Clarifying the Relationship between Partisanship and 2016 Vote Choice with Panel Data

When looking at polling crosstabs with party identification breakdowns of key variables like vote choice and approval ratings, many often conclude a strong impact of partisanship. This data shows that Democrats largely express support for Democratic candidates in elections and leaders, while Republicans express support for their candidates and leaders. Yet conclusions about this relationship could suffer from the issue of endogeneity (I’ve discussed this before here). Inferences about how partisanship affects an outcome Y need to assume stable partisanship, and that the outcome Y–such as vote choice–does not in turn affect partisanship. The possibility of reverse causation would mean that people first arrive at a decision to vote for Donald Trump, for example, and subsequently update their partisanship to match with their candidate preference. If this occurs, then the previously assumed “exogenous” nature of partisanship as an unmoved predictor becomes dubious. Maybe individuals who were originally Republicans but did not support Trump changed their partisanship, and as a result, partisanship became a mere reflection of vote choice, and not a stable underlying predisposition in a way that would be meaningful.

A similar concern has been raised regarding approval rating polls showing strong intra-party support for Trump. Original party base members may no longer identify as Republicans on surveys, and thus Republican party identification simply comes to mean support for Trump and not a meaningful underlying political trait. Cross-sectional survey data cannot overcome this problem, as it lacks a measure of an individual’s preexisting partisanship. Panel data, on the other hand, can better address this issue. Cross-sectional data uses contemporaneous 2016 measures of partisanship and vote choice (recorded at the same time) to say that 90 percent of Republicans voted for Trump. But the better approach would be to use a pre-2016 measure of partisanship–unaffected by Trump–and calculate how vote choice breaks down along this variable that better represents an underlying indicator of partisanship. Publicly available panel data–with waves in December 2011, November 2012, and December 2016–from the Voter Study Group (VSG) offers such an improvement in capturing party voting (the rate at which partisans vote for co-party candidates).

Specifically, I can compare how party voting looks like using both a 2011 measure of partisanship (before both the 2012 and 2016 elections) and a 2016 measure of partisanship (that is purportedly endogenous to 2016 vote choice). If there are large differences in party voting across these measures, then an endogeneity problem exists, suggesting that partisanship is shaped in response to vote preference. If small or no differences result, then partisanship constitutes a more exogenous variable–in line with the stable over time character that much of political science literature (and other evidence) suggests.

The VSG data offers mixed evidence but mostly lies in favor of the latter conclusion. I’ll start with the broadest perspective–overall rates of party voting: Republicans voting for Trump and Democrats voting Clinton as a percentage of all partisans who reported voting in 2016. If I use the 2016 measure of partisanship, I find that 89 percent of partisans voted their party in 2016. If I use the 2011 measure of partisanship, 84 percent of partisans voted their party in 2016. Thus, it appears that a small percentage of people shifted their partisanship to match their vote preference in 2016, and in a way that would slightly inflate the impact of partisanship on voting. But it’s fairly small, as the party voting rates remain similar.

Breaking these party voting rates by party and candidate reveals a similar picture, but with some additional information. The 2016 measures of partisanship suggest very high rates of party voting, with 90 percent of Democrats voting Clinton and 88 percent of Republicans voting Trump. That rate declines a bit when using a pre-Trump (2011) measurement of partisanship: 83 percent of original Democrats opted for Clinton, while 84 percent of original Republicans went Trump. These percentages are not that different, but at least some partisanship updating is likely at play. What’s more interesting is how this pre-Trump underlying partisanship better captures defection from the Democratic Party (in a way that–not balanced out by similar defection among Republicans to Clinton–could have tilted the election just enough to Trump). If we use a 2016 party measure, then we would conclude that seven percent of Democrats voted Trump. Using the 2011 measure of the original Democratic party base, however, nearly doubles that size, revealing 13 percent of Democrats who voted for Trump in 2016. This panel approach can thus offer a more meaningful estimate of how many original Democrats defected from the party in voting for the out-party candidate in 2016.

Finally, I wanted to further break down this comparison by using the full seven-point party identification scale. The below plot shows how each party identification group (of the seven in total) voted in 2016 when using an individual’s 2016 reported partisanship.

Differences in party voting rates at this partisanship subgroup level appear when comparing the above plot to the same breakdown but with an individual’s 2011 reported partisanship, as the below plot illustrates:

If we use 2016 cross-sectional data, then we end up with an overestimate of how closely “Strong Democrats” and “Not very strong Democrats” adhered to their party affiliation for deciding whom to vote for. This approach would say that 97 percent of strong Democrats and 79 percent of weak Democrats voted their party in 2016, as opposed to 90 and 70 percent respective rates when using underlying (2011) partisanship. Similar differences appear on the Republican side, but as mentioned earlier, the magnitude is smaller. The biggest difference is for the “Lean Republican category,” as while a 2016 measure suggest 90 percent of this group went Trump, a metric capturing original members of this category suggests 83 percent did.

In sum, this comparison does suggest some partisanship updating to accord with vote choice took place, but not to any large extent. Concerns with endogeneity should be tempered. That’s in large part because partisanship remains a very stable over time variable–at both the aggregate and individual level. To underscore this latter point, I used all three survey waves of the VSG (bringing in the 2012 wave that I’ve exclude up until now) to track individual level partisanship dynamics at three different points in time over a five-year span. That results in the following table, which shows the distribution of VSG survey respondents by the different possible party ID combinations they can have across the three survey waves. In each wave, they can express three different partisan affiliations, which makes for 27 unique combinations (3*3*3 = 27).

Two combination groups stand out: people who identified as Democrats in all three years (41.74 percent of all respondents) and people who identified as Republicans in all three years (33.79 percent). That means about three out of every four people (75.53 percent to be exact) are consistent partisans over the course of four years. The next most common group is people who do not reveal any partisan leanings–Independents–which makes up 6.07 percent of all respondents. Thus,  81.60 percent of all people have a consistent expression of partisanship (or lack thereof) across five years at three different points in time, a piece of evidence indicative of strongly stable partisanship.

Moreover, looking further down the table, only 4.64 percent of respondents ever identify with both parties at some point during the three survey waves. Rather, most of the party switching–which is very little to begin with–is into and out of the Independent category (this movement takes up 13.76 percent of all survey respondents). In light of these trends, the lack of substantial endogeneity–changes in partisanship driven by vote choice selection–should not come as much of a surprise.

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Sample Composition Effects in Alabama Senate Election Polling

The controversy surrounding Roy Moore in the Alabama Senate election seemed primed to create a differential partisan nonresponse bias phenomenon often seen in past election polling. As negative news increases about a candidate in an election, members of the public who share that candidate’s party or support the candidate become less inclined to take polls about the election. Shifts in election polling could thus result from mirages related to these nonresponse patterns, and not actual changes in opinion.

I’ve shown a similar trend using crosstabs from public polling in the context of Donald Trump approval rating polls, finding a moderately strong relationship between the partisan composition of a sample and Trump approval. I thought it might be informative to do the same with the string of pre-election polling for the upcoming Alabama Senate election. First, among polls that make partisan composition data available, I graph the relationship between partisan composition (the difference in Republican and Democratic percentage of a poll’s sample) and Moore’s margin of support (the difference in Moore’s and Jones’s intended vote shares).

There’s a weak but present association between the two variables here. If the relationship was one-to-one, then partisan composition would completely shape the polling outcome and would thus suggest changes in Moore’s polling numbers are shaped entirely to how many Democrats and Republicans take a poll. That’s not the case here For every one point increase in net Republican margin, there’s a 0.36 point increase in Moore’s margin. It’s positive as expected–the more Republicans that take a poll, the better Moore fares–but not too strong. This may have more to do with other aspects by which polls conducted for this race differ, in which case a comparison using consecutive survey results from the same pollster would be a more accurate test. However, not enough polls exist to do that.

Comparing the level of Trump support in a poll with Roy Moore’s advantage over Doug Jones produces a slightly stronger relationship, as shown below:

In this case, for every one point increase in Trump’s net approval, a 0.89 point increase in Moore’s margin over Jones results. Thus, the more people with favorable views toward Trump respond to Alabama Senate election polls, the better Moore appears positioned in the race. It’s worth noting that the variation in Trump net approval across polls–as small as +5 and as large as +22–likely also has to do with how different pollsters approach sampling and vary in methods. If I try to hold pollster “constant,” I only have two JMC Analytics polls to consider. As I’ve mentioned before, the lack of Trump approval change but presence of a Moore margin shift suggested the vote choice opinion shift was not artifactual but real opinion change.

Ideally, there would be more opportunities to look at within-pollster change like this. One comparison doesn’t preclude the possibility of nonresponse bias affecting polling in this race, but should indicate this bias isn’t as clear-cut and strong–even though the race’s dynamics and surrounding news would make it likely. At the same time, looking across polls does reveal patterns indicative of some stereotypical nonresponse bias effects. Polling will likely remain limited down the stretch of this race, but more polls to examine will always give a clearer picture of whether this bias is at play–especially from pollsters who have already polled the race earlier.

Presidential-Gubernatorial Race Splits and Party Voting in 2016

While down-ballot ticket races such as Senate and House elections have become increasingly nationalized–closely correlating with state presidential vote–gubernatorial elections have not followed this path as much. As Harry Enten detailed using 2012 presidential vote and 2014 gubernatorial vote totals, several states went for presidential and gubernatorial candidates of different parties. Examples include Florida, Maryland, Massachusetts, and Wisconsin. In some cases, states chose the same party, but diverged significantly in vote share that got them to that point (e.g. Kansas).

A similar thing occurred in 2016. Among the 12 states that held a governor’s race, Democratic vote share in gubernatorial elections could explain just 29 percent of variation in Democratic vote share in the presidential race. The relationship between the two variables can be seen in the below plot. If all states fell on the 45-degree line, then their gubernatorial and presidential votes would match perfectly. Thus, the further each point (state) diverges from the line, the more unrelated these vote shares are.

Five of the 12 states elected governors from the opposite party of the president that won the state: Vermont, New Hampshire, North Carolina, Montana, and West Virginia. The curious split between gubernatorial and presidential voting at the state level therefore appears to have persisted in 2016. That prompts obvious questions about voting at the individual level: to what degree is cross-party voting occurring? Such a question has implications for the broader study of partisanship, as it appears that party affiliation exerts a different force in presidential and gubernatorial ballot decisions. As a result, it might give a clue about the degree to which voters rely on partisanship or other factors–such as those specific to candidate traits or state conditions–in casting their votes.

Using 2016 CCES data, I was interested in seeing the rates of party voting by election type–presidential or gubernatorial–in the 12 states that had both race types in 2016. I calculate “party voting” as the percent of Democrats who vote for a Democratic candidate and the Republicans who vote for a Republican candidate out of all partisans who expressed a vote choice when asked after the election took place. (Note: the results below do not use vote validation out of sample size concerns, but when using only verified voters, the results are very similar.) The darker blue tinted bars correspond to party voting in the presidential race, while the lighter tinted bars represent voting in each of the state’s gubernatorial races. 95 percent confidence intervals are included for each calculation. While non-overlapping bars do not indicate statistically significant differences, these intervals should give a sense of the accuracy of each percentage which is informative for understanding party voting rates.

Few large divergences between presidential and gubernatorial party voting rates appear. Across most of the states here, people vote their party to similar (high) degrees, whether it’s a governor’s or president’s race. However, a few trends are notable. In Montana, one of the states that went to different parties, the party voting rate was 10.5 percentage points higher in the presidential race than in the gubernatorial race. 17 percent of Montanan partisans voted for a candidate outside their own party, suggesting some split-ticketing voting taking place. That should help clarify why the Democratic gubernatorial candidate (Steve Bullock) did 14 percentage points better than the Democratic presidential candidate (Hillary Clinton) did, for example.

Similarly, a statistically significant difference occurs in New Hampshire in the party voting rates by race type. While 94.9 percent of partisans voted for their co-party candidate in the governor’s race, fewer in the presidential race did at 88.5 percent. This split likely made the pairing of a Democratic presidential win and Republican gubernatorial win possible. One other mixed result state sees this type of split–West Virginia, which had a 84.6 party voting rate in its presidential race but only a 70.2 rate in the gubernatorial rate. West Virginians voted their party at a higher rate on the presidential ballot than on the gubernatorial ballot, possibly paving the way for a Democrat wining the governor’s office and a Republican winning the state at the presidential level.

Interestingly, this comparison sheds little light in the case of Vermont, where a Democrat in Clinton won 61.1 percent of the presidential vote but a Republican in Phil Scott won 52.9 percent of the gubernatorial vote. Party voting does not diverge by much by race type. I looked to see whether behavior by non-partisans (pure Independents) could be driving the different results, but little difference by race type appears there as well. Limitations of the survey data used may be at play here, as the sample of Vermonters has more Democratic voters for the governor’s race than it should (a 50-45 Democratic advantage among those surveyed even though it should be closer to 53-44 Republican).

Regardless, differing party voting rates may have played a role in the divergent presidential/gubernatorial race outcomes in West Virginia, Montana, and New Hampshire. Not only does that offer an indication of when partisanship exerts less of a force on vote choice, but it also might bear meaning for when races become nationalized or not. As Dan Hopkins discusses in the description for his forthcoming book, these patterns–especially from the first figure–could indicate the level of nationalization of a race, when a down-ballot candidates become tied to their national party and presidential candidate, and even whether party voting activation corresponds to nationalization of an election.