Abstract
Since the beginning of the new millennium, the partisan leanings and presidential voting of state white electorates have been changing. Drawing on party realignment theories and analyses of cultural politics, this paper hypothesizes that cultural issues may be the dimension along which the realignment is occurring. The empirical findings are consistent with this view. The cultural issue preferences of state white electorates are strongly related to change in partisanship from 2000 to 2016. Further, only cultural issue attitudes have become a stronger predictor of state white presidential voting over this period. The apparent effects of partisanship, economic issue attitudes, and racial attitudes have either declined over time or been substantial in some elections and less so in others.
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Notes
I use the term “white” to refer to people who are white and not of Latinx ethnicity. The focus on white state electorates is due to the divergent trends between whites and nonwhites, the greater homogeneity across nonwhite electorates, and some empirical limitations. All three reasons are discussed more fully below.
Republican party platforms “have asserted the fundamental right to life of the unborn; have criticized efforts to extend civil rights protections to homosexuals [and] have called for an expansion of public religious expression, particularly in the schools…. Democratic platforms, in contrast, have asserted the fundamental right of women to choose an abortion, have strongly supported the Equal Rights Amendment, and have called for expanded protection of the rights of homosexuals. Moreover, although there have been clear differences between the parties’ stands on cultural matters since 1980, these differences have grown ever larger over time” (Layman 2001, pp. 122–123).
For example, Miller and Schofield (2003, 2008) claim there is long-term periodicity driven by underlying instability of party competition in multidimensional space while Claassen (2015) emphasizes how demography and demographic change shape the political landscape and composition of the activist pool. of the antecedent causes, there remains agreement regarding the importance of activists to the process of party realignment.
“The processes of political change proceeds under the handicap of considerable friction. Preexisting party attachments may have a durability that contributes to the lag…. A new generation or so may be required for one outlook to replace another” (Key 1959, p. 204).
To be sure, against these two advantages is the disadvantage of not observing individual-level change. For example, while the results below clearly show that the partisan alignments of state white electorates have changed over time to come into alignment with preexisting cultural attitudes, the individual-level processes, if any, by which this has come about remain unobserved.
On the question of the micro-level causal interdependencies between cultural issue preferences and party identification, the empirical evidence is mixed. Carsey and Layman (2006) analyzes abortion policy preferences and finds that even among people for whom the abortion issue is salient, the effect of party identification on abortion policy preferences is stronger than the effect of abortion policy preferences on party identification. Goren and Chapp (2017) finds almost an exact opposite pattern. Rather than people brining their policy preferences into alignment with their party attachments, Goren and Chapp (2017, p. 124) reports that “most people ground their partisan identities in judgments about the frontline issues in the culture war [abortion and gay rights].”
As in the case of party identification, if there is a contemporaneous correlation between PRESVOTE and PID or CULT, then assessing the direction of influence becomes much more complicated.
While 16 years proves to be ample time for the analyses, it is important to note that there is nothing especially significant about 2000, either theoretically or empirically, compared to 1996, 2004, or some other starting point. The key is that the period of time encompassed (a) be long enough to reveal what, if any, realignment has taken place and (b) cover at least part of period during which cultural issues have risen to prominence in American politics.
For example, as the 2016 presidential election approached Pew (2016, p. 6) found that “[w]hile the GOP has made gains overall and among key groups…blacks and Hispanics are as likely to identify as Democrats or lean Democratic today as they were four or eight years ago.”
There is also much more homogeneity in preferences across states for African Americans compared to non-Latinx whites. In the 14 states with more than 250 African American voters, the Democratic margin of victory ranges from 70 to 93% points with a standard deviation of about 6 points. In contrast, there is much more heterogeneity among non-Latinx whites with a range of − 11 to 63 points and a standard deviation four times larger than that for African Americans, 24 points. Thus for African Americans, and possibly Latinxs, variation across states—the focus of this paper—may be less important than variation over time, especially very long periods of time, from reconstruction to the present day.
I coded party identification with the NAES data into five categories: strong Democrats (− 100), not strong Democrats, including “leaners,” (− 50), pure independents (0), not strong Republicans (+ 50), and strong Republicans (+ 100).
These polls were funded by a consortium of news organizations and conducted in all 50 states in 2000, 2004, and 2008. In 2012 and 2016 state exit polls were only conducted in selected states. Strong and not strong partisans are not differentiated in these surveys so I code party identification from the exit polls as a trichotomy with Democrats (− 100), independents (0), and Republicans (+ 100).
Like party identification from the NAES I code five categories in the CCES data: strong Democrats (− 100), not strong Democrats, including “leaners,” (− 50), pure independents (0), not strong Republicans (+ 50), and strong Republicans (+ 100).
This involved two steps. To put the CCES white state partisanship scores on the exit poll scale, I first regressed white partisanship as measured in the exit polls on white partisanship as measured in the CCES. Then, I computed the predicted exit poll partisanship based on the parameter estimates for all state-years for which CCES data was available. This rescaled version of the CCES white state partisanship scores remains perfectly correlated with the original version of the CCES measure, but its “units” are in terms of exit poll partisanship. Then I repeated the process for the NAES data. Finally, I averaged the available white state partisanship scores by year. Thus for each presidential year from 2000 to 2016 I produced a state-level measure of white partisanship based on the available survey data, all of which was scaled in the same units.
I employed the mi routine in Stata (StataCorp. 2017), using a multivariate normal model and Bayesian iterative Markov Chain Monte Carlo (MCMC) procedures. See the Appendix for further details.
See the Appendix for a discussion of two other plausibly important issues (immigration and guns) that are not included in the analysis.
Because the yearly sample sizes are smaller with the PEW data, following Highton (2011), which is also a state-level analysis (as opposed to Tesler (2012, 2016), which analyzes the individual-level data) I pool all of the PEW surveys over the period of time the four racial attitudes questions were asked from 1987 through 2000. This could put the racial attitudes measure at a disadvantage vis-à-vis the other two attitudinal measures, however as I will show the lack of much apparent effect of racial attitudes on white partisanship is not matched by a lack of apparent effect of racial attitudes on white presidential voting.
A factor analysis of all of the items (cultural, economic, racial) together confirms that there are three distinct dimensions. For one of the three extracted factors, the average loading for the cultural items is .85 compared to .06 for the economic items and .28 for the racial items. On the second factor, the average loading for economic items is .71 compared to .02 for the cultural items and .01 for the racial items. On the third factor, the average loading for racial items is .70 compared to .03 for economic items and .09 for cultural items.
Unlike the measures of state partisanship and cultural issue attitudes, constructing measures of state white economic and racial attitudes in the presidential election years between 2000 and 2016 was not possible due to lack of suitable items. While this places some limits on the analyses that may be undertaken, it does not interfere with testing the key hypotheses about long-term cultural realignment.
To investigate whether there is regional variation that might be driving the results, I reestimated the two models including interactions between the independent variables and a dummy variable coded 1 for the 11 former Confederate states and 0 for nonsouthern states. None of the interactions reach conventional levels of statistical significance and tests of the joint significance of the set of interactions (in both models) indicate that null hypothesis of no regional variation in effects cannot be rejected with confidence (p = .39 for the parameter estimates of partisanship in 2016 and p = .61 for the parameter estimates of cultural issue attitudes in 2016).
Ideally, in addition to estimating the effects of the independent variables in 2000 on the subsequent presidential elections as in Table 3, I would have also estimated a set of models where presidential vote in year (t) is regressed on previous presidential vote (t − 4) and the independent variables measured at time (t − 4). As described earlier, estimating these models was not possible. However, the Appendix provides additional estimates based on the same logic. The results reinforce those reported in Table 3.
To be sure, this is not to say the evidence showing that ordinary citizens sometimes adopt or change their preferences to those of their preferred parties is wrong (e.g., Lenz 2009, 2012; Margolis 2018a, b). However, the notion that either partisanship or issue positions are always more central may be in need of revision (Highton and Kam 2011).
The four intervals are 2000 to 2004, 2004 to 2008, 2008 to 2012, and 2012 to 2016.
A model of linear change (by state) with time fits the data better than one that also includes a higher order polynomial (time2).
In supplementary analyses I found that whether the immigration item is included or excluded has no effect on any of the substantive results.
An alternative would be to rely on the CCES for the 2012 and 2016 state white presidential vote estimates. As shown in the Appendix, the results are almost identical.
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Appendix
Appendix
Party Identification
Table 5 shows the correlation matrix for the measure of party identification across the five presidential election years. The patterns are what one would expect, namely that the correlations are all positive and substantial but smaller in magnitude as the time between measures increases. The average correlation when the time interval is 4 years is .93.Footnote 23 For 8 year intervals the average correlation is .87, and for 12 year intervals the average is .83. The only 16 year interval is from 2000 to 2016 and the correlation between state white partisanship in 2000 and 2016 is .72, indicating that only just about 50% of the variance in white state partisanship in 2016 can be accounted for by white state partisanship in 2000.
Figure 2 presents the observed data in a different fashion, but one that is more relevant for the purposes of this paper. For each of the fifty states, there is a scatterplot showing white state partisanship by year along with a line defined by regressing white state partisanship on time. As noted in the main text, several important observations emerge. First, there is substantial change across some states, but the direction and magnitude of change is far from uniform. Some state white electorates (e.g., Arkansas and West Virginia) have moved substantially in the Republican direction while others (e.g., California and New Mexico) have moved significantly toward the Democrats. Second, a simple model of linear change (by state) fits the data well. While there is some variation around the state trend lines, it is not substantial.Footnote 24
Trends in state white partisanship, 2000–2016
Issue Attitude Scales
As described in the main text, the cultural, economic, and racial issue attitude scales were all multi-item scales and constructed on the basis of factor analysis results. Those results are shown in Tables 6, 7, and 8.
Other Issues: Immigration and Guns
While the goal is create broad-based issue attitude scales that capture the most significant policy areas, some specific issues of plausible political significance are left out. In light of relatively recent events and political conflict, perhaps the two most notable exclusions in this paper are the issues of immigration and guns. First, with respect to immigration the available evidence suggests that it fits more closely with the cultural dimension than the racial dimension of issue attitudes. For example, the one immigration item in the 2000 Annenberg survey has an average state-level correlation of .75 with the six items in the cultural issue attitudes scale and .50 with the items in the racial attitudes scale. For the reasons described in the main text, I did not include immigration item in the cultural attitudes.Footnote 25
The 2000 Annenberg survey also includes two questions about gun control. They are highly correlated (.88) with each other but are not highly correlated with the items in the cultural issue scale (average correlation of .44). In preliminary analyses, I created a separate measure of attitudes about guns, but when included in the models with the other issue scales no distinct effect of preferences regarding gun control was evident. As a result, the models of partisanship and presidential voting do not include the measure of gun control issue attitudes.
State White Presidential Vote
As mentioned in the main text, I imputed some values of the presidential vote in 2012 and 2016. The imputations are based on the 2008 state white presidential votes for all 50 states, the observed values for the states with exit polls in 2012 and 2016, and the reported state white presidential vote in 2012 and 2016 from the CCES surveys.Footnote 26 To assess the quality of the estimates, I employed a procedure developed and advocated by Honaker et al. (2011), called “overimputation.” Overimputation involves (a) treating the observed values (one at a time) as if they were missing, (b) running the imputation procedure, and then (c) comparing the imputed values to the observed values. I overimputed the 59 state white presidential vote margins for which state exit polls were available in 2012 and 2016 and found that the imputation performed very well. As shown in Appendix Fig. 3, the correlation between actual and imputed values was nearly 1.0 (r = .96).
Alternative Models of State-Presidential Vote
Table 3 in the main text reports the main findings regarding the correlates of state white presidential voting from 2000 to 2016. In those models, all four independent variables (party identification and the three issue attitudes) are measured in 2000. The advantages are that they are all on the same footing—by being measured in the same year—and concerns about endogeneity are mitigated because with the exception of the estimates for presidential voting in 2000, the independent variables are measured at a point in time before the dependent variables.
An extension of the model estimated in Table 3 is shown in Table 9. In that model, lagged presidential vote is included as an independent variable for predicting state white presidential voting from 2004 to 2016. Across all four elections there is notable stability in presidential voting with the parameter estimates for lagged presidential vote ranging from .63 (2016) to 1.06 (2008). In addition, with lagged presidential vote included in the model, the coefficients for the issue attitude scales provide estimates for change in presidential voting from the previous election (Finkel 1995). The estimates for cultural issue attitudes are all positive, indicating that conservative white electorates changed more toward Republican presidential voting, especially in the 2012 and 2016 elections.
Another extension of the presidential voting model is shown in Table 10. As described in the main text it was not possible to measure economic and racial issue attitudes in the years subsequent to 2000. It was possible for partisanship and cultural issue attitudes. Thus Table 10 relates state white presidential vote to the lagged values of state presidential vote, partisanship, and cultural issue attitudes. The patterns of estimates are similar to those in Table 10, though the magnitudes appear larger for the negative effects of partisanship (in 2008 and 2016).
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Highton, B. The Cultural Realignment of State White Electorates in the 21st Century. Polit Behav 42, 1319–1341 (2020). https://doi.org/10.1007/s11109-019-09590-5
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DOI: https://doi.org/10.1007/s11109-019-09590-5