4.1 The standard measure

The standard measure of macropartisanship reflects the relative distribution of the two-party partisan identifiers at any given moment. This is simply calculated as the number of Democrats in a sample divided by the number of Democrats and Republicans in the sample multiplied by 100 (Erikson et al. Reference Erikson, MacKuen and Stimson1998; Green et al. Reference Green, Palmquist and Schickler1998; MacKuen et al. Reference MacKuen, Erikson and Stimson1989). We find that the Democratic Party held a small, consistent advantage with a median of 53.7% of the two-party identifiers. We also see that the Republican Party gained ground in the 1980s, lost ground in the middle of Clinton’s term, gained ground in the early part of the presidency of George W. Bush, but again lost ground in the Obama years (see Figure 1). That macropartisanship slightly favors the Democratic Party and appears to track with presidential approval and economic fortunes is consistent with expectations. We also see that overall macropartisanship is fairly stable, typically hovering around 54% and very rarely going below 50% or over 58%. Essentially, overall macropartisanship indicates a narrowly divided American public that makes only slight course corrections over time.

Figure 1.

Overall Macropartisanship using CBS/NYTimes Data. June 1983 to August 2016Footnote ⁷

While aggregate macropartisanship demonstrates both the overall stability and subtle shifts in the population, it fails to examine whether stability and shifts hold across racial and ethnic divisions. Using the same methodology mentioned above, we calculated macropartisanship measures for Blacks, Latinos, and Whites and plotted them over the course of our study (see Figure 2). For African Americans, we see an enormous gap in macropartisanship, with more than 90% of partisan identifiers falling in the Democratic camp. Furthermore, even though there are more than eight times as many non-Latino Whites than African Americans across all of our datasets, they have similar standard deviations. This provides prima facie evidence that African American partisanship is not only distinct from overall macropartisanship but is also more stable. This is consistent with the Dawson’s (Reference Dawson1994) contention that Black party identification is strongly predicted by linked fate, which downplays short-term forces. One of the remarkable things to observe here is that, running counter to conventional wisdom, the nomination and election of Barack Obama in 2008 produced little movement in Black macropartisanship, as group identity had already placed 9 out of every 10 African American partisans squarely within the Democratic Party. If anything, African Americans began trending even more toward the Democratic Party in the early 1990s.

Figure 2.

Macropartisanship by Race and Ethnicity. June 1983 to August 2016Footnote ⁸

For Latinos, we also find that the Democratic Party enjoys a significant advantage, though the advantage is not nearly so great, with almost 7 in 10 partisans identifying with the Democrats. We also note that Latino macropartisanship appears less stable. The passage of the IRCA in 1986 seems to have garnered the party of Reagan some additional support, though Latinos drifted back to the Democratic Party for most of the Clinton administration; this also coincides with major shocks to the party system that occurred in California, where the Republican Party targeted Latinos and paid the price in terms of a generation of lost party identifiers (Bowler et al. Reference Bowler, Nicholson and Segura2006; Dyck et al. Reference Dyck, Johnson and Wasson2012). The Republican Party again picked up support in the wake of 9/11, before Latinos moved away from the Republican Party during the Bush administration in the later years.Footnote ⁹ Democrats gained their biggest advantage in the wake of the Great Recession and the election of Obama but lost ground as the Spanish-language media and the activist community became increasingly concerned over deportations during Obama’s first term in office. However, support for the Democratic Party among Latinos appears to spike in presidential election years. The greater variation observed among Latinos is consistent with the notion that many from the Latino community lack firm roots and ties that span generations with one of the American parties, yet return to the Democratic Party in important election years, all of which is consistent with Hajnal and Lee (Reference Hajnal and Lee2011) core arguments.Footnote ¹⁰ The Democratic advantage, the volatility in Latino partisanship, and the recent strengthening of this advantage are consistent with other recent work in the field (see Hajnal Reference Hajnal2020). Regardless, Figure 2 supports our contention that Latino macropartisanship is distinct from overall macropartisanship.

For the non-Latino White population, we find a small advantage for the Republican Party. Non-Latino Whites drift away from the Democratic Party in the Reagan years consistent with the movement of White men away from the Democratic Party and the creation of the durable gender gap (Kaufmann and Petrocik Reference Kaufmann and Petrocik1999). White macropartisanship ebbs toward the Democratic Party a bit in the George W. Bush administration’s later years, and then slightly away from the Democrats during the Obama years, reflecting the thermostatic nature of public opinion trends more generally (Erikson et al. Reference Erikson, MacKuen and Stimson2002). However, all of these shifts are small. In short, non-Latino Whites and African Americans seem to shift gradually, supporting Green et al. (Reference Green, Palmquist and Schickler1998) contention that macropartisanship changes slowly, as if eroded by the wind, not quickly, as if eroded by the tide (see Figure 2). White partisans are simply far more Republican than African Americans or Latinos, at times appear to drift in the opposite direction of African Americans, and are more stable than Latino partisans.Footnote ¹¹ In short, the overall measure of macropartisanship found in Figure 1 masks important variations by race and ethnicity found in Figure 2.

4.2 Dyad ratio algorithm

While we have over 500 surveys to construct this 34 year time series of data, the inconsistent time between surveys has to be considered, and the data transformed in some way to correct for the fact that at different times the precision of our estimates differ and that there are some time windows where there are holes in the data. Stimson’s dyad ratio algorithm (1991, Reference Stimson2015, Reference Stimson2018) presents a solution for this problem. This factor-analytic algorithm compiles time series data by weighting each individual survey by how well it correlates with the latent concept—macropartisanship (Carlin et al. Reference Carlin, Hartlyn, Hellwig, Love, Martínez-Gallardo and Singer2018; Claassen Reference Claassen2019). Point estimates are then exponentially smoothed (see Stimson Reference Stimson2018 for a detailed explanation). Previous macropartisanship studies and policy mood studies used this approach in the United States (see Enns Reference Enns2016; Stimson, MacKuen, and Erikson Reference Stimson, Mackuen and Erikson1995; Erikson et al. Reference Erikson, MacKuen and Stimson2002), while comparativists use this approach to create cross-national measures of presidential approval (see Arnold et al. Reference Arnold Christian and Wiesehomeier2017; Carlin and Hellwig Reference Carlin and Hellwig2019).

Described by Stimson as an algorithm “for building a continuous regular time series from the scraps of dated survey results that are typically available for public opinion analysis,” the algorithm stores data in a sparse matrix, with variable-indexed rows and time-indexed columns (Stimson Reference Stimson2015, 1). Therefore, within each column, there is a time step with a corresponding variable value. In this case, that variable is macropartisanship as the Democratic percentage of two-party PID. The matrix is sparse because surveys are only performed every so often or clustered around certain events (like elections), so most of the time steps will have no data. A dyad is the values for the same variable at different time steps. The dyad ratio, then, is a ratio of one of these dyads, or the ratio of the value of the variable at one time step to the value at a different time step. The algorithm uses both forward and backward recursion to calculate two dyad ratios at each time step. These dyad ratio estimates in both directions are then averaged to produce an estimate that uses all the available information and that weights all the items equally. Stimson’s implementation of the algorithm also smooths the data and accounts for validity estimation.

This is important because it allows us to generate monthly, quarterly, and annual estimates of macropartisanship from 1983 to 2016, even when the frequency of polling varies considerably over time. Here, we see immense value in having 561 surveys over 34 years and we go well beyond other available estimates of macropartisanship that might be gleaned from a single number taken from the American National Election Study or the CCES.

Drawing on Stimson (Reference Stimson2018), we start by recalculating macropartisanship for the entire population; here we present the monthly version of the data. A simple line graph of our measure of macropartisanship created by Stimson’s dyad ratio algorithm sees that these data closely matche what we found in the previous section where we plotted cubic splines of the raw data (see Figure 3). In the raw data, we found the mean of macropartisanship was 53.84, while in our dyad ratio algorithm data have a mean of 53.91, meaning the Democratic Party tended to hold a small advantage. The Republican Party reached approximate parity in the late 1980s until the mid-1990s, again in the immediate wake of 9/11, and during the height of the Tea Party in 2010 and 2011. The Democratic Party held advantages early in the Reagan Administration, in the second term of the Clinton Administration, the second Bush II term, and in Obama’s second term. Again, these ebbs and flows in this measure of macropartisanship largely track with the larger literature, with Republicans gaining ground when a president of their party is popular or when a Democratic President loses popular support, while the Democratic Party gains ground under the opposite conditions.

Figure 3.

Overall Macropartisanship using Stimson’s Dyad Ratio Algorithm. June 1983 to August 2016

Now that we have an overall measure of macropartisanship, we can also use Stimson’s dyad ratio algorithm to recalculate macropartisanship by race and ethnicity (see Figure 4). Again, we see the Stimson method produces a figure that closely tracks the cubic spline plots of the raw data found in the previous section (see Figure 2). Using the dyad ratio method, we find that over 9 out of 10 African American partisans identify with the Democratic Party, with a mean macropartisanship score of 93.31, compared to 93.22 in the raw data. The small shift toward the Republican Party that took place in the 1980s to the mid-1990s was overwhelmed by a shift back to the Democratic Party starting in the mid-1990s. That stabilized even before the nomination and inauguration of Barack Obama.

Figure 4.

Macropartisanship by Race and Ethnicity using Stimson’s Dyad Ratio Algorithm June 1983 to August 2016

Latinos also follow the same pattern when we compare the data using the dyad ratio algorithm (see Figure 4) and the raw data (see Figure 2). We find a mean macropartisanship score of 68.58 for Latinos using the dyad ratio algorithm, while the raw data produced a mean of 68.49. We also see the same movements of Latinos over time, as well as the higher rate of volatility among Latinos compared with African Americans or non-Latino Whites.Footnote ¹²

Finally, we turn to the non-Latino White segment of the population. Again, the use of dyad ratio algorithm (see Figure 4) results in similar trends as when we plotted the raw data earlier (see Figure 2). The Democratic Party’s advantage among the White population evaporated during the Reagan Administration and, with the exception of the latter years of the second Bush II Administration, the Republican Party held a consistent advantage. The macropartisanship score of 46.80 in the raw data is matched by a score of 46.90 in the monthly data using Stimson’s algorithm. We can also see that non-Latino Whites do not necessarily follow the same patterns as the other groupings. While African Americans stuck with the Democratic Party during the Obama Administration and Latinos trended toward the Democrats, the White population moved toward the Republican Party. In sum, Stimson’s dyad ratio algorithm produces substantially similar results to what was seen in the plots found in the previous section and allows us to develop monthly, quarterly, and annual measures of macropartisanship by race and ethnicity.Footnote ¹³

4.3 Independents

One limitation of the preceding analysis is that it calculates macropartisanship as a relative measure of the two-party PID. This is interesting if the variation in those identifying as independent is stagnant. However, macropartisanship largely ignores the dealignment/realignment debate found in the literature (Dalton and Flanagan Reference Dalton and Flanagan2017; Hawley and Sagarzazu Reference Hawley and Sagarzazu2012; Lawrence Reference Lawrence2018). Additionally, the volatility of Latino macropartisanship and the theoretical roots of that instability, we believe that this new dataset also unveils other interesting patterns worthy of further exploration. As has been argued by Hajnal and Lee (Reference Hajnal and Lee2011), independent party identification among groups with less rooted partisanship, particularly Latinos, reflects non-partisan or apolitical origins that come from a group feeling disenfranchised and disconnected from the political process. Therefore, beyond the relative strength of voting behavior for the Democratic party as captured by the share of the two-party PID, variation in independent identification may also give us an insight into macropartisanship among Latinos.

We begin with a presentation of dyad ratio estimates of independent identification for the overall sample (see Figure 5). Unlike changes in Democratic or Republican Party identification, or even macropartisanship, independent identification is remarkably stable in the overall sample. After an initial decline, independents consistently form a bit over one-third of the sample from 1987 for about 20 years, with a mean of 36.73%. However, starting around 2007, we start to see a sustained upward trend in independent identification. For the last 5 years of our data, we find independent identification averaged 41.87%, a five-point rise from the overall average.

Figure 5.

Overall Independent Identification using Stimson’s Dyad Ratio Algorithm. June 1983 to August 2016

Next, we present the dyad ratio estimates of independent identification for African Americans, Latinos, and non-Latino Whites in Figure 6. Here, we start with the non-Latino White independent identification, as it most closely follows the overall trend, with an average of 37.21%. It also follows the initial decline, 20 years of stability, and then uptick overall the last decade of our data. The Black population consistently demonstrates lower independent identification, with an average of only 26.43%. They also demonstrate a sharp upward trend starting in around 2012, after the uptick seen in the overall population and among non-Latino Whites. Turning to the Latino population, we see a fair amount of overlap with the White population, averaging 37.72%. More importantly, we see a sharp upward trend in independent identification starting around 2008, with more than 50% of Latinos identifying as independents in the final 3 years of the surveys. This provides prima facie evidence that dealignment is a substantial trend among Latino respondents in the last decade.

Figure 6.

Independents by Race and Ethnicity using Stimson’s Dyad Ratio Algorithm. June 1983 to August 2016

This decline in party identification in both major parties is hidden in macropartisanship data, and especially hidden without considering variation by race and ethnicity. To be sure, one limitation of our data is that we do not have information on the partisan leanings of independents over the study period. As has been well established (Keith et al. Reference Keith, Magleby, Nelson, Orr and Westlye1992), independents who lean toward the Democratic or Republican parties tend to have voting behavior patterns that look much more similar to those of weak party identifiers than they do to pure independents. Therefore, we do not wish to overstate these trends, as picking “leaning Independent” instead of “weak Partisan” can be viewed as a social desirability effect (Klar and Krupnikov Reference Klar and Krupnikov2016). Still, as Hajnal and Lee (Reference Hajnal and Lee2011) have argued, independent identification means something different for Latinos and we are especially interested by the fact that as Latino macropartisanship has trended toward the Democratic Party, we also observe a higher share of independent identification. While it is beyond the scope of this paper, we believe that this merits further investigation.

5.1 The original measure

The most direct way to (1) assess the validity and (2) assess the utility of our new measure of macropartisanship is to compare it to the extended data presented by Erikson et al. (Reference Erikson, MacKuen and Stimson2002) available from Stimson’s (Reference Stimson2017) website. While in the previous section we used monthly dyad ratio calculations, in this section we use quarterly dyad ratio calculations, as the MacKuen, Erikson, and Stimson’s dataset contains quarterly measures. In Figure 7, we see that our measure of macropartisanship tracks with MacKuen, Erikson, and Stimson’s original dataset.Footnote ¹⁵ We do find that MacKuen, Erikson, and Stimson’s measure is slightly more Democratic than our measure, with means of 54.82% and 53.84, respectively.Footnote ¹⁶

Figure 7.

MacKuen, Erikson, and Stimson (MES) versus CBS/CBSNYTimes Quarterly Measures of Macropartisanship June 1983 to August 2016

In addition to comparing our overall measure of macropartisanship with the original measure created by MacKuen, Erikson, and Stimson, we also compare our measure of macropartisanship for non-Latino Whites with MacKuen, Erikson, and Stimson’s original measure. We compare the two because the literature on macropartisanship fails to account for variations by race and ethnicity and the traditional measure was developed when the White population accounted for a far larger proportion of the population. In Figure 8, we see that our measure of macropartisanship for Whites also tracks closely with MacKuen, Erikson, and Stimson’s data.Footnote ¹⁷ In sum, our measure of macropartisanship based on CBS and CBS/NYTimes polls, as well as our subset of the non-Latino White population, closely tracks MacKuen, Erikson, and Stimson’s original measure.

Figure 8.

MacKuen, Erikson, and Stimson (MES) versus CBS/CBSNYTimes Quarterly Measures of White Macropartisanship June 1983 to August 2016

5.2 Validity by comparison with the ANES

A careful reader might wonder how we are certain that our disaggregated measures of macropartisanship are actually measuring what we assert they are measuring. Here, we might be concerned that data collection techniques, particularly for interviewing Latinos, were not as good in the 1980s and 1990s as they are today. While the CBS/NYT surveys did complete Spanish interviews, we know little about their early sampling procedures and can only assume that they are a far cry from what are considered best practices for reaching the Latino population today, particularly the Spanish-speaking Latino population.Footnote ¹⁸ As a check on external validity, we check our yearly estimates of macropartisanship for White, Latinos and Blacks against existing data points available from the American National Election Studies.

Figure 9.

ANES versus CBS/CBSNYTimes Annual Measures of Macropartisanship 1983 to 2016

The ANES is suitable for this task, as they have consistently taken biennial surveys of approximately 2000 Americans since the 1990s, with many interviews taking place in person for much of the data collection period. Given the ANES is only conducted every 2 or 4 years, here we used Stimson’s dyad ratio algorithm to calculate annual measures of macropartisanship.Footnote ¹⁹ Remarkably, the data track very well with our annual estimates from CBS News and CBS/New York Times surveys (see Figure 9). For African American respondents, we observe a slight decline in the 1980s and then a small but sustained upward trend identification in the Democratic Party. Even though we are looking at a very small sample size (N = 14), we see a positive and significant correlation (r = 0.48, p = 0.08). Similarly, Latinos show a dip in Democratic Party identification during the 1980s, a return in the Clinton years, another dip in the early part of the Bush II administration, and a sustained move toward the Democrats in both datasets. Again, despite the small sample size, we find a strong statistical correlation (r = 0.78, p < 0.01). Interestingly, we find similar levels of White identification in both datasets but find the weakest statistical relationship (r = 0.28, p = 0.34). Nevertheless, starting in the later years of the Clinton era, we see White macropartisanship track much more closely in both datasets, with a small upward trend in the Bush II administration and a slight downward trend in the Obama years. Overall, a comparison of our new dataset to the ANES data provides strong evidence of external validity, particularly for African American and Latino samples.

5.3 Disaggregated macropartisanship and the known predictors of macropartisanship

Erikson et al. (Reference Erikson, MacKuen and Stimson2002) establish a series of correlates of macropartisanship—notably presidential approval (direct) and economic indicators such as consumer confidence (indirect). As a means of establishing the external and construct validity of disaggregating macropartisanship by race and ethnicity, we examine the relationships between White, Latino, and Black macropartisanship and (1) presidential approval, (2) consumer confidence, (3) political polarization, and (4) presidential administration. Our examination here reveals five central observations. First, there is strong evidence that we are presenting a new measure of macropartisanship that is valid and performs like the previous measure presented by Erikson et al. (Reference Erikson, MacKuen and Stimson2002). Second, we find evidence of a more muted relationship between consumer confidence and macropartisanship, consistent with literature finding that partisan motivated reasoning has subsumed economic voting (Duch et al. Reference Duch, Palmer and Anderson2000). Third, it appears that there is a notable exception to this trend among Latinos, whose partisanship we have shown to be the least stable/most variable; Latino macropartisanship appears most responsive to economic conditions, while macropartisanship for Blacks, and possibly for Whites, is not.Footnote ²⁰ Fourth, increased political polarization, particularly the Republican Party’s sharp shift to the right, has moved Whites to the GOP and away from the Democratic Party. Finally, we also see mixed evidence of administration effects, with the greatest effect seeming to be a substantial shift among Latinos toward the Democratic Party in the Obama years. This shift appears to have occurred even when controlling for traditional factors and polarization.

First, we follow the methodology laid out in MacKuen et al. (Reference MacKuen, Erikson and Stimson1989) to calculate the mean deviates for macropartisanship. This is simply an observed value for macropartisanship minus the mean for macropartisanship. We calculated this for overall macropartisanship, for Latinos, for Whites, and for African Americans. A score above zero signifies that Democratic Party identification was above average for the observed time period, while a score below zero indicates Democratic identification was below average.

Second, we collected monthly presidential approval data from Gallup (UCSB 2018). ^{Footnote 21} Again, we calculated the mean deviate for presidential approval, so a higher score means a president is more popular than the overall average, while a negative score means a president is less popular. Next, we lag these scores by 1 month so that last month’s presidential approval would predict this month’s macropartisanship. We also multiply this measure of approval by –1 in the case of Republican presidents so that higher approval rates for Republican presidents should lead to lower Democratic Party identification and lower macropartisanship numbers.

Third, we gathered another of the original factors linked with changes in macropartisanship, consumer confidence. Consumer confidence is designed to capture the effect of the economy on changes in partisanship. A good economy will presumably lead to not only more popular presidents, but a shift in partisanship toward the incumbent party (Erikson et al. Reference Erikson, MacKuen and Stimson1998; Green et al. Reference Green, Palmquist and Schickler1998; MacKuen et al. Reference MacKuen, Erikson and Stimson1989). The Consumer Confidence Index (CCI) is collected monthly via survey and surveys were lagged 1 month. Again, we calculated the mean deviate, so a score above zero indicates above average confidence, while a score below zero shows below average consumer confidence. Again, we multiply this economic measure by −1 for Republican administrations.

Fourth, we assembled data on political polarization. Since the advent of Nixon’s Southern strategy, the parties have increasingly diverged and Congress has become increasingly polarized (Lewis Reference Lewis2020). This led to a long process of rebooting the American political process so that liberals identified as Democrats and conservatives as Republicans (Levendusky Reference Levendusky2009). At the elite level, this polarization was asymmetrical, with Republicans in Congress moving much further to the right than Democrats moved to the left. Similarly, Poole and Rosenthal’s (Reference Poole and Rosenthal1997) well-known claim regarding a two-dimensional spatial model, where the second dimension captured social issues, including race, evaporated during the period under study (Hare and Poole Reference Hare and Poole2014; Voteviewblog 2017). Consequently, we calculate elite polarization using the median of first dimension NOMINATE estimates, where we take the median Republican score in a given congress and subtract the median Democratic score to create our measure of polarization (Lewis et al. Reference Lewis, Poole, Rosenthal, Boche, Rudkin and Sonnet2021).Footnote ²² Based on the polarization literature and the race/ethnicity literature, we expect Latinos, African Americans, and Whites will respond to this polarization accordingly, with Latinos and African Americans increasingly identifying with the Democratic Party and Whites moving to the Republican camp.

Finally, we added a series of presidential dummy variables for each administration included in our study: Reagan, Bush I, Clinton, Bush II, and Obama. Again, this harkens back to MacKuen et al. (Reference MacKuen, Erikson and Stimson1989) original model that showed substantial variation across administration. We decided not to include events dummies, given this analysis is simply designed to provide additional validation of our measures of macropartisanship.

We start with simple Pearson’s correlation coefficients, presented in Table 1. We look to see if presidential approval or CCI are correlated with overall macropartisanship, or with macropartisanship broken down by race or ethnicity. First, we see that presidential approval is a reliable predictor of macropartisanship across all groups. However, we see that it is substantially weaker for Latinos and African Americans than it is for Whites.

Table 1.

Presidential Approval, Consumer Confidence, and Macropartisanship

Pearson’s correlation coefficients. *p < 0.10, **p < 0.05, ***p < 0.01

We conducted both a Hotelling’s T-Test and a Steiger’s Z-Test to determine if any of these correlations were different from one another (Weiss Reference Weiss2011). We found that the relationship between White macropartisanship and presidential approval is significantly stronger than the relationship is for Latino macropartisanship or African American macropartisanship.Footnote ²³ The White population simply seems to respond to presidential approval cues more than other groups. Second, we see that CCI is not significant for Whites but is significant for Latinos and African Americans. For Latinos, the effect of a good economy is as predicted. Good economic times lead to shifts in macropartisanship toward the president’s party, while bad times lead to shifts away. However, for African Americans, the reverse is true. Good economic times actually move people away from the president’s party, while bad times move them toward the president. This unusual finding might be a function of who oversaw economic good times, or it might be a function of the distribution of gains during recent economic good times. Either way, this is worth additional investigation in future works. Third, we see that polarization has differential effects across racial and ethnic groups, with the correlation being positive for Latinos and African Americans and negative for Whites. As the parties diverged on policies over the last several decades, we see that African Americans moved even more sharply into the Democratic camp, Latinos moved somewhat to the Democratic camp, while Whites moved substantially toward the Republican camp.Footnote ²⁴ Taken together, presidential approval provides additional evidence for the validity of our data, while consumer confidence introduces some questions.

However, Mackuen et al. (Reference MacKuen, Erikson and Stimson1989) argue that the effect of economic performance on macropartisanship is likely to be indirect, working through its connection to presidential approval. Additionally, we cannot discount the fact that (1) the importance of economic evaluations in macropartisanship may vary by race and (2) may be muted by considerations in individuals and the political environment (see Anderson Reference Anderson2007 for a review of this literature). To consider the effect of presidential approval, consumer confidence, and presidential administration, we present two types of analysis: (1) regression models with a lagged dependent variable (Achen Reference Achen2000; Keele and Kelly Reference Keele and Kelly2006) and (2) Newey–West (Newey and West Reference Newey and West1987) models with six lags (see Table 2). The use of a lagged dependent variable is quite common in time series models given the likelihood of serial autocorrelation. Newey–West estimators not only account for serial autocorrelation but also for heteroskedasticity that is common in time series data.Footnote ²⁵ In these models, we include the variables for presidential approval, consumer confidence, and polarization but also add in presidential administration dummies. We use the Bush II administration as the excluded category as its macropartisanship scores average closest to the mean.Footnote ²⁶

Table 2.

Presidential approval, consumer confidence, political polarization, administration, and macropartisanship.

Standard errors in parentheses.

*** p < 0.01, ** p < 0.05, * p < 0.10.

These models produce interesting results. We see that presidential approval is positive and significant in all models. A popular president simply brings all racial and ethnic groups toward the president’s party, while an unpopular president hurts the president’s party. It is with economic evaluations and political polarization that differential effects appear. A strong economy matters for Latinos, may matter for Whites, but has no effect on African Americans. This is consistent with our expectations, as weaker Latino partisanship allows for a greater role for the economy. Similarly, whether it is the result of group identity or the skewed distribution of economic gains over the last several decades, African Americans appear unresponsive to economic ups and downs.

Our findings related to political polarization may be the most interesting. Polarization makes no difference for Latinos, clearly matters for Whites, and may also matter for Blacks. Once we controlled for other factors, the positive correlation between polarization and Latinos shifting to the Democratic Party evaporates. However, we see a strong, negative effect for Whites. As the associations between the Democratic Party and civil rights and the Republican Party and racial conservatism accelerated, Whites moved substantially away from the Democratic Party (see Carmines and Stimson Reference Carmines and Stimson1989; Hutchings and Valentino Reference Hutchings and Valentino2004). The effect for African Americans is positive and significant, but only in the Newey–West model. This initial analysis cannot definitively resolve the effects of polarization on macropartisanship but does provide important insights that merit study in future works.

The administration dummies produce some interesting insights. When comparing administrations to the Bush II administration, we see that Whites and Blacks were a bit less Democratic during the Bush I and Clinton administrations, though the effect for Blacks is only significant in the Newey–West model. Interestingly, the Obama administration may have been a boon to Democrats among Latinos and Whites, but not among African Americans, with positive and significant coefficients in the Newey–West models. For Whites, it may just mean that Obama was able to make small gains among Whites despite the Republican Party’s gains related to polarization. Again, this merits less exploratory analysis in future works.