60 years of partisanship: Race, religion, and region

Back in the Eisenhower years (1953 to 1960), the U.S. electorate was over 90% white and over 90% Christian. These days, it’s closer to 70% white and 70% Christian, with people who are both white and Christian comprising just about half of potential voters.

Currently, white Christians are mostly Republicans while non-whites and non-Christians are mostly Democrats. If you were to project these modern political coalitions backwards in time, you’d expect that the mid-1950s were years when the large majority of people voted Republican. But it wasn’t the case. Eisenhower himself was a Republican, but in six of his eight years both the House and Senate were controlled by Democrats.

The key, of course, is that Republicans haven’t always gathered their support from white Christians generally. Back in the 1950s, white Southerners and white Catholics were mostly Democrats. Republicans were primarily white Protestants outside the South.

Over time, however, things changed. Democratic support for the Civil Rights Acts in the 1960s contributed both to blacks becoming more solid Democratic supporters and to white Southerners moving towards Republicans. Then the slow evolution of Republicans into the party of “family values” since the 1980s led to additional movements among white, churchgoing Christians, including lots of Catholics and Southern evangelicals.

Let’s take a look at some of that. The chart below uses American National Election Studies (ANES) data from the 1950s to 2016. My “partisanship” measure combines self-labelled party identification with reported votes for president, house, and senate—so, the biggest Democrats here not only identify as Democrats but also turn out and vote for Democrats, and the same for Republicans.

I looked at ANES variables on race, religion, and region, and made splits that were big deals in predicting partisanship over time. The first splits were by race: non-Hispanic white vs. non-Hispanic black vs. Hispanic/Asian/other. Then I split whites into Protestants vs. Jews vs. “other religion” (which includes Catholics, “nones,” those in various minor religions, and also various Christians who don’t think of themselves as either Protestant or Catholic). Then I split white Protestants by South vs. not South and weekly church attendance vs. less than weekly. And I split the “other religion” folks into monthly attendance vs. less than monthly attendance—the large majority of the monthly attending folks here are Catholics and other non-Protestant Christians, while the less-than-monthly folks are a mix of “nones” and low-attending folks who nonetheless retain a religious identity.

The chart has a lot going on, but it’s not that complicated in the end. The different colors represent the different demographic groups. The size of the circles show the relative size of the groups in various years (so, e.g., you can see from the size of the red circles that the electorate used to have very few Hispanics/Asians/others but now it has quite a lot, approaching 20% in the 2016 ANES sample). The left-to-right position is the degree of Democratic vs. Republican partisanship (so, e.g., if you look at the recent years up top, you can see that blacks are especially strong Democrats while white, Protestant, Southern churchgoers are especially strong Republicans).

(Note: To smooth things out, the displayed results are based on averages of the relevant year along with the surrounding years—so, e.g., the earliest year, 1956, is based on averaging ANES data from 1952 to 1960. The exception is 2016, where I obviously didn’t have future data to average, so it’s mostly just based on 2016 data. One implication of this is that a sudden shift that occurs in Year X would actually start showing up on the chart a bit before that, and won’t fully appear until a bit after.)

If we start at the bottom of the chart in the mid-1950s, Republicans were mostly white Protestants from outside the South and Democrats were a hodge-podge of Jews, those in other religions or no religion (back then, this was mostly Catholics), non-whites, and Southern Protestants. Through the 1960s, as Democrats moved left on civil rights, blacks became very solidly Democratic and non-Southern white Protestants became less solidly Republican, while Southern white Protestants became less solidly Democratic.

Things were pretty stable for these various groups from 1970 to 1990, but then big shifts kicked in as white Protestants, churchgoers, and Southerners continued moving towards Republicans. These were the years when Republicans became paradigmatically home to white evangelicals, something that is as true today as it has ever been. In addition, the Bush II and Obama years saw further gains for Democrats among non-whites.

I’m just looking at race, religion, and region in this analysis. There are more details, obviously—income, gender, age, union membership, and so on. And, of course, the most recent election saw real splits along educational lines, especially among whites. But, still, race, religion, and region are, as far as I know, among the biggest demographic deals over this time period.

Can we know what’s coming?

Whenever I look at historical data like this—on politics, fertility, employment, religion, or whatever—I’m always struck by the tremendous uncertainty of projecting future long-term trends. I imagine someone looking at the massive downward fertility trends in the early 20th century trying to predict the mid-century Baby Boom. I imagine someone looking at the massive upward trend in prime-age women’s labor participation in the second half of the 20th century trying to predict that it would peak in 2000 and then decline. The next time you see, for example, projections of worldwide religion and fertility trends out to 2060, think about what such projections about 2016 would have looked like had we made them in 1972.

Such uncertainty is no less potent when it comes to political party coalitions. These coalitions are cobbled together and evolve in ways that rely on a variety of not-at-all-inevitable circumstances. For instance, six months ago the U.S. had a presidential election that given any one of a hundred subtle differences might have come out the other way—not quite as purely coin-tossy as 2000, but still. And the 2016 election involved complex changes from 2012, some from party switching but perhaps more so from differences in turnout.

Estimating how these kinds of complex matters might develop over longer timeframes involves a number of known unknowns and unknown unknowns. Such efforts are entertaining, to be sure, but it’s a little crazy to actually believe any of them.

Actually, epistocracy might have helped Clinton defeat Trump

But she probably would have been running against President Romney, and might have still lost.

Epistocracy is in essence the idea that voting participation or outcomes should be adjusted relative to citizens’ knowledge levels. For example, perhaps people should have to pass a basic political knowledge test before being allowed to vote, or perhaps we should allocate extra votes to people who do particularly well on such tests.

In a post last year, I took the 2016 exit polls, along with political knowledge test results from the 2012 American National Election Studies (ANES), and tried to estimate how various epistocracy proposals would have affected the 2016 presidential race between Clinton and Trump. It was complicated work, estimating party- and demographic-specific knowledge levels from the 2012 sample, and seeing how that would plug into the demographic voting patterns from the 2016 exit polls.

My conclusion was that various epistocracy proposals would have helped Trump. But the new 2016 ANES sample was recently released. And now it seems likely that my conclusion was wrong.

There’s no need for a complicated model this time. The 2016 ANES survey included nine political knowledge items (e.g., knowing which party controlled the U.S. House and Senate and being able to identify various domestic and foreign leaders). It asked people how they voted in the 2012 and 2016 presidential elections. So, you know, we can just look within the same sample at Obama vs. Romney voters and Clinton vs. Trump voters regarding how well they did on the nine-item test of political knowledge.

The chart below shows the main results, including political knowledge averages (the dots) and 95% confidence intervals (the lines). Looking at 2012, according the the 2016 ANES sample, Obama voters averaged 5.1 correct out of the 9 items measuring political knowledge, while Romney voters averaged 5.48. This was a highly significant difference (p = .00001); you can see that the 95% confidence intervals are pretty far apart. In 2016, in contrast, Clinton voters averaged 5.37, while Trump voters averaged 5.15. This was a smaller knowledge gap than in 2012, though still significant (p = .009); you can see that the 95% confidence intervals overlap a bit.

So, the headline here is that various epistocracy proposals likely would have helped Clinton over Trump. But such proposals would have especially helped Romney over Obama. (In fact, before anyone on the left gets too mouthy about the average political knowledge of Trump voters, they should note that the 2016 ANES data suggest that Trump voters were at least as knowledgeable on average as Obama voters.) And it’s entirely more difficult to say what the implications would have been for a contest pitting President Romney against challenger Clinton in 2016, though the ANES sample suggests that epistocracy proposals would have perhaps given a small boost to Romney.

OK, so how did this happen? What did my earlier estimates miss? What changed such that, in the 2016 ANES sample, we see substantially higher political knowledge for Romney voters relative to Obama voters, but then things flip in the Clinton-Trump race?

Changes from 2012 to 2016

There are at least a couple of basic things that caused my earlier analysis to differ from the results in the 2016 ANES sample. One, as I discussed in yesterday’s post, is that the 2016 exit polls have some substantial differences with the 2016 ANES sample. For example, relative to the ANES data, the exit polls contain a lot more college-educated folks, show a weaker pro-Clinton margin among the college-educated, and show a wider gender gap. In short, I checked what the 2016 ANES political knowledge outcomes would look like if the ANES sample mirrored the demographic composition of the exit polls, and Clinton’s epistocracy advantage would be reduced but not eliminated. In addition, the 2016 ANES data show marginally smaller racial and gender gaps in political knowledge than did the 2012 ANES, so that was part of it as well.

But the bigger impact comes from what the 2016 ANES sample shows about differences in turnout between 2012 and 2016. The new data suggest that two complementary things happened: A group of especially low-knowledge folks who had turned out for Obama ended up sitting out the 2016 election, while at the same time a different group of especially low-knowledge folks who had sat out the 2012 election ended up turning out for Trump. This raised the average political knowledge of Clinton voters relative to Obama voters and lowered the average political knowledge of Trump voters relative to Romney voters, something that went beyond what the basic demographics in the 2016 exit polls suggested.

As I discussed in a recent post, the 2016 ANES data show a lot of complex shifts from 2012 to 2016. These dynamics involved not only (or even primarily) voters switching between parties, but also (in fact, mainly) switches between voting and not voting.

The chart below shows averages in the nine-point political knowledge measure, split out by both 2012 and 2016 major-party votes (and non-votes).

  • There are three groups with pretty high knowledge averages: People who voted Romney and then switched to Clinton (though this is a pretty small group, which shows up in the wide 95% confidence intervals represented by the line), people who voted for Romney and then Trump, and people who voted for Obama and then Clinton.
  • And then there are three groups in the middle: People who voted for Romney and then didn’t vote for president in 2016, people who didn’t vote in 2012 and then voted for Clinton, and people who voted for Obama and then switched to Trump.
  • And then there are three groups with particularly low political knowledge averages: People who didn’t vote in 2012 and then voted for Trump, people who voted for Obama and then didn’t vote in 2016, and people who sat out both the 2012 and 2016 presidential elections.

Thus, you can see what I’m talking about at the low end of the chart. There was a group of low-knowledge Obama voters who didn’t turn out in 2016. And there was also a different group of low-knowledge Trump voters who hadn’t turned out in 2012. These groups have substantially lower political knowledge on average than both the Romney voters who didn’t turn out in 2016 and the Clinton voters who hadn’t turned out in 2012. It was these turnout dynamics that primarily drove the flip in the major parties’ political knowledge averages between 2012 and 2016.

As I’ve mentioned, these kinds of patterns might spell trouble in the 2018 midterms for Republicans. Just as Obama had personally attracted a segment of low-knowledge folks who don’t normally turn out and couldn’t be relied on in midterms, we now have indications that Trump also personally attracted his own (different) segment of unreliable, low-knowledge folks.

Political knowledge and the primaries

The 2016 ANES data provide a look at the primaries as well. The chart below shows political knowledge averages for the major contestants’ voters in the primaries (and also shows the lower average of people who didn’t vote in the primaries).

Would epistocracy proposals have affected the Clinton-Sanders primary race? It doesn’t look like it. Would epistocracy proposals have hurt Trump in the Republican primaries? Yes, it’s likely. This isn’t, though, because Trump’s primary voters were especially low-knowledge—they weren’t significantly lower than Clinton’s and Sanders’s primary voters. Instead, it’s because the other Republican primary voters were especially high in political knowledge.

These patterns reinforce a couple of earlier points. First, political knowledge is higher among people who regularly vote. And, second, typical Republican voters (whether voting for Romney in 2012 or for Cruz, Kasich, or Rubio in the 2016 primaries) do better on political knowledge tests than typical Democratic voters.

As usual, it’s complicated

So there are various ways that epistocracy might have stopped Trump in 2016. If there had been some significant epistocracy mechanism in the 2012 general election, then it’s likely that Romney would have won, and, under the usual assumptions, unlikely that Trump would or could have mounted a successful Republican primary challenge to a sitting president. Or, if some significant epistocracy mechanism had first been implemented in the 2016 Republican primaries, then it’s likely that Trump would have had a harder time winning that nominating contest—perhaps we would have seen a Cruz-Clinton race instead. Or, if some significant epistocracy mechanism had first been implemented in the 2016 general election, then it’s likely that Trump would have lost to Clinton.

But, again, just because epistocracy might have hurt Trump, it’s not the case that epistocracy would typically hurt Republicans. Quite the opposite. A significant epistocracy adjustment would have probably meant that Obama would have been, at best, a one-term president. And it also might have given an edge to any major non-Trump Republican in a race against Clinton.

And this is, of course, why support for epistocracy proposals usually comes from folks who prefer conservative economic positions but oppose Trumpian white nationalism—that is, libertarians, mostly. (In general, people who test well tend to hold more libertarian-leaning issue positions.) In the wake of Trump’s unusual, unlikely, skin-of-his-teeth Electoral College victory, though, a lot of liberals might start agreeing that some form of epistocracy would be desirable (even if impracticable). But, you know, be careful what you wish for.

Comparing samples from 2016: Exit Polls vs. ANES vs. CCES

The publicly available data on the 2016 presidential race tell a broadly consistent story, but have some important differences as well.

In addition to the 2016 exit poll results that were made public on election night, in the past month we’ve seen releases of new data from two other important samples, the American National Election Studies (ANES) and the Cooperative Congressional Election Study (CCES). The exit polls combine phone surveys of early and absentee voters with live surveys of exiting voters at a number of physical polling places; the available results appear to be from over 24,000 voters. The ANES combines an online sample and a face-to-face sample, containing around 2,800 voters. The CCES is an online sample containing around 45,000 voters.

After the actual ballots had been counted, Clinton won the popular vote over Trump by 48.2% to 46.1%. This margin of 2.1 points is very similar to the overall margins reported by the exit polls and the CCES, though the ANES sample is somewhat Clinton-skewed, containing around 49% for Clinton and 44% for Trump.

All these samples measure roughly comparable information on a number of demographic items—race, gender, religion, education, and so on. In this post, I’ll compare results across the three samples on a selection of them. While the samples are similar on some of the major themes of the election, there are also important differences. Some of these differences involve the percentages of the samples represented by various groups (e.g., the exit polls have a lot more college-educated folks than ANES and CCES). Other differences are in the Trump-Clinton margins within various subgroups (e.g., the exit polls show Trump with an especially large margin among non-degreed white men, while the ANES sample shows Clinton with an especially large margin over Trump among Hispanics).

So let’s see some details. The first chart below shows whites, blacks, Hispanics, and immigrants. There are small differences in the racial makeup of the samples, with relatively fewer whites and more Hispanics (and also more immigrants) in the exit polls, and relatively more whites and fewer Hispanics (and also fewer immigrants) in the CCES. The bigger differences are in the support margins. In particular, the ANES sample shows substantially more support for Clinton over Trump among immigrants and Hispanics. (The “Trump-Clinton Margin” here simply subtracts Clinton’s percentage from Trump’s percentage, thus showing bigger Clinton margins to the left of 0 and bigger Trump margins to the right of 0.)

The next chart below shows women, men, people with 4-year degrees, and people without 4-year degrees. Here, there are some really major differences between samples. Primarily, the exit polls show a markedly higher percentage of people with college degrees (50%), particularly as compared with the CCES (31%). Yet, recall that the exit polls and the CCES both show similar overall outcomes, giving Clinton around a 2-point advantage over Trump. And, sure enough, we see how it works in the margins by education: The exit polls have more college graduates, but show Clinton with a smaller relative advantage over Trump among college graduates—and, in this case, the smaller advantage among a larger group ends up producing an overall average similar to the CCES (which shows a larger advantage among a smaller group). The two samples end up taking different roads to the same place.

In addition, in the chart above, the exit polls showed a wider gender gap than either the CCES or (especially) the ANES. I had remarked in an earlier post using CCES data that I was surprised that gender wasn’t a bigger deal; this intuition came in part from my earlier look at the exit polls.

The next chart below shows whites split out by education and gender. Here, we see echoes from the prior chart—the exit polls had more college graduates (and, obviously, fewer non-graduates), different Trump-Clinton margins by education, and a wider gender gap. So, in this chart, we see some really big sample differences. While the exit polls showed Trump with a 48-point margin over Clinton among white men without degrees, the ANES data have this at only a 28-point margin. While the exit polls showed Trump with a 14-point margin over Clinton among white men with degrees, the CCES has this at only a 2-point margin. While the exit polls showed Clinton with only a 7-point margin over Trump among white women with degrees, the ANES sample places Clinton’s advantage at 20 points. These are, umm, non-trivial differences.

The last chart below shows white evangelicals, non-Christians (of all races), LGBT folks, and military veterans. And there are more differences between the samples, particularly in the Trump-Clinton margins. For example, the ANES shows Clinton with a 67-point advantage over Trump among LGBT folks, while for the CCES it’s 50 points. (Actually, in the ANES, this is only LGB folks—they didn’t ask their sample about T.) Also, the CCES shows Trump with a 27-point advantage over Clinton among veterans, while in the ANES it’s 18 points.

Between deification and nihilism

Comparing across samples, there are some things we’re pretty damn sure of. For example, Clinton did a lot better than Trump among racial minorities, LGBT folks, non-Christians, immigrants, and the college-educated. Trump did a lot better than Clinton among white evangelicals, non-degreed whites, and veterans.

How much better, exactly? Well, that’s complicated. There’s no perfect data, no singular answer, no assumption-free yardstick. Each sample has idiosyncrasies and drawbacks. Each sample has “special sauce,” from sampling strategies at the front end to the construction of weighting variables at the back end. This stuff is really hard.

As a consumer of social science, a central challenge is to try to stay in that middle ground between the hazards of data deification and data nihilism. On the one side, sometimes we form opinions that are way too certain based on limited samples and stilted analyses. Indeed, as we just saw, even comparing very high-quality samples using simple percentage splits reveals a number of important differences. So just imagine all the crazy nonsense regularly produced by running, say, complex multivariate analyses using small and obviously non-representative samples. Seriously.

On the other side, sometimes the uncertainties in sampling and analysis make us too quick to throw out the baby with the bath water, or, you know, to deny that anyone ever knew there was a baby there in the first place. All population estimates based on limited samples are probabilistic, but that doesn’t somehow eliminate the fact that having more data from more sources tends to produce better estimates.

The mature response is a laborious one, one that consistently acknowledges the tremendous complexity of social science and the hard reality of noise. Ain’t nobody got time for that, I know. And I certainly haven’t always struck the right balance myself. But it’s important to try.

Electorates are like rivers

You can’t step into the same one twice.

According to the United States Election Project, around 137 million people voted for president in 2016 (out of 231 million who were eligible). In 2012, around 129 million voted for president (out of 222 million who were eligible).

Thus, there were lots of 2016 presidential voters who hadn’t voted for president in 2012—at the very least around 8 million. But, of course, it’s a much higher number than that. Some of the 129 million who voted in 2012 didn’t vote in 2016, either because they couldn’t (e.g., they died) or, more often, because they chose not to.

In fact, according to data recently released from the American National Election Studies (ANES), around 19% of presidential voters in 2016 hadn’t voted for president in 2012—that would be around 26 million out of 137 million. Further, around 12% of 2012 presidential voters who were eligible to vote in 2016 didn’t vote for president in 2016.

It’s understandable that folks prefer to think of the voting population as a rather fixed thing—as though, for example, when you look at Michigan in 2016 and see that Trump beat Clinton by 47.5% to 47.3%, while back in 2012 Obama beat Romney by 54.2% to 44.7%, then that probably means that the main story is that tons of Obama voters switched to Trump in Michigan. But it’s just really more complicated than that.

So let’s see some details from the ANES data. Here, I’m looking only at people who reported voting for president in either 2012 or 2016 (that is, I’m excluding people who didn’t vote for president in either election). In other words, it’s a sample of the combined 2012/2016 presidential voters, as surveyed in 2016 (which means it’s missing people who voted in 2012 but then died or otherwise became ineligible to vote before 2016).

The first chart below shows the full sample of 2012/2016 presidential voters. It’s broken down by major-party votes (Obama vs. Romney and Clinton vs. Trump) along with “neither,” which combines non-voters and third-party voters. So, for example, the “Neither Neither” category combines people who voted third party in both elections, people who didn’t vote for president in 2012 and then voted third party in 2016, and people who voted third party in 2012 and then didn’t vote for president in 2016.

The results show a remarkably dynamic situation. Overall, in this sample of 2012 and 2016 presidential voters, around 61% voted for the same party in both elections. And then around 27% voted in one but not the other presidential election, including non-voters in 2012 who then voted in 2016 (17%) and 2012 voters who sat out 2016 (10%). And the other 12% voted in both elections but either switched between the major parties (7%) or switched between a major party and a third party/independent candidate (5%).

Given how close Trump’s Electoral College victory was, you can pin it on any number of causes. Would Clinton have won if more Obama voters had turned out? Yes. If she had been able to pull in more previous non-voters? Yes. If more Romney voters had sat this one out? Yes. If Trump had attracted fewer former non-voters? Yes. If she had converted more Romney voters? Yes. If Trump had converted fewer Obama voters? Yes.

Now, it’s certainly the case that Obama was more popular relative to Romney than Clinton was relative to Trump. This shows up in a number of ways. Most obviously, Obama’s popular vote win was bigger than Clinton’s popular vote win. And, according the ANES data, there were more Obama-to-Trump voters than Romney-to-Clinton voters. And, while both Clinton and Trump pulled in substantial numbers of prior non-voters, there were more Obama voters than Romney voters who sat out 2016 (or voted third party).

The thing I don’t get, though, is the continued narrative that particularly highlights Trump’s conversion of former Obama voters. Yes, it was a contributing factor. But, no, it doesn’t look like The Biggest Thing. Relative to the number who switched from Obama to Trump (5.3%), there were considerably more folks who had voted for neither Obama nor Romney in 2012 and then voted for Trump (8%) or who voted for Obama and then voted for neither Clinton nor Trump (8.9%).

A few subgroups

I suspect that many of my readers are surprised by the level and complexity of these electoral shifts. But I also suspect that many of my readers are college-educated whites. And college-educated whites are weird. Politically, among other things, they have high turnout rates and they’re much more likely than other folks to hold consistently liberal or consistently conservative positions.

So you just don’t see as much electoral instability among college-educated whites. The chart below shows it pretty clearly in the 2016 ANES data. Here, around 74% were in the voted-for-the-same-party-in-both-elections club, 7% were new voters, 6% were drop-outs, 6% switched between major parties, and 6% switched between a major party and a third party/independent. Further, the dynamics from 2012 to 2016 among whites with 4-year degrees had roughly balanced effects on the 2016 race. For example, Clinton and Trump attracted about the same number of new voters, about the same number of Romney and Obama voters voted for neither Trump nor Clinton, and about the same number switched from Obama to Trump as from Romney to Clinton.

In contrast, things were just a lot less stable when you move away from degreed whites. The next chart below shows whites without 4-year degrees. For these folks, only around 55% were in the voted-for-the-same-party-in-both-elections camp. And you can really see Trump’s strengths with this group. In particular, while 35% voted for both Romney and Trump, another 13% voted for Trump after not having voted (or voted third party) in 2012, and then another 7.7% voted for Trump after voting for Obama in 2012. (Again, this doesn’t support a Trump-won-mainly-by-converting-Obama-voters narrative—there were substantially more new voters in Trump’s white working-class base than there were 2012 Obama voters, something I also found when I looked at recent data from the Cooperative Congressional Election Study (CCES).)

Non-whites also displayed similarly high levels of electoral instability when we compare 2012 with 2016, shown in the chart below. Here, only around 57% were in the voted-for-the-same-party-in-both-elections camp. Unlike non-degreed whites, though, when non-whites did turn out to vote, Democratic candidates had an enormous advantage. But there’s great variance in turnout. In this ANES sample of 2012/2016 voters, around 15% of non-whites voted for Obama but then not for either Clinton or Trump, which was almost exactly offset by around 15% who had voted for neither Obama nor Romney but then voted for Clinton.

The ANES data on non-whites does suggest, though, some real weaknesses for Clinton relative to Obama. In general, one might have expected a larger portion of new Clinton voters relative to dropout Obama voters from at least a couple of sources—including from younger folks who came of voting age since 2012 and from recently naturalized immigrants. Further, there were hardly any Romney-to-Clinton converts among non-whites, while there was a small but not insignificant number of Obama-to-Trump converts. Indeed, in a pretty startling bit of detail, there were more non-whites who didn’t vote for Romney and then voted for Trump (8%) than who did vote for Romney and then voted for Trump (6%).

Yeah, but …

Yeah, but, it’s just one sample. True. It’s always better to have multiple independent data sources when addressing complex issues. I will say, though, that one of my main conclusions—that, among non-degreed whites, Trump had more prior non-voters than Obama converts—looks pretty similar in the ANES sample here as it did when I looked at the 2016 CCES sample. Now, having said that, I can also report a major difference between the ANES and CCES samples—the latter has fewer 2016 non-voters and, relatedly, more same-party 2012/2016 voters. I haven’t yet developed a good sense of why that is.

Yeah, but, retrospective surveys inflate the winner’s support, and in this case will especially inflate Obama’s support in 2012. Also true. Indeed, a suspiciously high percentage of the ANES sample report voting for Obama relative to Romney in 2012. Two things, though. First, you actually should expect some degree of Obama inflation here relative to Romney. This is because Romney did particularly well with seniors and, not to put too fine a point on it, people who were seniors in 2012 are less likely to show up in a 2016 survey than people who were not seniors in 2012. But, still, the Obama vs. Romney numbers remain high. Which raises a second point: If you think that the Obama vs. Romney numbers are off in favor of Obama, what that means is that you think that the number of Obama-to-Trump converts is probably even lower than these samples are showing. And, likewise, you think that the number of new Trump voters is probably even higher. That is, if anything, these samples are probably underreporting the extent to which new Trump voters outnumbered Obama converts.

Will Trump have his own midterm problems?

Before 2016, the presidential and midterm elections from 2008 to 2014 were marked by seesawing variations in the partisan makeup of the voting population. Obama scored strong victories in his presidential elections, only to be followed by a “shellacking” in both midterms. Prior to 2016, this created a conventional wisdom about how Democrats do well in presidential years but not midterms. After 2016, though, conventional wisdom shifted: This wasn’t a phenomenon about Democrats but more specifically about Obama—when Obama was personally on the ballot, he drew out a segment of supporters who don’t typically vote.

So now it seems we might see something similar with Trump. He drew out his own segment of supporters who don’t typically vote—whites without college degrees who are younger and poorer. It could be that many of them grow disillusioned with Trump, given that he seems likely to break some of his populist promises (e.g., to make sure everyone has better and cheaper healthcare). But we could also see a more basic Obama-like phenomenon, where many of Trump’s new voters simply don’t show up in midterms when he isn’t personally on the ballot, even if they’ll turn out in 2020 when he is.

We’ll have to wait and see. The details of who turns out to vote from year to year are messy, complicated, ever-changing. Or as Socrates put it (according to Plato): “Heracleitus says, you know, that all things move and nothing remains still, and he likens the universe to the current of a river, saying that you cannot step twice into the same stream.”

Who are the ideologues?

People whose issue opinions cluster into relatively consistent liberal or conservative bundles tend to be better educated, white, older, and richer.

It’s one thing to ask about the features that tend to divide liberals and conservatives. Religion and sexual orientation are big deals here, along with race, education, church attendance, military service, gender, income, and so on (see, e.g., here and here).

It’s another thing to ask about ideological consistency. When surveys solicit opinions on a number of different kinds of issues—on abortion, income redistribution, immigration, the military, same-sex marriage, the size of government, race, the environment, guns, and so on—some people are pretty thoroughly either liberal or conservative, where the large majority of their issue positions line up on the same side. But then some people pretty thoroughly mix-and-match their issue opinions, choosing about as many liberal positions as conservative ones.

The tendency to hold issue opinions that are ideologically clustered (or not) has its own set of demographic predictors. When we looked at General Social Survey data (in our book and an article), Kurzban and I identified a big divide between whites with college degrees and everyone else. Across different political issues, whites with college degrees have a lots of the most consistently liberal folks as well as lots of the most consistently conservative folks.

The recent 2016 Cooperative Congressional Election Study (CCES) presents a good opportunity to dig deeper here. It’s really big, with over 64,000 respondents who were asked a nice range of different issue opinions. The downside is that it’s an online study, which self-selects a more sophisticated sample, thus overestimating things like political engagement and ideological consistency. But while the baseline might be off somewhat, we can still see what sorts of features tend to distinguish between the ideologues and the mixers-and-matchers.

Basically, I took my CCES ideology scale, which combines 10 different issue positions (on abortion, guns, the minimum wage, immigration, and so on), and instead of looking for what predicts liberals vs. conservatives, I looked for what predicts ideological consistency vs. inconsistency. I examined a broad range of available demographics and the big splits are shown in the chart below.

My results report two things. One is a basic breakdown on ideological consistency, where I’m using Pew’s definitions for what counts as “consistent” (i.e., people whose issue preferences almost all line up in the same direction), “mixed” (i.e., people whose issue preferences are pretty close to equally divided), and “mostly” (i.e., people in between “consistent” and “mixed”). The second thing I show is the percentage of variance that a single factor accounts for in a factor analysis of the 10 issue items within the given group. Don’t worry if you don’t know what that means—just understand that a bigger number here means a higher degree of ideological consistency.

The CCES sample confirms the big roles of education and race in ideological consistency, but suggests some other details as well. One is that, within college-educated whites, Millennials are substantially less likely to be left-right ideologues. This could say something about Millennials in particular, but, more likely, it’s probably a general effect of age. That is, I imagine that if I looked at a big sample from the mid-1990s, where now it’s the Gen Xers who are the younglings, we would see similar patterns of reduced ideological consistency. Or, to put it another way: Just wait—as they age, it’s likely that educated, white Millennials will come to match the ideological consistency of educated, white older folks.

Another detail is that, among the non-college folks, income shows up as a major secondary factor—here, those with lower incomes are less ideologically consistent than those with higher incomes. This overlaps, of course, with both race and education.

The overall differences here are large. Among non-Millennial whites with 4-year degrees in the CCES sample, around 50% are consistent ideologues. But among people who’ve never been to college and who have household incomes below $40,000, only around 17% are consistent ideologues. Among not-so-poor people who’ve never been to college as well as non-whites with at least some college, only around 25% are consistent ideologues. (And, again, all these numbers are inflated relative to the population as a whole, given the skew in online samples.)

Ideology and demographics

I’ve pointed out the big overlap among items such as demographics, identities, ideology, partisanship, issue opinions, and interests. For example, while some political scientists have argued that the electorate is driven by social identities rather than by ideology or issue opinions, it actually doesn’t make much sense to draw such distinctions too starkly.

Along these lines, we’ve just seen that some groups are more likely to hold ideologically clustered opinions than are other groups. In other words, left-right ideology is a bigger deal for some groups. Does this mean that social identities are less relevant for these groups? Not at all. In fact, in an important sense, the more ideological a group, the more relevant some basic demographics become in predicting their politics.

I show a bit of this in the chart below. Here, I’ve taken the six groups from the prior chart—groups that run from most ideologically consistent up top to least down below. Within these groups, I looked at how some of the big-deal demographics—religion, church attendance, sexual orientation, military service, and gender—predict their liberal vs. conservative leanings (using my 10-issue CCES ideology scale).

The question here is: Within these groups, how big of a difference do these demographics make in dividing liberals from conservatives? The clear overall pattern is that, in general, and especially with regard to religion, demographics are more predictive of issue opinions among more ideologically consistent groups than among less ideologically consistent groups. For instance, on my 10-point scale, non-Christians are around 4.7 points to the left of churchgoing evangelicals when we look within the most ideological group (i.e., non-Millennial whites with 4-year degrees), but non-Christians are only around 1.9 points to the left of churchgoing evangelicals when we look within the least ideological group (i.e., people who haven’t been to college and have incomes below $40,000).

(Note for nerds:  The chart above reports OLS regression estimates from models where these demographics are simultaneously predicting my 10-point CCES ideology scale.)

Put another way, when you know their religion, sexual orientation, military service, and gender, it’s actually a lot easier to predict the various issue opinions of college-educated whites than those of other folks. Increased ideological tendencies actually make key social identities more (not less) relevant. So you shouldn’t view ideology and social identities as competing explanations here—again, there’s a big overlap.

Vaguely interesting (March 29)

(1)  “[L]evels of gender-typed behavior at ages 3.5 and 4.75 … significantly and consistently predicted adolescents’ sexual orientation at age 15.”

(2)  More from Tyler Cowen and Ryan Avent on all the non-working men. (But if you look at my posts here and here, you’ll see why I’ve become more than a little confused by these kinds of discussions.)

(3)  And, speaking of often-repeated things (e.g., today) that aren’t actually true, here’s Auerbach & Gelman on the claimed increase in white mortality.

(4)  “Many pollsters and strategists believe that rural white voters, particularly those without college degrees, eluded the party’s polling altogether — and their absence from poll results may have been both a cause and a symptom of Donald Trump’s upset victory over Hillary Clinton in several states.”

(5)  “High-level professionals and managers respond to rising unemployment by withdrawing support for raising tax progressivity. By contrast, manual workers (along with low-level professionals and managers) respond to rising unemployment by increasing their support for tax progressivity.”

(6)  “[I]deologically populist Americans … have historically held issue preferences that matched the policy positions expressed by Donald Trump in the 2016 primaries. … [T]he Trump candidacy was able to activate a segment of the electorate that has historically not been part of the GOP electoral coalition.” (Or, as I’ve said: “there were a lot of downscale whites who weren’t voting because they didn’t have candidates plausibly offering what they generally wanted—white nationalist priorities combined with left-leaning economic positions. … He built it and they came.”)

Public opinion on budget trade-offs

Americans show a mix of self-interest and ideology in their reactions to trade-offs among domestic spending, defense spending, and taxes.

The 2016 Cooperative Congressional Election Study (CCES) asked its respondents an interesting item on budget priorities: “The federal budget deficit is approximately $1 trillion this year. If the Congress were to balance the budget it would have to consider cutting defense spending, cutting domestic spending (such as Medicare and Social Security), or raising taxes to cover the deficit. Please rank the options below from what would you most prefer that Congress do to what you would least prefer they do: Cut Defense Spending; Cut Domestic Spending; Raise Taxes.”

The item is explicitly built around trade-offs, given that it asks people for a rank order of their preferences among the three options. Overall, cutting defense spending was the most popular (37% tagged it as their first choice and only 23% as their last) while raising taxes was the least popular (24% placed it first while 42% placed it last). This is neither a “liberal” nor a “conservative” pattern—the most popular item (cutting defense) and the least popular (raising taxes) are both things that liberals tend to support and conservatives tend to oppose, in some form or another. Really, it’s the prioritization of domestic spending relative to the other two that divides the sides. (Indeed, the preference for domestic cuts was one of the items included in the 10-issue CCES ideology scale that I analyzed the other day.)

Who’s more likely to want to protect defense spending by cutting domestic spending, protect domestic spending by raising taxes, and so on? I examined a large range of demographic variables and identified the ones that seem to be making the most substantial contributions.

The chart below shows the results. For data nerds, I’ve included a set of regression results (see the notes below the chart for details). For everyone else, just focus on the “and what they mean” section at the bottom of the chart. In short, different demographic features predict different splits in priorities.

The most common trade-off was between domestic spending and defense spending. There were various features where, on average, it’s more likely for folks to want to protect domestic spending at the expense of defense spending (being black, atheist/agnostic, and LGBT), and others where folks are more likely to want to protect defense spending at the expense of domestic spending (being a veteran or a Christian, particularly an evangelical).

(Notes: The response items are coded 1=first preference, 2=second, and 3=third. The predictor variables are coded 1=applicable and 0=inapplicable. The results are from OLS regressions, and I’m reporting unstandardized coefficients. All evangelicals are also Christians, so the effects of “evangelical” are over-and-above being “Christian.”)

There were also features that predicted wanting to allow higher taxes in favor of protecting either domestic spending (being retired/disabled, atheist/agnostic, and LGBT) or defense spending (being a Baby Boomer or older). Obviously, most of the retired/disabled folks are also older, so this reveals particular support for raising taxes among older folks relative to cutting spending.

And keep in mind that things go the other way as well. The results show that older people and those retired/disabled are relatively more likely to favor higher taxes. But this also means that younger workers are relatively less likely to favor higher taxes, preferring to cut spending. Similarly, the results show blacks favoring domestic over defense spending, but this also implies a relative preference among non-blacks for defense over domestic spending.

Interests and coalitions

Some aspects of these results are consistent with an interest-based perspective. At first, I was surprised that I didn’t see poorer folks being much more likely to want to protect domestic spending (though race shows up). Instead, there was a pretty clear pattern where older folks and retired/disabled folks favored tax increases over spending cuts. But then I went back to the question wording. The question only mentions cuts in Social Security and Medicare as examples of “domestic” cuts. And, sure enough, retirees and disabled folks are more likely to favor protecting this spending (which for many of them is a big deal), trading it off against higher taxes (which wouldn’t greatly affect many of them). Similarly, military veterans want to protect defense spending—some of these veterans are currently enlisted, and, at any rate, almost 10% of the defense budget is allocated to the Department of Veterans Affairs.

The results remind me of another survey showing that, when forced to choose among spending priorities, older folks prioritize Social Security and defense, while younger folks prioritize job creation and education. If you don’t see at least a bit of self-interest there, then you need to check your glasses.

Other features show the potency of current ideological and party coalitions. In particular, atheists/agnostics and LGBT folks are often liberal on lots of different kinds of issues, whereas evangelicals (and particularly white evangelicals) are often conservative on lots of different kinds of issues (see, e.g., here).

Even for religion and sexual orientation, though, there’s a degree of interest-based thinking available. Some of the fight over safety-net spending involves relative preferences between governmental safety nets and private charitable programs. Kurzban and I have argued that people who might rationally worry about discrimination from white, Christian charities (e.g., non-whites, non-Christians, and LGBT folks) ought to have some degree of preference for non-discriminatory governmental safety-net programs.

But it’s clear that some dramatic recent shifts—in ideological clustering and specifically in the economic policy preferences of wealthier non-Christians—also have coalitional aspects. The Reagan era brought about a lasting alignment between religious conservatives and small-government conservatives, pushing many non-Christians to the left and away from their former libertarianish positions. Though I don’t have data directly on point, I’m assuming that the story has been similar for LGBT folks. It also seems likely that we’ve seen a related story with military veterans, as Republican support for defense spending has drawn veterans in and produced some coalitional shifts in veterans’ views on rich-poor and religious issues.

These kinds of coalitional trends aren’t, of course, wholly divorced from interests. They involve folks who really care about a given set of interest-based positions adjusting opinions on other issues that are less important to them. This produces results such as those we’ve just seen for budget priorities, where some predicting features seem interest-based while others seem coalitional. Yet, typically, the underlying patterns of coalitional choice are themselves linked to interests. It would be a real stretch to argue, for instance, that the current Democratic/liberal coalition (which strongly opposes group-based discrimination) doesn’t represent high-priority interests of most atheists and LGBT folks, and vice versa for the Republican/conservative coalition in relation to white evangelicals.

As I’ve said, there’s a big overlap among the standard toolkit of political explanations. It’s usually not very plausible to make this-thing-matters-but-that-thing-doesn’t sorts of arguments when thinking about demographics, identities, interests, partisanship, ideology, and issue positions. It’s all there, intermingled, thwarting facile answers.

The reality and myth of the decline in men’s employment

From the 1970s to the 1990s, the employment rate for prime-age men fell, while for women it rose dramatically. Since 2000, however, both have declined (and partially recovered) about equally. The recent pattern doesn’t point to a particular problem with men.

Men, apparently, are in trouble. In the 1950s and 1960s, typically around 94% of men ages 25 to 54 were working. But in the past 10 years, they averaged only around 84%. And the alarm bells are ringing: The Decline of Men. The Missing Men. Men at Work … or Not. The Nonworking Prime-Age Men. America’s Men Aren’t Working.

In a prior post, I looked at young men’s work and school patterns. There’s been all this talk of how they’re playing video games and living in their parents’ basements, and I wanted to get a better sense of the scope of the problem. I was genuinely surprised by what I saw, or, more to the point, didn’t see. When you look at men in their teens and early 20s, it just isn’t the case that they display a major new trend of idleness. Mostly, it’s just that the Great Recession really was a great recession.

However, heading into prime-age territory, I did see that there were worrying trends for men in their late 20s and early 30s. Their recent rise in idleness went beyond what we’ve seen before.

So for today’s post I wanted to take a closer look at prime-age men, defined as ages 25 to 54. The Bureau of Labor Statistics has employment-population ratios for this group going back to 1948. And as I was looking at that, I started focusing in on the similarities between men’s and women’s employment trends over the past 20 years. And I was again surprised.

There are three charts in the graphic below. The first shows the yearly employment-population ratios for both men and women ages 25 to 54 from 1948 to 2016. The main historical trends are immediately apparent. From the late-1940s through the late-1980s, women’s work rates rose spectacularly, particularly starting in the mid-1970s. They stalled in the early-1990s, but then crept up further, reaching their as-yet all-time high in 1999/2000.

Prime-age men, on the other hand, with various ups and downs, have been on a slow overall decline since the 1970s. They averaged around 94% working in the 1950s and 1960s, 91% in the 1970s, 88% in the 1980s and 1990s, 86% in the 2000s, and 83% in the 2010s.

But here’s the thing. Women’s employment has been declining since 2000 as well. You can see it more clearly in the middle chart, which zooms in on 2000 to 2016. Both men and women failed to reach their 2000 employment level in the period between recessions in the mid-2000s; both were hit hard by the Great Recession; and both have since made partial but not full recoveries, currently landing pretty close to their 2008 employment levels.

It’s the last chart that really shows it. I put men and women on the same scale, looking at both as a percentage of their 2000 employment levels. And there’s the story. Men and women were about equally affected by the early-2000s slowdown; men pulled ahead a bit in the subsequent housing-bubble recovery; then men were hit especially hard in the housing bust and financial collapse; men started recovering first; and now—and this is really the surprising point—for the past few years men and women have been making about equal progress along practically the same slope relative to their 2000 employment levels.

In short, that whole thing about how men have been experiencing a 21st century employment decline relative to women doesn’t seem to be actually, you know, true. Prime-age men are down from 2000, to be sure. But so are women, to basically the same degree.

(Note: The BLS has data through February of 2017, and I wanted to use the most recent data, so each year here goes from March of that year to February of the following year. So, for example, the 2016 numbers combine March 2016 to February 2017.)

I could see—maybe—telling a “recent decline of men” story around 2010 or 2011. Men were getting hit particularly hard then. But I really don’t understand how the employment numbers from 2012 to the present support that narrative. Yes, men are down. But so are women. We can tell all the stories we want about porn and drugs and marriage declines and video games, and how those things are sapping prime-age men’s will to work. But then we need to explain why women have been experiencing very similar declines since 2000. It seems likely that one should be looking for explanations that apply roughly equally to men and women.

Why the spotlight on men?

So, yes, I’m surprised. Given all the hype about declining men, this is not what I was expecting to see. While there is an essential reality to the hype—the percentage of prime-age men working has basically been declining since the 1970s—there’s also a big myth, in that the past 20 years have not seen an overall decline in men’s employment relative to women’s employment. Both have declined (and partially rebounded) to similar degrees, though the timing and scope of their hits from the Great Recession differed somewhat.

Which begs the question: What is it that makes so many people so eager to tell a men-only story about recent employment trends? I get why we tell separate stories about the second half of the 20th century—women’s employment was shooting up while men’s was creeping down. But what’s driving the 21st century narrative, when men and women are showing very similar trends?

Perhaps it has something to do with the fact that, when we really don’t want to do anything to help struggling people, we often focus on some blame-worthy caricature. Perhaps uneducated, responsibility-avoiding men are the high-education liberal/libertarian analog to the right’s welfare queens—the narrative figures that suggest that the only urgent response warranted is vigorous finger-wagging. Or maybe that’s not it. I really don’t know.

Vaguely interesting (March 24)

(1)  “[W]e found no support for the hypothesis that life history strategy predicts cooperation or that early childhood environments interact with current resource scarcity to predict cooperation.”

(2)  “[T]he observed wage patterns are most consistent with men marrying when their wages are already rising more rapidly than expected and divorcing when their wages are already falling, with no additional causal effect of marriage on wages.”

(3)  “The median national [adolescent birth rate (ABR)] fell 40% from 72.4/1,000 in 1990 to 43.6/1,000 in 2012. The largest regional declines in ABR occurred in South Asia (70%), Europe/Central Asia (63%), and the Middle East/North Africa (53%).”

(4)  “Half of the public incorrectly thinks the [ACA] allows undocumented immigrants to receive financial help from the government to buy health insurance.”

(5)  “The ‘game of chicken’ which could be a serious problem for driverless cars.” (Calls to mind Kurzban’s opening story in Hypocrite of the difference between crossing the street in Philadelphia vs. Southern California.)

(6)  “National turnout of eligible voters was 60.2% – 1.6 percentage points above the 58.6% turnout in 2012, though slightly lower than 2008.”

The demographics of liberals and conservatives (with CCES data)

I previously looked how demographics—race, religion, gender, and so on—relate to whether people are more liberal or more conservative on Pew’s ideology scale, which combines ten different issue items (on government spending, environmental regulation, race, immigration, homosexuality, and so on) into a single measure. Today, I’ll do the same thing with data from the recently released 2016 Cooperative Congressional Election Study (CCES).

The best thing about the CCES is that it’s yuge. The Pew data in my prior post included around 16,000 survey-takers, which is pretty damn good, but this CCES sample is over 64,000. (According to dictionary.com, this is pronounced: siks-tee fohr muhth-er fuhk-ing thouzuhnd.)

The pre-election wave of the CCES included a number of questions about policy preferences. I chose a set of ten of these to make my CCES ideology scale—including items on assault rifles, conceal-carry permits, border patrols, deportation, abortion rights, abortion funding, environmental regulations, same-sex marriage, government spending priorities, and the minimum wage (I include the full items at the bottom of this post). Most of the CCES issue questions were in a binary support/oppose format, which made the ideology scale straightforward: For each of the ten policy items, I coded the liberal option as 1 and the conservative option as 0, and then added them all up. Thus, an extreme liberal would score 10 (by giving the liberal response on all ten survey items) and an extreme conservative would score 0.

The CCES has a nice range of demographic information, including race, religion, church attendance, age, gender, education, income, marital status, employment status, sexual orientation, transgender status, whether the respondents have been union members, whether they’ve served in the military, whether they own their home, whether they invest in the stock market, and whether they or their parents are immigrants.

In short, I took the 10-point issue-based ideology scale and started looking for demographic splits. The biggest deals were sexual orientation and religion, so I first split up the sample by the major divisions there. Then I went into the largest of the remaining subgroups and looked for whatever the next-biggest deals were, and created even smaller subgroups, and so on. I stopped when I had 24 subgroups. These 24 groups are mutually exclusive and encompass the entire sample.

And that’s what’s shown on the two charts below. They contain each group’s average score on the 10-point issue-based ideology scale. In the first chart, I show the most liberal six groups and the most conservative six groups. So, the first line is “LGBT; Atheist/agnostic”—these are people who both (1) indicated that they are either lesbian, gay, bisexual, or transgender and (2) chose either atheist or agnostic as their religious category. And it’s just a really liberal group. When asked the ten different issue questions, they chose the liberal responses on 8.5 items on average. In fact, almost two-thirds of these folks are mega-liberals (scoring 9 or 10) while less than 1% are mega-conservatives (scoring 0 or 1). The most conservative group is at the bottom of the chart—straight (i.e., not LGBT), evangelical, white, male homeowners. They average around 2.6, where over 40% are mega-conservatives while less than 3% are mega-liberals.

Overall, on the first chart above, the most liberal groups are a sort of demographic rebel alliance, including many LGBT folks, atheists/agnostics, other non-Christians (Jews, Buddhists, “nothing in particular,” and so on), and racial minorities. (Keep in mind, though, that we’ve got a second chart coming, which will show some not-so-liberal LGBT folks, non-Christians, and racial minorities. For example, LGBT folks who are also either evangelicals or military veterans are actually middle-of-the-road on average.)

The most conservative groups, in contrast, are almost all anchored by straight, evangelical whites. In fact, I ended up with five groups made up of straight, evangelical whites, and they’re all among the six most conservative groups. The other group showing up here includes straight, white, non-evangelical Christians who are male military veterans.

The second chart below shows the 12 groups in the middle. As I mentioned, some of the more interesting ones here involve groups you’d normally think of as pretty liberal—LGBT folks, non-Christians, and racial minorities—that actually aren’t so liberal among some narrower segments. These include: straight atheists and agnostics who’ve never attended college; other straight non-Christians who don’t have 4-year degrees or who are military veterans; straight, Christian Hispanics and Asians; straight, evangelical blacks; and, as already mentioned, LGBT folks who are either evangelicals or veterans.

Another way to frame what’s going on here is to think about the averages on the 10-point issue-based ideology scale as simultaneously influenced by a wide range of characteristics. Some demographic features push the average up in a more liberal direction (being LGBT, atheist/agnostic, black, etc.) and other features push the average down in a more conservative direction (being evangelical, white, a veteran, etc.).

In fact, here’s some actual math allowing you to estimate the average ideological positions of a wide range of very specific profiles:

  • Think of a group defined simultaneously by LGBT status (yes/no), religion (atheists and agnostics vs. other non-Christians vs. non-evangelical Christians vs. evangelicals), whether they attend religious services more than once a week (yes/no), race (white/black/other), veteran (yes/no), whether they have PhDs, MDs, MBAs, or other graduate degrees (yes/no), and gender (female/male).
  • Start with 6.4.
  • Then, if applicable to the group you’re thinking about, add 1.3 for LGBT folks, 1.3 for atheists/agnostics, 0.9 for blacks, 0.7 for folks with graduate degrees, and 0.7 for women.
  • Then, if applicable to the group you’re thinking about, subtract 2.1 for evangelicals, 0.8 for whites, 0.8 for folks who go to religious services more than once a week, 0.7 for veterans, and 0.7 for non-evangelical Christians.

The result gets you very close to that group’s average on the 10-point ideology scale. A quick example. People similar to Barack Obama: start with 6.4, add 0.9 (black), add 0.7 (grad degree), and subtract 0.7 (non-evangelical Christian). The result is 7.3, a rather liberal average.

From these numbers, you can see where the big-deal divisions are. Atheists/agnostics (+1.3) and evangelicals (-2.1) are, thus, on average, 3.4 units away from each other. Blacks (+0.9) and whites (-0.8) are, on average, 1.7 units apart. LGBT folks are, on average, 1.3 units more liberal than straight folks. As shown in the charts above, these can really add up when people contain multiple features pointing in the same direction (e.g., people who are both LGBT and atheist/agnostic), but can also create politically conflicted groups when some major feature pushes one way and another pushes the opposite way (e.g., people who are both evangelical and black).

Unpacking ideology

On the issue-based ideology scale I’m using here, fewer than 30% of the CCES sample are mega-liberals (with scores of 9 or 10) or mega-conservatives (with scores of 0 or 1). The other 70% have at least a couple of liberal views and a couple of conservative views among their ten issue positions. In fact, almost a third of the sample have more-or-less equal numbers of liberal and conservative issue opinions, landing in the middle range from 4 to 6.

Ideological consistency is a big and growing deal, but there remain interesting domain-specific factors creating ideological divergence. I’ve showed some of that in various posts I’ve done (mostly using Pew data) on the demographics of specific issues—for example, racial discrimination, marijuana legalization, income redistribution, and views on immigration and Islam—and especially when I directly contrasted white nationalist vs. economic issues, white nationalist vs. lifestyle issues, and economic vs. lifestyle issues.

And then Kurzban and I have a book that systematically goes through the varying demographic differences driving a wide range of political issues, mostly using data from the General Social Survey (GSS). There, we also spent a lot of time trying to figure out why there are all these domain-specific demographic patterns, and we primarily chalked it up to interests. Really, though, lots of the major pieces of political analysis are hard to tease apart—there’s a big overlap among interests, identities, demographics, ideology, partisanship, and issue positions.

I might do some issue-specific analyses of the CCES data in future posts. But online panels like the CCES are probably better for large-scale ideological patterns than for specific issues. Ideological clustering tends to be significantly stronger in online samples (and then it’s a bit less strong in phone samples like Pew, and then weaker still in knock-on-doors samples like the GSS), so the CCES probably overstates the similarity of demographic predictors across different kinds of issues. But, still, I might give it a shot. It’s just a blog after all.

Notes for nerds: Variables and terminology

I used CCES’s “commonweight” weighting variable for all analyses.

Here are the ten policy items from the CCES that make up my ideology scale: Ban assault rifles (support=1 and oppose=0); Make it easier for people to obtain concealed-carry permit (oppose=1 and support=0); Increase the number of border patrols on the U.S.-Mexico border (not selected=1 and selected=0); Identify and deport illegal immigrants (not selected=1 and selected=0); Always allow a woman to obtain an abortion as a matter of choice (support=1 and oppose=0); Prohibit the expenditure of funds authorized or appropriated by federal law for any abortion (oppose=1 and support=0); Strengthen enforcement of the Clean Air Act and Clean Water Act even if it costs US jobs (support=1 and oppose=0); Allowing gays and lesbians to marry legally (favor=1 and oppose=0); Raise taxes and cut defense rather than cut domestic spending (favor=1 and oppose=0); Raise the federal minimum wage to $12 an hour by 2020 (for=1 and against=0).

To arrive at the issue set, I used stepwise regression involving a number of CCES policy items to predict self-labelled ideology, party identification, and the two-party 2016 presidential vote. I selected the ten items that made the biggest contributions to these regressions. That is, I wanted a set of items, each of which contributed in its own way to predicting broad political orientations and voting.

Some terminology: “White” includes non-Hispanics who were coded as either white, native, or other. “Hispanic/Asian” includes Hispanics (regardless of other racial category), Asians, Middle Easterners, as well as mixed-race individuals. “Evangelical” includes all non-Catholics who identified as “born again or evangelical” as well as Mormons.

Social science for the pleeps