Vancouver’s Transformation: from Phoenix to Montreal in under 60!

I can never quite get over just how dramatically the residential stock of Vancouver has transformed away from its former dominance by single-family detached houses. As recently as 1961, proportionately more of Vancouver’s residents lived in detached houses than in Phoenix, Arizona today (well-known for its inescapable sprawl). Less than sixty years later, Vancouver has now toppled both New York and Montreal as the least house-dominated metropolis in North America! That’s a dramatic transformation!

VancouverTransforms-2016

Above I’ve updated an older figure of mine that made its way into my (award-winning!) book with the newest census data from 2016, when Vancouver definitively displaced Montreal as the least house-dominated metropolis in North America. What’s led to and resulted from this dramatic change in Vancouver’s housing stock? That’s all in the book!

DeathLifeHouseCover

Surveying Realtors

I’m always both fascinated by and wary of the data produced by real estate associations. I initially had a whole chapter in my book devoted to taking apart survey data on consumer preferences put together by real estate organizations (sadly, but probably correctly, it got cut). Here’s one of my favourite such survey questions (see slide 8) based on what Vancouverites might want to buy if, inspired by the Bare Naked Ladies, they had a million dollars. (Nearly a quarter chose to keep the $1 million and rent!)

I notice that such data is back in the news again, this time based on surveys of realtors, from April 2016 to April 2017, who’ve recently represented buyers in sales. The write-up leaves a lot to be desired in terms of methods (what’s the sample size of realtors and buyers? what’s the response rate? are there warning flags in terms of representation of realtors and buyers?) It’s also unclear whether this represents entirely re-sale or also sales of new residential real estate. This makes it difficult to evaluate the quality of the data. But it’s still kind of fun to play around with it.

I’ve broken the data, as presented by REBGV, down into my own categories. Here’s type of sale:

REBGV-Data-TypeSale

According to recent surveys of REBGV realtors, investment purchases make up about one in five sales. The role of foreign investment (largely, but not entirely post-Foreign Buyer Tax) is relatively small. But survey quality, about which we know little, likely matters a lot for these estimates. Are some realtors and real estate companies more likely to respond than others (especially those, like New Coast, likely to especially target overseas buyers)? Other important details are also missing: Are sales of newly constructed properties included? How do realtors decide who counts as a foreign investor vs. a domestic one?

Setting investment purchases aside, first-time buyers, targeted by a much-derided recent BC Liberal finance assistance program, make up nearly a third of buyers. That’s a pretty big chunk of sales! But here it’s not clear quite what counts as “first-time.” First time in Vancouver, first time in Canada, first time at all anywhere? Other moves, making up nearly half of all purchases, tend to be from buyers moving around from one dwelling to another.

Finally, there’s really interesting data breaking down moves of owners moving from one property to another by type (condo apartment, townhouse, and detached house) at old home and new. I simplified this into lateral moves, moves to likely bigger units (upsizing), and moves to likely smaller units (downsizing). Many general life cycle models of housing assume households tend to upsize over time as they grow, the better to fit with children. Downsizing only (maybe) occurs after retirement or when children move out. But with Vancouver steadily moving away from single-detached houses, upsizing is the least likely type of move between owned units. Instead, most moves are either lateral (e.g. apartment to apartment) or downsizing. That’s pretty interesting, and likely reflects, in part, how people moving here from elsewhere in North America typically find a house out of reach.

And just where are people coming from?

REBGV-Data-TypeMove

Hmmm… returning to the data quality issue, it’s a little concerning to me that the “investors” category in this question is so much smaller (14%) than in the previous question about type of sale (20.8%). Where did the extra investors go? Did some of them move as they made investment purchases? Were others counted as living in the same community? Weird.

But we get some idea about what proportion of sales represent people moving here from beyond the Metro area, and it’s about 12%. That could account for many of the downsizers, as they reckon with the realities of Vancouver’s pricey market (esp. for single-family detached homes). Another healthy chunk might involve retirees (more on that in a second).

Setting aside investors, we can actually do a comparison of where moving buyers are coming from by looking to Census data (or more accurately, National Household Survey data). The 2016 data on mobility and migration aren’t out yet, but the 2011 data (limited access here, but also recently out in IPUMS) provides a breakdown for those who’ve moved in the past year. Limiting the sample to those in Metro Vancouver, I looked at household heads who’d moved in the past year and owned their own home. How did where came from match up to REBGV data in 2016-2017?

REBGV-Data-TypeMoveCompareCensus

That’s actually a pretty good match! There is some difference in terms of who the Census thinks is moving within their own community relative to who realtors think of as moving within their own community. This likely relates to shifting definitions of communities (again, not defined in the REBGV data). But looking at the proportion of new buyers moving within the metro area (in green) relative to those moving in from away (blue and pink), the figures are actually quite close, at about 86% of non-investment residential sales being to local buyers.

The Census from 2011 would suggest slightly more recent buyers moving to the area came from outside Canada than the REBGV data from 2016-2017, but not by a lot (7.4% to 5.8%), and the disparity could arise from either historical change (including the imposition of foreign-buyer tax) or from issues with data quality (see above). Still, a pretty good match.

It’s actually harder to match up the “demographic” categories used by REBGV data to census equivalents. But playing around with the community profile data from BC Stats, I did my best. Here’s how new buyer households in the REBGV surveys from 2016-2017 kinda, sorta stacked up against all households in Metro Vancouver by household types in 2011.

REBGV-Data-TypeHH-CompareCensus

Again, it’s tricky to make sense of REBGV categories and match them up to Census categories (the census, for instance, does not differentiate between “young couples without children” and “empty-nesters,” and I’ve no idea how these were defined for the realtor survey either). I also don’t know how demographics on investors were tabulated, or where they fall relative to households looking to buy a place to live. But the general match-up between all households (from 2011 Census) and new buyer households (from 2016-17 REBGV survey) looks plausible to me in terms of what I might expect. New household formation drives a lot of sales. So couples without children are disproportionately likely to buy a place while retirees (or those age 65+ in the Census) don’t actually move all that much (there’s a lot of aging-in-place).

I don’t know that I have a big takeaway from all of this data exploration. I think the REBGV data remains kind of sketchy for estimating investment purchases until we get some basic information about data quality and representativeness out of the way. But setting aside investors, the data on where new buyers are coming from when they move within or to Vancouver lines up well with what I’d expect from the census, which is reassuring and kind of cool.

Bright Lights, Big City, Bigger Metro Area

The young are generally drawn to the central cities of big metropolitan areas in North America. That’s where the bright lights can be found! The action! The scene! And also most of the universities, the majority of metropolitan rental stock, lots of the jobs, etc.

Vancouver is no exception. As revealed in previous posts looking at net migration patterns, the City of Vancouver really draws in the young people, both from the surrounding suburbs and further abroad. Then many of those young people gradually have kids and settle down into the surrounding suburbs again. Others remain, renewing the population of children and middle-aged stalwarts in the city. For the metro region as a whole, just how many people remain in the central city as they age and how many move out to the suburbs might not matter a great deal. But for a variety of municipal purposes (including the vitality of school districts), these patterns can matter a lot!

So is Vancouver weird in its age distribution with respect to what proportion of people live in its central city?

One issue in answering this question is the relative size of the City of Vancouver relative to the metro area as a whole. The City of Vancouver, in 2016, reached just over 630,000 people. But the metro area (CMA) of Greater Vancouver is much larger, reaching nearly 2.5 million people. In other words, the City of Vancouver is at the centre of a fragmented metro area much larger than itself. This is quite different from the situation of many other central cities where their boundaries contain large portions of their own suburbs (like Edmonton or even Toronto), obscuring the mobility of the young. Ideally we’d want to compare Vancouver with other metropolitan areas with central cities of about the same size to get a feel for how weird we might be.

Cascadia to the rescue! Let’s look to our sister cities to the south. The City of Portland’s population, at just over 610,000 in 2015, is quite comparable to the City of Vancouver. The Metro population of Portland, at just over 2.3 million, is also nearly the same. For good measure, let’s also throw in Seattle, with a city of similar size (just over 650,000), but a significantly larger metro area (3.6 million).

So how does the proportion of various age groups residing in the eponymous central cities compare across these three metropolitan areas?

Cascadia-AgeGroupCentralCity

There’s definitely a pattern here. Central university cities within much larger metro areas tend to concentrate young adults. They also concentrate the elderly, though to a lesser extent. But they consistently lose those in middle age (and their school-aged children) to the more suburban parts of the metropolitan area.

Overall Vancouver looks a lot like Portland and Seattle to me. This despite quite different housing markets and housing stock. If anything, what stands out is that the City of Vancouver seems to be retaining MORE of its school-aged children than its similarly positioned neighbours to the south. And it’s also attracting a greater proportion of the elderly.

What else have I learned? Cascadian comparisons are actually pretty cool! Bring on the Cascadia Urban Analytics Cooperative!

My award-winning book wins an award!

The Death and Life of the Single-Family House just won the Canadian Sociological Association‘s John Porter Tradition of Excellent Book Award. I’m truly honoured by this award, especially by the company it allows me and my book to keep! My thanks go out to the review committee, and also to my very supportive team at Temple University Press., especially the editors of the Urban Life, Landscape, and Policy series.

I’m joined by three other members of the UBC Sociology Department on the list of awards handed out by CSA this year, including my good friend Sean Lauer who won the Angus Reid Applied Sociology award as a Practitioner. Incidentally, he and fellow faculty member Carrie Yodanis also have a book out this year, called Getting Married: The Public Nature of Our Private Relationships. Two of our graduate students, François Lachapelle and Patrick Burnett also won an award for their paper, “Canadianization Movement, American Imperialism, and Scholastic Stratification: Professorial Evidence from 1977 to 2017.” To be sure, this is important stuff (and I say that as an American immigrant to Canada and beneficiary of the processes described).

But back to the company my book gets to keep! Through the CSA Awards, I’m excited to discover Dalhousie Prof. Karen Foster‘s new book, Productivity and Prosperity: A Sociological History of Productivist Thought, published by the great team at Univ. of Toronto Press. (I’m also looking forward to reading their recently published Gentrifier, but that’s another story). Prof. Foster’s work looks right up my alley in terms of trying to get at how economic concepts like “productivity” get measured and talked about in ways that are both socially constructed (often in a problematic fashion) and also highly consequential in terms of how they shape public policy. Really cool stuff – I’m looking forward to reading it!

The list of past award winners of the John Porter Award also places my book in brilliant company. I’ve been longing to get a copy of Vic Satzewich‘s book, Points of Entry: How Canada’s Immigration Officers Decide Who Gets In, from the excellent UBC Press, for quite some time now. This is an area of real interest to me (for instance, I regularly have students read some of the training documents for how immigration officers determine which marriages are “real”). Prof. Satzewich’s influential work has been cited by Canadian policy-makers in recent leadership debates (if wrongly), and it’s another I’m really looking forward to reading.

The rest of the list is equally as brilliant (Lesley Wood’s Direct Action, Deliberation, and Diffusion ; Elke Winter’s Us, Them, and Others ; Andrea Doucet’s Do Men Mother? ; Kay Anderson’s Vancouver’s Chinatown – which I cite in my own book! – and many more besides! See the list to fill out your own bedside table).

DeathLifeHouseCover

Now I’ll go back to pettier concerns, like listening to myself repeat the phrase “my award-winning book” over and over again, and envisioning how it will look the next time I revise my CV and find myself with something lovely to plant in the vast, otherwise empty landscape of its “Awards” section.

 

Good Age-Specific Net Migration Estimates Come in Threes!

Recently I posted on how we’re still not seeing any big age-specific losses in net migration figures in Metro Vancouver following the release of 2016 Census data. To summarize, there is STILL no flight of the millennials, BUT maybe there’s a slow leak of the Baby Boomers, which might be seen as evidence of “cashing out” of the local real estate market.

Today I wanted to provide both some metropolitan comparisons to note how Vancouver’s patterns fit with a couple of similar places, and also some municipal comparisons within the Metro Vancouver area. I also wanted to make some technical adjustments in how I modeled mortality* as I aged people through the past five years to estimate net migration, which really matters for older adults (not so much for the young). Again, I’m using 2011 and 2016 age distributions drawn from census profiles to get at age-specific net migration estimates for each of the metro areas and municipalities below.

First let’s compare Vancouver as a metropolitan area to two other metro areas: Edmonton and Toronto. I like this comparison primarily because Vancouver is nestled nicely between these two areas in terms of size, and they’re all big university towns.

ThreeMetroNetMig-2016

For Vancouver, you may notice that the figure looks very similar to what I posted two days ago, up until you get to folks in the 70s and above. That’s where mortality effects really start to matter! I think the above is a better approximation of those effects, but it’s tricky to get them right.

Comparing Vancouver to Toronto and Edmonton, what stands out most for me is just how similar these three metropolitan areas look! Metro Edmonton has grown faster over the last five years in % growth terms, but age-wise, the basic pattern of growth is the same as in Metro Vancouver or Metro Toronto. Young people (including Millennials) pour into all three of these areas, and then mostly stick around.

I noted in Vancouver there was new evidence (at least new to me) of a slow leak of Baby Boomers over the last five years. It appears this leak is also showing up in Metro Toronto, with a very similar pattern. It appears there are fewer folks in their late fifties and sixties than might be expected, suggesting they’re leaving town (cashing out?). Then people in their seventies and above start returning (probably for the good health care & related facilities).

There is also a later-life leak of Metro Edmontonians, but it starts later and never quite stops until the latest age. This could reflect more of a straightforward retirement and return home effect for the many folks drawn to the region, but it’s hard to say. At any rate, all later life migration patterns are dwarfed by the influx of younger adults (and their children) into these growing regions. I don’t see a lot of cause for concern about any particular age-groups shying away from our rapidly growing metro areas.

What about within Vancouver’s metro area? I’m somewhat ambivalent about emphasizing municipal differences in age-specific net migration patterns insofar as metropolitan areas tend to be tightly integrated. When a group disproportionately moves over the border from one municipality to another, it doesn’t have a big impact on the vitality of the region as a whole. Nevertheless, it’s worth tracking, and it certainly can have big implications for quite local livability, diversity, development, and transportation questions.

Here I’m just going to compare Vancouver and Surrey, the Lower Mainland’s biggest two municipalities, with Maple Ridge, a smaller suburb further out.

ThreeMunisNetMig-2016

Here you really get a sense of how tightly connected central cities and their suburbs can be. As the region’s central city (and biggest university town), Vancouver receives an ENORMOUS influx of young people. Then, as they move into their thirties (and often start having children of their own), they tend to move out again, slowly leaking out of the City thereafter. Nevertheless, so many young adults move to the City of Vancouver that they overwhelm the later leavers. In net terms, the majority of young adult arrivals stick around in the City of Vancouver all through their later lives.

But back to the leavers – where do they go when they leave? Mostly to the suburbs. Maple Ridge is the City of Vancouver’s mirror image in this regard. People in their thirties and beyond account for most of this suburban municipality’s growth. By contrast, young adults, especially of university age, but extending into the twenties, flee Maple Ridge. Where are they going? (see above).

What about Surrey? It’s still a suburb, but also increasingly a centre of action in its own right within a multi-polar metropolis. At the moment it’s hit a sort of demographic sweet spot where it’s gaining people at all ages. Nevertheless, it’s worth noting that while young adults aren’t exactly fleeing Surrey, their contribution to its growth isn’t as strong as for older adults or their children, and it remains nowhere near as strong as what we see in the City of Vancouver.

On the whole, these net migration patterns are not too surprising for a relatively large metropolitan area. Young people tend to leave home and move toward the vibrant city centre. Later they tend to move back to the suburbs as they settle down and start families of their own. If anything, what’s striking here is just how many young people remain in the City of Vancouver as they age, living on their own or in diverse families across a wide array of the different housing options the City is working to provide – if still, typically, at too great an expense!

 

 

*- my mortality modeling from my earlier post was really crude – simply applying five years of the expected death rate to the starting (2011) population. Bad demographer, bad! Now I’m using BC Deaths data to apply a survival rate and age the population from 2011 year by year, for each of the past five years, allowing one-fifth of the population in any given age group to age to move to the next mortality risk with each year and then applying the survival rates to the surviving population in sequence. This still doesn’t account for the mortality of recent migrants (in other words, recent arrivals could die and never be counted by the census, and I don’t take into account their mortality in any separate fashion – if I did it would boost the net migration estimates, especially for older adults). I’m also twiddling a bit with my estimates for 0-4 year olds and 85+ year olds, as needed by modeling (infant mortality is much higher than any year afterward until quite late in life, and after 85 I’m dividing the population into about half experiencing 85-89 vs. 90+ mortality). But I think I’ve got most of the technical details now closer to realistic for estimation purposes. As noted previously, none of this really matters much for younger population groups.

 

Update: The Lifeblood of Vancouver still isn’t leaving!

New Census results out today from the 2016 Census! They include dwelling type, age, and sex figures. The former is of great interest to me, but I’m going to concentrate on the latter just to update my older posts on migration patterns for Metro Vancouver.

Behold, the lifeblood of Vancouver still isn’t leaving!

NetMigration-2016update

I followed the same basic procedure here as I described in previous posts, comparing 5-year age groups across 5-year census periods. For example, given how many 20-24 year olds we had in Vancouver in 2011, how many 25-29 year olds would be expect to be here in 2016? Without any net migration, we’d expect roughly the same number, subtracting a few who died. So if we compare population figures, and make minor adjustments for mortality (I used 2013 figures, drop me a line for details [UPDATE: I think I’ve made better technical assumptions about late life mortality effects in this later post, reducing net migration estimates from age 70+]), then we can estimate net migration by how many more (or less) people show up in 2016 than we’d expect. I use the intervening age intervals (e.g. 23-27 year olds) as labels to demonstrate where most of the in-out movement is taking place between census years, which I find really captures, for instance, those university years (18-22) well.

The big takeaway, given the frequent concerns expressed over millennials leaving Vancouver,* is that it’s STILL NOT HAPPENING! Young people continue to pour into the region (University town, vibrant urban scene, etc.), and they tend to stay well into their forties.

What does appear to be new this year, at least according to my calculations (which are heavily dependent upon mortality assumptions as the population gets older), is that we’re starting to see a net loss of our late-career / early retirees. These are folks in their fifties and sixties. Yes, yes, the slow leak of our Baby Boomers is upon us! Apocalypse Now! (to be fair, it is their movie…) It’s quite possible these are predominantly people cashing out on their investments in the local real estate market and leaving for elsewhere. But if so, that’s about the only age-specific migration trend I’m seeing that seems driven by Vancouver’s widely unaffordable real estate.

*- I’ve still not seen any calculations or corrections on this issue from Bloomberg. Show your work! Tell us where your bad data is coming from! It’s ok to get stuff wrong, but not ok to keep false stories running!

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:

class-by-edu

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?

incquart-by-edu

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:

renting-by-iq-edu

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?

Unemp-by-iq-edu

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.”

disat-by-iq-edu

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?

obama-by-iq-edu

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?

trump-by-iq-edu

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:

stata-readout

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