Getting Educated about Working Class Whites

[Short Version: A university education is one part vaccine against lies and one part credential for entry into the middle class. Which part explains the split in the white Trump vote? Mostly the vaccination against lying part. So stop using education as a proxy for who’s in the working class!]

There have been a boatload of stories about how “working class whites” swung the US election in favor of Trump. Most of these stories, when you look at them closely, use educational divides to define class. So that:

White working class = non-Hispanic whites without university degrees

And indeed, evidence would seem to indicate that this group swung heavily toward Trump. The response, in many quarters, has been to imagine that white working class voters have been left behind in the de-industrializing economy of the USA. The vote for Trump was a vote to shake up the system, speaking of the pain and marginalization of disenfranchised factory workers and unemployed coal miners – especially in the American heartland. In more nuanced reporting, Trump voters are thought to share a “deep story” of resentment, directed at others “cutting in line” in pursuit of the American dream. (See Isaac Martin‘s thoughtful and critical review of this reporting). But let’s get back to some fundamental measurement issues. Since when was university education just about class, or class just about university education?

To be fair, universities have been selling themselves as the route to upward mobility (and/or maintenance of privilege) for a long time now. And we hear a lot about declining opportunities for those without university degrees, including in research on recent mortality trends. There is also great sociology that conflates these issues, if usually in nuanced form, as in Annette Lareau‘s very teachable Unequal Childhoods, where the big divide documented is labeled as class-based, but mostly concerns the interaction of primary schooling with different parenting styles for those with and without university educations.

Lareau’s work is nuanced and complicated in part because of how she studies education systems. These provide status and privilege directly, through credentialism, offering perhaps the clearest basis for thinking of universities as producing social classes. But Lareau shows how education systems also work in conjunction with distinct sets of parent-child interactions to inculcate particular habits. Some of these are about how to get authorities (like teachers) on your side. But others are more directly about how to use systems to gather and sort through information, as in doctor’s visits. Schools can help kids learn things, especially in conjunction with particular “classed” parental interventions. While Lareau studies elementary schools, the lesson should carry over into universities. In an ideal world (indeed, my ideal world!), university educations aren’t just about getting good jobs and reinforcing class divides. University educations are also about learning; about helping people sort through information. For instance, university educations may assist in discerning truth from lie.

To return to the 2016 presidential election: there’s been a lot of lying going on recently.

So what role did completing a university education play in the 2016 election? Was education primarily about white middle class winners from white working class losers, who correspondingly turned to Trump for their salvation? Or was the role of education primarily about sorting truth from lies?

Armed with the recently released ANES (American National Election Study) 2016 results, I think I can make a pretty strong case for the latter interpretation.

First, to establish some basic points:

Point 1) Education can not be reduced to class (nor vice-versa).

If only we could just ask people what class they belonged to! Then we wouldn’t need to use education as a proxy. ANES 2016 to the rescue! People get to (or are forced to) claim their own class identification. I’ve simplified education and self-assigned class categories (the latter drawing from combining pre- and post-election questions), to see how they fit together. Here’s what I get:


There’s a definite relationship between education and self-assigned class, but it’s not at all a perfect fit. Most people make some choice between defining themselves as working class and middle class, although a few are willing to identify as lower or upper class. What’s striking is that within any given education category, you’ll find all four of these class self-identifications. There’s definitely a relationship, insofar as middle-class and upper-class identification rise with educational level, but there’s plenty of messiness, with a ton of people identifying themselves as middle class without a university degree.

But maybe this is all some kind of false consciousness? How about we run this again by pre-tax annual family income quartile and use that to assign class?


Once again, we see a clear relationship between education and income-assigned class, but it’s far from determinative. In many ways, this is a better comparison, insofar as people aren’t forced to identify with a (culturally poorly defined) class divide between “working” and “middle” and there are a lot more people who fit into the top and bottom quartiles (the quartile cut-offs, for those who care, are $27.5k, $60k, and $100k). But in other ways it’s a worse comparison, insofar as it ignores self-identification as well as important distinctions in both partnership status (adding a dual income can easily move someone up a quartile) and geography (relative income varies a lot by place).

Still, I’ll mostly stick with income quartile assigned class to make a few further observations. After all, family income can tell us a lot about marginalization. If we’re concerned about a white working class that’s been left behind, it might be more important to measure the resources income brings directly rather than thinking of class as a cultural identification. But both could potentially tell us more about marginalization than education.

Home ownership is another marker of middle-class status for many people (hey! Read my book! Or one of many others out there making roughly the same point). So who’s left out of the middle-class in terms of home ownership? Let’s check via our education v. income splits:


By and large, home ownership follows income rather than education. The lower your income quartile, the greater your likelihood of remaining a renter. This shouldn’t be too surprising. Mortgage lenders want to know your income and credit rating, but they really don’t care about your education. Indeed, there’s evidence from the recent past that lenders don’t necessarily want you to read the terms of your loan too closely. Education doesn’t track onto homeownership as a measure of class nearly as well as income. Let’s try a better measure of marginalization, tracking popular discourse about a white working class that’s been left behind. Who is most likely to be unemployed or disabled?


People who are unemployed or disabled mostly show up in the bottom income quartile. There is a shallow relationship to education (more highly educated people look less likely to show up as unemployed or disabled), but it seems to me marginalization is overwhelmingly about being stuck in that bottom income quartile. Those are the people who have truly been left behind. But we might also measure people’s feelings of dissatisfaction with their lot in life more directly – at least in the ANES data, where they’re asked “how satisfied are you with your life as a whole?” Most people are actually pretty satisfied, so here I group together those who are unsatisfied and those just “slightly satisfied.”


Lo and behold: here too I’m seeing mostly a relationship to income. Those in the bottom two quartiles are far more likely to be dissatisfied than those in the top two. To the extent there are relationships with education they look curvilinear, moving in different directions by  income quartile. A case could be made that people experience dissatisfaction both from marginalization in terms of their everyday resources, as well as in terms of the respect they feel their entitled to. I’ll set this aside for the moment to return to a central theme, education is a bad proxy for marginalization.

So if education is a bad proxy for social class insofar as we’re mostly talking about who’s getting (and feeling) marginalized in the USA, then what good IS education? And why does it so powerfully predict who voted for Trump? If we think of university educations not just in terms of the class credentials they provide, but also in terms of the skills at sorting through information we hope they provide, then we might imagine people who complete their university degrees are better at sorting lies from truth. Let’s test this. How does believing Barack Obama is Muslim breakdown by education and income quartile?


Hey! Now THAT looks like an education effect! As a faculty member at a big university, this is somewhat heartening. Maybe with every class I teach, my students are actually getting better at telling truth from lie. It’s working, it’s working! On the other hand, I’m not seeing big or consistent income effects here. This isn’t a class story so much as it’s a truthiness in education story. Completing a university education, working through all of those core classes in addition to electives, can provide an inoculation, of sorts, against lying. We’ve developed an effective vaccine against con-men! It’s called the university! (Not 100% effective, I know, but not half-bad).

So how does education versus income quartile play out in predicting a vote for Trump among those who actually bothered to vote?


Wow! There’s that education effect again!

Trump lies all the time. It’s pretty well documented. Those most likely to fall for the con are those least inoculated against it. This is not a straightforward story about the marginalization of the “white working class” (a story that always occludes the marginalization of everyone who isn’t white). Once you control for education in who voted for Trump, class effects either disappear, or actually turn back toward their “normal” alignment (more marginalized folks voting for more supportive candidates). Controlling for education, the unemployed and disabled tended to vote against Trump, as did renters. These election results were never about an uprising of the downtrodden (the dissatisfied on the other hand, tended to vote for Trump, which speaks perhaps to the more complicated relationship we might imagine between satisfaction in life and feelings of entitlement). Education was the big effect we saw in an election rife with misinformation – much of it weaponized against American democracy. Controlling for something as simple as people believing that “Obama is Muslim,” reduces the education effect considerably. The viral lies were effective once they got past our defenses.

So here’s a positive lesson from this election: if I sometimes doubted the value of my job prior to 2016, I can now rest a little easier. Universities aren’t just about reifying privilege, so it’s time to stop using degrees as a shortcut for talking about social class! And it’s time to take seriously what we’re doing in terms of helping people sort the truth from the lies. [In case you’re wondering, yes, it’s possible this whole post can be read as a pep talk to get myself to finish my grading…]



Here’s a full logistic regression model predicting a Trump vote, for those intrigued by such things:


I’ll readily admit that I’m a novice with ANES data – this is the first time I’ve played around it. I ran it through my old version of Stata 10. Happy to share my Stata code (as .pdf) Do-file-text



So here comes the Cascadia Urban Analytics Cooperative! A new cross-border initiative bringing together UBC with the University of Washington! I’ll be generally curious to see where this goes. The notion of Urban analytics, of course, would suggest some interest in urban issues. But so far, at least, there’s very little mention of anything involving urban studies, urban geography, urban sociology, planning, law, or social science of any sort. It’s early days, of course, but I’d be a bit more encouraged if I saw some mention that “urban” implied people living in cities, and we have some relevant expertise that might be worth tapping into!

In the meantime, here’s the four program lined up so far (quoting from the press release):

  • The Cascadia Data Science for Social Good (DSSG) Summer Program, which builds on the success of the DSSG program at the UW eScience Institute. The cooperative will coordinate a joint summer program for students across UW and UBC campuses where they work with faculty to create and incubate data-intensive research projects that have concrete benefits for urban communities. One past DSSG project analyzed data from Seattle’s regional transportation system – ORCA – to improve its effectiveness, particularly for low-income transit riders. Another project sought to improve food safety by text mining product reviews to identify unsafe products.
  • Cascadia Data Science for Social Good Scholar Symposium, which will foster innovation and collaboration by bringing together scholars from UBC and the UW involved in projects utilizing technology to advance the social good. The first symposium will be hosted at UW in 2017.
  • Sustained Research Partnerships designed to establish the Pacific Northwest as a centre of expertise and activity in urban analytics. The cooperative will support sustained research partnerships between UW and UBC researchers, providing technical expertise, stakeholder engagement and seed funding.
  • Responsible Data Management Systems and Services to ensure data integrity, security and usability. The cooperative will develop new software, systems and services to facilitate data management and analysis, as well as ensure projects adhere to best practices in fairness, accountability and transparency.


Down at the University of Washington, the new cooperative will be based at Urbanalytics, a University of Washington initiative drawing on “civic hackers” to think up creative solutions to making urban life better.They have a variety of affiliated projects, including one on “housing stability,” apparently led by a physicist and a neuroscientist. I’ve no doubt these are creative and clever people with lots of insight to offer. But as someone who works in housing – an extraordinarily complicated and policy-heavy field requiring a lot of local knowledge – I worry. Wouldn’t you want to add to your team, say, someone who actually knows something about, I don’t know… housing?

On the whole, it’s neat to see the efforts here, and there’s great potential (calling Jens Von Bergmann!) There’s also increasingly a lot of data to play around with, and data scientists have an important role to play. I just worry that brand new efforts to be socially responsible and make cities better won’t get very far without drawing upon the existing strengths of people who have been working toward those efforts for a long, long time.


At Home Looks Like…

A few years ago I received a grant (with co-I Frank Tester) to explore more closely the connections between housing and home in two locations with marked housing crises: Vancouver, BC and Arviat in Nunavut.  And so the Making Housing Home project was born. The basic starting point for the work was that housing was an important component of home, but did not, in and of itself, constitute home.  Instead home could be found in our routines and connections to a wide variety of people, places, and things.  So we set out to document everyday routines and their relationship to housing.  Mostly we worked through in-depth interviews and collaborative calendar and map construction projects.  But we tried as many different ways to get at home as we could think of.  One sub-project involved working intensively with youth in both Arviat and Vancouver to get cameras and some basic photography training into their hands and let them document what “at home” looked like for them.

One member of my research team, Karina Czyzewski, took an especially critical lead role in this sub-project.  Working with the youth in Vancouver, and with other team members, she put together an exhibit at the Roundhouse Community Centre.  Then she brought photos and descriptions of home together into this wonderful booklet, which we printed off and gave to all youth participants and several other community partners in both Arviat and Vancouver. I’m now providing an electronic copy of the booklet here to get as wide exposure for it as possible.

So here’s the booklet!  (Or click on the image below!  Note: 19MB size file)


While our research is still on-going, and I’ve got a whole lot of data analysis ahead of me, I think this is a good time to get some of the voices of our participants out there, speaking in their own words about their experiences of home.  I think the results speak for themselves.  So… at least for the moment… I’ll stop talking about them.  Enjoy, and feel free to share widely!

Who is an “average voter”?

I’m restraining myself from writing too much about the US election, but I’m definitely reading about (and obsessively tracking) the race.  In light of that, here’s a piece I really enjoyed from the NYTimes: “So What Do You Think Of Hillary Clinton Now?”

Effectively, it’s one of those pieces where a reporter (Emma Roller) goes out and interviews a bunch of people on the street, in this case to gauge their reception to Hillary Clinton’s nomination as Democratic candidate for US President.  Or as Ms. Roller put it:

What does Mrs. Clinton’s presidential nomination mean to average voters, die-hard Democrats and Bernie or Busters? We asked a few here in Philadelphia.

I suppose, as I read through, I can pick out a few of the “die-hard Democrats” and the “Bernie or Busters,” but who is supposed to be an “average voter?”  And what does that even mean?

In a straightforward statistical sense, we can identify who falls into the group of “modal voters,” at least once we have a set of votes.  Modal voters would include all of those who voted for the candidate who won the most votes.  If we can arrange candidates on a scale (say left-to-right), then we can also come up with a population of voters that we could draw from in order to select a “median voter.”  But in each case, if we wanted to find someone to exemplify the modal or median voter, we’d still have to randomly select from all of the possible people that would fill in that category.  To put it mildly, there is a lot of diversity there.  But finding someone to exemplify an “average voter?”  I have no idea how that might be accomplished.

Here I think Emma Roller actually means something different.  She’s looking for someone who isn’t selected into the streets of Philadelphia as either a Democratic delegate (like Ms. Ali, her first interviewee) or a Bernie-or-Bust protester (like  Ms. Ernst or Mr. Hainer), presumably making them more “average” in terms of their level of political participation.  Still, it’s tricky to pick these people out.  Do we count Ms. Driver and Ms. Sanabria (two of my favorite interviewees)?

Ms. Driver said she and Ms. Sanabria spontaneously decided to rent a car and drive to Philadelphia from Washington for Mrs. Clinton’s nomination after watching Michelle Obama’s speech on Monday night. Ms. Sanabria texted her.

“She was like, ‘I’m crying!’ and I was like, ‘No, I’m crying!’” Ms. Driver said. “We have to go. This is a historic moment. We can’t miss this.”

That sounds like a pretty unusual (and kind of awesomely spontaneous) level of political participation.  But even the people who seem more “normal” in their orientation to politics, like Mr. Schumann, are also really wacky (as she notes, at 59, “Mr. Schumann is the oldest person I’ve seen playing Pokémon Go” – making him my another of my favorite interviewees even though I really, really don’t play Pokémon Go).  As a matter of fact, most people are kind of wacky, as I’ve often witnessed in my own interviews with people.  It’s part of what makes the job of sociologist fun.  And the US, like Canada, is a diverse country, full of idiosyncratic wackiness.  So what use is it attempting to find an example of anyone average?

To return to a theme, one reason I like the kind of thing we see in Ms. Roller’s piece is that good stories attached to real people quickly remind us just how devoid of human messiness our statistical averages may be.  That’s not to say that the statistical stuff is wrong and we should all resort to “voice of the street” analyses.  Indeed, statistics is ultimately how we’ll figure out who is going to win this election.  But if you really want to get into how or why someone wins this election, the stories help remind us of the underlying diversity and complexity of peoples’ decision-making processes.