What we talk about when we talk about a “housing crisis”

What do we talk about when we talk about a housing crisis? People doing without any kind of housing? People living in inadequate housing? Crowding together? Spending too much on rent? People struggling to get into home ownership? People not being able to afford ownership of the single-family detached house they always dreamed about?

To me, a “crisis” suggests fundamental needs unmet. But just what’s a “need”? How should this be separated from a “want” or “dream,” if at all? Addressing these questions and trying to figure out how they matter was the subject of my keynote talk at the PartnerLife conference last week in beautiful Cologne, Germany.

I illustrated my talk with my favorite case study: Vancouver. In the process, I ran some numbers to compare how Vancouver is doing relative to other metropolitan areas if we address some of the different things we mean when we talk about a housing crisis. In particular, I was interested less in the kinds of “dream” measures used by organizations like Demographia (oh, it’s painful to even link to them!), and more in the fundamental measures of need (note: I’ve compared rents elsewhere, though I need to update the comparison!).

How is Vancouver, long considered the most unaffordable housing market in North America using Demographia’s single-family detached house measure, doing when we look at homelessness? How about when we look at providing basic standards, avoiding crowding, and insuring affordability?

To answer the first question, we can look at homeless counts. I’ll work on building this measure further, but for now I’ll just compare Vancouver with our near neighbours to the south (Seattle and Portland) and to the east (Calgary). Is homelessness a major crisis here?

The first answer to this question is indisputably: YES. Homeless is a major crisis wherever it occurs, with large effects, for instance, on the risk of dying. But a more nuanced answer, of more use in thinking through solutions and sorting out what’s working, is to consider the relative size of the homelessness crisis. Though it’s far from a definitive comparison, I started looking into this question by comparing homeless count data by relevant population size, across the regions of Vancouver, Seattle, Portland, and Calgary.* Here’s what I get:

HomelessCountComparison-2

Is homelessness in Vancouver a crisis? Yes. But when compared to other nearby metro areas, Vancouver looks like it’s doing better. This is important in terms of judging the response so far and thinking through how to continue dealing with this crisis.

Let’s address the second question: How are we doing in terms of insuring people are living in adequate housing, not feeling too crowded, and not spending too much money on rent? In Canada, we have a nice comparative measure of “core housing need” that gets at these components of housing crisis. Importantly, these aspects of a “housing crisis” remain detachable, revealing, for instance, different sorts of crises between the North of Canada (where the issue at hand tends to be crowding) and the South (where it tends to be affordability). Overall core housing need is worst in the North and on reserves, where we can talk about some serious housing crises. But here let’s just look at how Vancouver is doing by comparison with other metro areas in Canada given the most recent data available.**

CoreHousingNeedComparison

How’s Vancouver doing by core housing needs? Not so great. We’ve got a lot of people feeling the pain of unmet housing need, as defined by Canadian standards. Mostly these are people spending more then 30% of their income on rent. I’ll be the first to suggest that this is a funny standard, but it still indicates a real problem, especially for those at the bottom of the income distribution. At the same time, by comparison Vancouver is not actually the worst Canadian metropolis. The worst is tiny Peterborough, Ontario! What’s going on there? I’ve no idea, though now I’m quite curious (and it might just be the small sample size of the income survey). After Peterborough, Toronto is also worse than Vancouver.

While both homeless counts and core housing needs remain open to critique in terms of their conceptualization and measurement, they’re also the best measures of need we’ve got. As such, I’d argue they remain the best measures of when we’re seeing a real housing crisis. Using these measures, we can see that there are indeed housing crises at play in Vancouver. At the same time, in comparative context we can recognize that Vancouver’s doing much better at addressing these real crises than it’s typically been given credit for.

Why doesn’t it get credit for what it’s doing right? I think the unaffordability of the single-family detached house in Vancouver sucks up a lot of attention. I’ll continue to argue that this is a very BAD measure of a housing crisis. After all, if we want to reduce the size of our ecological footprints, if we want to support our great cities, if we want to combat isolation and obesity, and arguably if we want to sustain our democracies, then we want to discourage everyone from living in single-family houses. This means not everyone can get what they want. But it’s not a housing crisis if everyone still gets what they need. And by that measure, Vancouver’s doing better than most people think, even if it’s still got a lot of work to do.

 

 

*- It’s worth noting that the administrative basis for count data here varies between metro region (Vancouver), county (Portland and Seattle), and city (Calgary). I’ve used the relevant administrative data from the 2010/2011 census year as the denominator in each case. This means population has been kept constant for comparison purposes, while the homeless population has been allowed to grow, resulting in a slight underestimate of homelessness per 10,000 people in early years and overestimate in later years. Also of note, King County and Multnomah County are smaller than the metro areas of Seattle and Portland (accordingly). The City of Calgary is relatively co-terminous with its metro area. This could bias overall estimates of relative counts for metro areas. But even if we were to just use central cities (where the populations of Vancouver, Seattle, and Portland are quite similar at @ 600,000) or metro areas, the overall results would still be about the same – there are a lot more homeless people showing up in other nearby cities and metro areas relative to Vancouver. A caution also remains in the possibility for different definitions and methods in each region, particularly with respect to the meaning and coverage of “transitional housing.” Also of note: the big drop observed in Portland between 2011 and 2015 might be worth following up on!

**- the data here come from the CMHC, and are based on Canada’s income survey rather than census data. It’s possible the sample size of the surveys explains some of the variation, and rankings here should be considered preliminary until we get something more definitive, like the 2016 Census data! In past census years, Vancouver and Toronto usually compete in the metropolitan title for greatest proportion in core housing need, and Peterborough tends to be more middle-of-the-road.

 

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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.

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.

 

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

 

Cascadia!

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)

AtHomeLooksLike-Image

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.