).

Perhaps our most important contribution to this literature, however, is that we demonstrate that an increase in sorting, which is ultimately a dynamic process of identity alignment, is causally related to decreased rates of ticket-splitting.21

These findings also have broader implications for the study of both political identities and “polarization” within the mass public. First, if intolerance of out-group members increases as cross-pressures within an individual’s social identities wane

(Brewer and Pierce 2005), then convergence between ideological and partisan identities should reduce an individual’s propensity to cast an out-party vote. As the psychological self-concept narrows, voters are unable to bring themselves to cross party lines. This not only lends further credibility to conceptualizing partisanship as a social identity, but provides tangible evidence that sorting has meaningful behavioral consequences.

21 Although we do not possess long-term panel data to test this effect over more than two presidential elections, this snapshot of sorting from 1992 to 1996 helps better illustrate the important behavioral consequences that an increase in sorting produces.

Sorting and the Split-Ticket

Second, sorting has been identified in prior research as a force for increasing social and

“affective” polarization in the American public (Mason 2015). Simply, as sorting increases, Americans increasingly dislike and distrust each other on a social level— even more than they disagree on policy outcomes. Although a great deal of research has devoted itself to a debate over the nature of mass polarization (Abramowitz 2010;

Fiorina, Abrams and Pope 2008; Iyenger et al. 2012), our contributions demonstrate that social polarization has meaningful electoral consequences. If sorting is capable of increasing polarization within the ballot box by driving individuals to think of themselves as (tribal) party members, and less as “Americans” who are open to the most qualified candidates, then the likelihood that voters will prefer a mix of legislators falls precipitously. This distinctly electoral polarization is the final outcome of the social and psychological motivations that arise from an increasingly sorted electorate.

A sorted electorate is not only more socially divided, it is also increasingly rigid in the decisions it makes regarding the future of American government.

Sorting and the Split-Ticket

References

Abramowitz, Alan. 2010. The Disappearing Center: Engaged Citizens, Polarization, and American Democracy. New Haven: Yale University Press. Beck, Paul. A., Baum, Lawrence., Clausen, Aage.R., and Smith, Charles .E. 1992. Patterns and Sources of Ticket Splitting in Subpresidential Voting. American Political Science Review, 86, 916–28. Berelson, B. R., Lazarsfeld, P.F., & McPhee, W.N. (1954). Voting: A Study of Opinion Formation in a Presidential Campaign. Chicago: University of Chicago Press Burden, B. C. & Kimball, D.C. (2004). Why Americans Split Their Tickets: Campaigns, Competition, and Divided Government. University of Michigan Press. Burden, B. C. (Ed.) (2002). Uncertainty in American politics. Cambridge: Cambridge University Press. Brewer, Marilynn B. and Kathleen P. Pierce. 2005. “Social Identity Complexity and Outgroup Tolerance.” Personality and Social Psychology Bulletin, 31, 3, 428- 437. Campbell, A. & Miller, W.E. (1957). The Motivational Basis of Straight and Split- Ticket Voting. American Political Science Review, 51, 293-312. Campbell, Angus, Philip E. Converse, Warren E. Miller, Donald E. Stokes. 1960. The American Voter. New York & London: John Wiley & Sons, Inc. Davis, Nicholas T. 2014. “The role of indifference in split-ticket voting.” Political Behavior. http://link.springer.com/article/10.1007/s11109-013-9266-9 Ellis, Christopher, and James A. Stimson. 2012. Ideology in America. 1st ed. Cambridge University Press. Fenno, Richard F., Jr. 1975. "If, as Ralph Nader Says, Congress Is 'The Broken Branch, How Come We Love Our Congressmen So Much?" in Norman J. Ornstein, ed., Congress in Change: Evolution and Reform. New York: Praeger, pp. 277- 287. Fiorina, Morris P. 2009. Disconnect: The Breakdown of Representation in American Politics. University of Oklahoma Press. Fiorina, Morris P. and Matthew S. Levendusky. 2006. “Disconnected: The Political Class versus the People.” In Red and Blue Nation? Characteristics and Causes of America’s Polarized Politics, ed. Pietro S. Nivola and David W. Brady. Washington, D.C.: Brookings Institution Press, 49-71. Fiorina, Morris P.; Samuel J. Abrams and Jeremy C. Pope. 2008. “Polarization in the American Public: Misconceptions and Misreadings.” The Journal of Politics 70(2): 556–560. Green, Donald, Bradley Palmquist and Eric Schickler. 2002. Partisan Hearts and Minds. Yale University Press.

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Greene, Steven. 1999. “Understanding party identification: A social identity approach.” Political Psychology 20: 393-403.

Greene, Steven. 2002. “The Social-Psychological Measurement of Partisanship.” Political Behavior 24: 171-197. Greene, Steven. 2004. “Social identity theory and Party Identification.” Social Science Quarterly 85(1): 138-153. Hogan, Robert. “Challenger Emergence, Incumbent Success, and Electoral Accountability in State Legislative Elections.” Journal of Politics, 66(4): 1283- 1303. Hogg, Michael A. 2001. “A Social Identity Theory of Leadership.” Personality and Social Psychology Review 5 (3): 184–200. Huddy, Leonie. 2001. “From Social to Political Identity: A Critical Examination of Social Identity Theory.” Political Psychology 22(1): 127-156. Huddy, Leonie, Lilliana Mason, and Lene Aaroe. 2015. “Expressive Partisanship: Campaign Involvement, Political Emotion, and Partisan Identity.” American Political Science Review 109(1): 1-17.

Iyengar, Shanto, Gaurav Sood, and Yphtach Lelkes. 2012. “Affect, Not Ideology A Social Identity Perspective on Polarization.” Public Opinion Quarterly 76(3): 405–431. Lavine, Howie., Johnson, Christopher.D., and Marco R. Steenbergen. 2012. The Ambivalent Partisan: How Critical Loyalty Promotes Democracy. Oxford University Press. Levendusky, Matthew. 2009. The Partisan Sort: How Liberals Became Democrats and Conservatives Became Republicans. Chicago: University Of Chicago Press. Mason, Lilliana, Leonie Huddy and Lene Aaroe. 2011. “The Power of Partisan Identity in Active Political Times.” Annual Meeting of the Midwest Political Science Association, Chicago, IL. Mason, Lilliana. 2013. “The Rise of Uncivil Agreement: Issue versus Behavioral Polarization in the American Electorate.” American Behavioral Scientist 57(1): 140– 159. Mason, Lilliana. 2015. “‘I Disrespectfully Agree’: The Differential Effects of Partisan Sorting on Social and Issue Polarization.” American Journal of Political Science 59(1): 128-145. Mulligan, Kenneth. (2011). Partisan Ambivalence, Split-ticket voting, and Divided Government. Political Psychology, 32(3), 505-530. Roccas, Sonia and Marilynn Brewer. 2002. “Social Identity Complexity.” Personality and Social Psychology Review 6(2): 88-106. Stonecash, Jeffrey M. 2005. Political Parties Matter: Realignment and the Return of Partisan Voting. Lynne Rienner.

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Tajfel, Henri and John Turner. 1979. “An integrative theory of intergroup conflict.” In The Social Psychology of Intergroup Relations, eds. W. G. Austin and S. Worchel. Monterey, CA: Brooks/Cole.

Sorting and the Split-Ticket

Table 1. Logistic Regression and Marginal Effect Estimates for House-President Split-Ticket Voting, 1972-2012 Marginal Marginal Model 1 Model 2 effect effect

Party strength -1.40** -0.19** ------(0.10) (0.01) Sorting ------2.29** -0.28** (0.16) (0.02) Out-party incumbent 1.15** 0.18** 1.21** 0.18** (0.09) (0.02) (0.12) (0.02) Indifferent 0.13 0.02 0.18 0.02 (0.09) (0.01) (0.11) (0.01) Policy-balancing 0.78** 0.10** 0.44* 0.05 (0.18) (0.03) (0.20) (0.02) Issue extremity -0.39** -0.05** -0.29* -0.04** (0.08) (0.01) (0.13) (0.02) Knowledge -0.21** -0.03** -0.23** -0.03** (0.05) (0.01) (0.04) (0.01) Education 0.00 -0.00 0.18 0.02 (0.13) (0.02) (0.14) (0.02) White 0.09 0.01 0.07 0.00 (0.11) (0.01) (0.11) (0.01) Black -0.47** -0.05** -0.62** -0.06* (0.18) (0.02) (0.18) (0.01) South 0.41** 0.06** 0.43** 0.06** (0.11) (0.02) (0.12) (0.02) Constant -2.05** -2.38** (0.14) (0.11) Psuedo-R2 0.1079 0.1251 N 12,779 10,329

Source: 1972-2012 Cumulative ANES Time-Series Notes: Year fixed effects estimates not shown, see Table A1. Smaller sample size in Model 2 result of missing observations on ideological self-identification variable used to construct sorting measure. Standard errors in parentheses are clustered by year; *p<0.05, **p<0.01. Marginal effect is discrete change of variable across range of values (0 to 1).

Sorting and the Split-Ticket

Figure 1. Partisan Strength, Sorting, and Split-Ticket Voting

0.40

0.35

0.30

0.25

ticket - 0.20

0.15

% casts casts % split 0.10

0.05

0.00 Min Max

PID Sorting

Source: 1972-2012 Cumulative ANES Time-Series Notes: Columns represent percentage of respondents within category that split their tickets.

Sorting and the Split-Ticket

Table 2. Logistic Regression Estimates for Subpresidential Ticket-Splitting, 2010 CCES Governor-US Senate Governor-US House Governor-State Senate Governor-State House (1) (2) (1) (2) (1) (2) (1) (2) Party strength -1.16** ------1.20** ------1.23** ------1.27** ------(0.11) (0.13) (0.12) (0.11) Sorting ------2.32** ------2.27** -2.55** ------2.46** (0.18) (0.22) (0.21) (0.18) Incumbent Governor 0.15 0.16 0.22* 0.23* 0.07 0.09 0.13 0.15 (0.09) (0.09) (0.11) (0.11) (0.09) (0.09) (0.09) (0.09) Incumbent US House -0.11 -0.10 ------(0.12) (0.12) Incumbent US Senate ------0.05 0.03 ------(0.11) (0.11) Policy-balancing 0.84** 0.71** 1.34** 1.17** 1.10** 1.01** 0.84** 0.78** (0.15) (0.16) (0.18) (0.19) (0.18) (0.18) (0.16) (0.17) Education -0.21 -0.17 -0.04 0.01 0.32* 0.36* 0.03 0.09 (0.13) (0.14) (0.15) (0.15) (0.15) (0.15) (0.14) (0.14) Interest -1.27** -0.97** -1.3** -1.03** -1.08** -0.76** -0.87** -0.55** (0.17) (0.17) (0.18) (0.19) (0.21) (0.21) (0.21) (0.21) White 0.00 0.07 -0.06 0.00 -0.08 -0.01 0.08 0.14 (0.12) (0.12) (0.13) (0.13) (0.13) (0.13) (0.13) (0.13) Black -0.12 -0.41* 0.11 -0.17 -0.21 -0.54* -0.26 -0.59* (0.20) (0.20) (0.21) (0.21) (0.22) (0.23) (0.23) (0.23) Old South -0.11 -0.04 0.23 0.31* 0.06 0.12 -0.02 0.03 (0.10) (0.10) (0.13) (0.14) (0.11) (0.11) (0.10) (0.10) Age 0.00 0.00 0.01* 0.01* 0.01 0.01* 0.00 0.00 (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) Constant -8.53 -9.90 -17.00* -16.47* -14.15* -15.49* -8.89 -10.36 (6.03) (6.12) (7.04) (7.12) (6.82) (6.89) (6.71) (6.82) N 27,675 27,675 22,207 22,207 21,906 21,906 22,544 22,544 Notes: Standard errors in parentheses are clustered by year; *p<0.05, **p<0.01.

Sorting and the Split-ticket

Figure 2. The Effect of Partisan Strength and Sorting on Split-Ticket Voting, 2010 CCES

Notes: Predicted probabilities for partisan-strength and sorting are estimated using corresponding “Model 1” and “Model 2” from Table 2. Values range from minimum (Independent; fully unsorted) to maximum (Strong partisan; fully sorted). See Table A5 for exact values.

Sorting and the Split-ticket

Figure 3. Mean-Centered Change in Ticket-Splitting across Group Changes in Partisan Strength and Sorting, 1992-1996 ANES Panel Study

0.40

0.30

0.20 ticket) - 0.10

0.00

-0.10 ∆ in in ∆ Pr(split -0.20

-0.30 partisan sorting* partisan sorting partisan sorting** strength strength strength decrease no change increase

Source: 1992-1996 ANES Panel Study Notes: Predictions on y-axis derived by first subtracting the average overall decrease in ticket- splitting from all group changes. These values represent percentage change in ticket-splitting attributable to change within a group relative population as a whole. Significance tests represent two-group mean-comparisons against “otherwise” reference category. *p<0.05, **p<0.01, N = 227.