Are certain trends in partisan public opinion tied more to changing party composition than is often assumed when looking at data? That’s the question that has been increasingly raised of late, particularly in light of developments in Republican public opinion. Stability of Trump approval among Republicans could be deceptive, for example, because Republicans who dislike him could have shed their party label, while those who continue to support him still identify as Republican and in turn leave a more “fanatical” bunch to make up Republican respondents.
These important endogeneity questions (e.g. uncertainty over the direction of the causal arrow between Republican identification and Trump support) take different forms. In some cases, it concerns the effect the candidacy and election of Trump has had on who chooses to identify as Republican–and the ill-equipped nature of cross-sectional surveys to address this potential change. I recently used the two panel waves before and after this past election as part of 2016 CCES data to approximate what kind of effect Trump’s election had on party movement (more notes on this Twitter thread). While there were statistically significant effects of race and ideology on the decision to either defect from or to the GOP from before to after the election, they weren’t large. So at least with that CCES data, the election itself did not seem to have too large of an impact on who chooses to identify as Republican.
The other form is more long-ranging. If there are longer-term changes in what type of people and demographic subgroups identify with each of the major parties, cross-sectional opinion data over several years cannot distinguish between actual opinion change and reshuffling party makeups. A time series showing Republicans changing their opinion on an issue from 2010 to 2017, for example, could actually be more tied to who chooses to identify as a Republican.
To try to gauge some of these longer term compositional shifts, as well as get a sense of the dynamics before Trump’s entry into the U.S. political scene, I took a look at 2010-2014 CCES panel data. The same 9,500 Americans were surveyed in 2010, 2012, and 2014 as part of this study, which allowed for tracking of individual level party identification shifts. The sizable sample in this data allowed for these shifts to then be evaluated along different demographic traits. I took a quick look and focused on two in particular–the first, age group, is shown below, while the second, education among whites, is addressed later.
Each column grid here divides Americans into the partisan groups with which they identified in 2010. The x-axis represents the percentage partisanship distribution of these groups when they were asked about their party in 2014. I break this trend up by four age groups in the above graph. The general takeaway from this result about individual level partisanship shifts is that the Democratic Party has become increasingly younger and the Republican Party has become increasingly older during this four-year pre-Trump span. While 92 percent of 18-44 aged Democrats in 2010 continued to identify as Democrat in 2014, fewer young Republicans in 2010 (86 percent) maintained Republican identification four years later.
Notably, the defection away from the Republican Party shrinks as age group gets older (looking from the top to bottom of the graph). The trend isn’t as perfectly gradual on the Democratic side, but the 12 percent of the oldest Democrats who defect four years later–eight percentage points more than Republicans in the comparable situation–offers a good indication of a partisan reorientation with respect to age. Moreover, pure Independents age 65 or older in 2010 shift in greater amounts to the GOP (25 percent) than to the Democratic Party (11 percent) when evaluated four years later. Importantly, this individual level panel data approach can confirm an age-based partisanship shift that’s not tied up with increasing non-white shares of the youngest population (more on this here).
The second demographic trait by which I wanted to break down partisanship change was educational level among whites only. The graph below for this dynamic follows the same approach as the previous one, except four educational groups replace the four age groups.
Interestingly, partisanship change from 2010 to 2014–at least with this data–shows stability among people who identified as Republicans in 2010. The effects of outside shifts are still small but less stable. Movement among 2010 Independents in 2014 appears to make the GOP trend toward a population with lower formal education levels, as the three lower educational groups here enter the Republican Party more than the highest educational group does. On the other end, it’s clear the Democrat Party is growing more educated in terms of who it loses and who it gains as party members. A steady gradient exists among 2010 Democrats such that as educational level decreases, defection from the Democratic Party increases. 97 percent of post-graduate Democrats remain Democratic in 2014, while only 84 percent of 2010 Democrats with a high school education or less remain so. Additionally, the amount of 2010 Independents the Democratic Party gains in 2014 is positively correlated with their educational level. This trend with Democrats in particular is consistent with other accounts of a growing educational partisanship divide.
In sum, this type of data–at an individual level–can confirm that partisanship shifts observed in cross-sectional data have been in effect before Trump’s entry into American politics. If certain subgroup traits are correlated with certain public opinion expressions, then this becomes important for how we understand partisan public opinion change (or stability). Panel data that includes a 2016 wave would be of course the optimal type to answer a lot of the aforementioned endogeneity questions. Still, these results suggests party compositional shifts are certainly real. Helpful next steps in the future would track how the same respondents answer identical questions in 2012 as opposed to 2016, for example, to more rigorously evaluate the meaningfulness of cross-sectional time-series public opinion data.