Henderson’s Guide to Pandemic History

What will happen when the Pandemic ends?

Will pre-Pandemic patterns, like people moving to Vancouver, go back to normal? Or will small towns, far-flung suburbs, and rural areas see a boost at the expense of cities, reflecting perhaps a new aversion to density and/or embrace of the rise in telecommuting acceptability? (we’ve seen such speculation in certain corners of City Hall).

Or indeed, might we see the opposite? Will people flock to cities like Vancouver as we return to mobility (including newly amped up immigration along with outreach to Hong Kong) and enjoyment of all the urban pleasures we’ve given up during the pandemic?

It’s all speculation at this point. But it’s got me curious about the past. What happened after the 1918-1919 influenza pandemic? And here I struggle with two things: 1) there was a LOT going on during and prior to the 1918-1919 flu pandemic, making it hard to isolate any response, and 2) the census data skips right around the two key years, with timing gaps too large for zooming in.

I can’t fully fix the overlapping events (WWI, and prior to that a big speculative economic crash), but I can kind of get around some of the data limitations of the Census by playing with some historical data sources I’ve been meaning to give more attention, in particular, the brilliant collection of BC City Directories archived by the VPL, including especially Henderson’s City and Greater Vancouver Directories and Wrigley’s BC Directories.

First, a couple of quick notes about the 1918-1919 Pandemic, brought to you by Margaret Andrews (1977) enlightening research in “Epidemic and Public Health: Influenza in Vancouver, 1918-1919” open access in BC Studies vol. 34. According to Andrews, the Pandemic hit Vancouver especially hard relative to other cities in Canada and the USA. It was also very different from today’s Pandemic in targeting mostly young and middle-aged adults.

At the same time, it was similar to today’s Pandemic in arriving across multiple waves, though the first (in 1918) took the greatest toll.

So what can we add by looking at City Guides? Well, we can compare them to Census results to get a more fine-grained sense of how the City responded to and potentially bounced back from the Pandemic of 1918-1919. The guides include, especially, the Henderson’s City of Vancouver Directories and related Wrigley’s Guides (which swallowed up Henderson’s in 1924), all providing listings of businesses (and households) across Greater Vancouver. I estimate the number of listings for each year, folding businesses and households together. While this isn’t a perfect match for population, or even households, it provides a relatively consistent method for a fine-grained look at how Greater Vancouver businesses and households together experienced the concentrated events piling up between census years (more details below!)

What’s our fine-grained examination of directory listings in combination with census data tell us? It appears we really do miss a lot with census data alone, especially between 1911 and 1921, where we saw a gigantic speculative bubble crash in 1913, followed by the Dominion’s entrance into WWI in 1914, and the Influenza Pandemic itself in 1918.

Where Census data from 1901, 1911, 1921, and 1931 make Vancouver’s growth look relatively steady and nearly linear, directory data demonstrate the enormous upset and losses of 1913-1915 in Vancouver, followed by a bottoming out and start at recovery during WWI (when many otherwise unemployed men went to fight in the war), finally interrupted by effective stasis during the Pandemic of 1918-1919. Then boom! Vancouver was off to the races again, climbing rapidly in listings from 1919-1923 and again (jumping different guides & methods) from 1924 seemingly only slowing a bit in 1926. From there, the trajectory of growth seemingly carried right through the beginnings of the Great Depression to 1931, when the next census was carried out.

Is past prelude? If so, Vancouver looks set to recover quite spectacularly from the Pandemic once it ends, as people flock back to the joys of the city. Maybe we’ll get our own Roaring 2020s!

But of course, for now we’re still here in the middle of the damn thing. So I’m still singing “Come On Vaccine.”

You know the tune…

APPENDIX

A couple quick methods notes for my beloved nerds. Historical census data was taken from Norbert MacDonald’s “Population Growth and Change in Seattle and Vancouver, 1880-1960” from Pacific Historical Review 39(3): 297-321 (unfortunately paywalled). MacDonald combines South Vancouver and Point Grey into the City of Vancouver boundaries for 1921, but I believe he considers the populations of these municipalities effectively too low to matter in earlier years. Henderson’s Directories were released on a yearly basis with a pretty standard, two column format, from 1905-1923, and seemingly covered all of Greater Vancouver during this time, with listings showing up in North Vancouver, New Westminster, and Burnaby, for instance (though North Vancouver was sometimes also reported separately). Ads were placed somewhat randomly within the text, rather than as full pages. In 1924, the Henderson directories were absorbed by Wrigley’s directories, using a new three column format (and smaller type) with interspersed full page ads. I attempted to estimate the listings for each year of these two different sources by gathering page numbers for alphabetized listings (of resident households and businesses) and multiplying by an estimate of the number of listings per page, excluding full page ads where possible. I estimated ~95 listings per page for Henderson’s and ~184 listings per page for Wrigley’s, based upon a quick count on what seemed representative pages (the second A listings), but this estimate could certainly use further checking.

Homeless Counts and Migration Patterns in Metro Vancouver, Calgary, and Winnipeg

People move. That includes people who end up getting counted as homeless. How should we interpret what homeless counts tell us about these people?

To an important extent, this question brings us back to fundamental interpretations of who gets counted. Is being counted as “homeless” interpreted as a social problem: the lack of enough accessible housing? Or is it being interpreted as a person problem: identifying the “homeless” as fundamentally different from housed people?

I’m a sociologist and a housing scholar, and I think homeless counts can be really useful indicators of the social problem of housing inaccessibility. We’ve got some great solutions to this problem, which basically come down to making more housing more accessible to more people. The alternative approach, interpreting homeless counts as identifying problem people, is… really problematic. The solutions it points toward tend to involve “fixing” people (at best?) or keeping them out entirely.

We can see an example of this problematic approach at work in a recent article, entitled: “Vancouver is Canada’s dumping ground for the homeless, and this needs to stop.” The language is offensive, immediately identifying those counted as homeless as more like trash than people, and pointing toward the need to keep them out. Sure enough, the gist of the piece is that Vancouver’s homelessness problem is being driven by problem people coming here for our mild weather in combination with the concentration of supports and services here and the lack of them elsewhere. This mixes a potentially good message (we need more housing and services and supports everywhere) with a bad message (so stop providing them here) as well as the aforementioned dehumanization.

From here on out, I’m going to set aside these portions of the argument and turn my attention toward a few of the empirical claims. Correspondingly, I’m also going to focus at the metropolitan level in terms of thinking about migration and homelessness, meaning I’m setting aside how people counted as homeless, as well as supports and services, are distributed within metro areas (my position, again, is that we need more housing, supports, and services, and every neighbourhood should have them). For the rest of this piece, I’m mostly going to return to my starting question: how should we interpret what homeless counts tell us about people who move? And I’m mostly going to do it by comparing patterns of migration as they show up in homeless counts in Metro Vancouver to Calgary and Winnipeg.

First let’s start with a few relevant claims from the “dumping ground” piece that are easy to knock down. Do people counted as homeless in BC disproportionately congregate in Metro Vancouver? That’s an easy one, and the answer is: no. As I showed awhile back with a post drawing upon coordinated provincial counts, on a per capita basis, Metro Vancouver has fewer people showing up in homeless counts than most other metro and non-metro locations across BC. Why use a per capita basis? Because people counted as homeless are people. And knowing what proportion of people get counted as homeless tells us something important about where we see problems with the accessibility of housing. These problems are widespread across BC rather than concentrated in Metro Vancouver.

What about more broadly? Is Metro Vancouver Canada’s “epicentre of homelessness”? Is it due to our mild weather as claimed in the piece above? Let’s look outside BC, comparing Vancouver to Calgary and Winnipeg (where no one’s claiming mild weather). If Vancouver was really the epicentre of Canada’s homelessness crisis, you’d think we would jump out when we control for the size of the surrounding population. But quite the opposite happens. Both Calgary and Winnipeg have more people showing up in homeless counts per 10,000 residents than in Vancouver.

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So maybe Vancouver’s not the epicentre of where people are becoming homeless, but instead the place where people are disproportionately moving after they become homeless elsewhere? Except, when we look at the proportion of people counted as homeless who migrated to each city within the last year, it’s actually much higher in Calgary, and only a little lower in Winnipeg. Suddenly the idea that all Canada’s homeless people are moving to Vancouver because of the weather looks pretty… well… ludicrous.

It’s worth noting that Winnipeg was actually featured as the origin for a homeless man in Vancouver in the image accompanying the “dumping ground” piece. So we should definitely take a look at how Winnipeg’s Street Census makes available the origins of its interprovincial migrants who show up as homeless. Guess what: 23% of them came from BC!

Is Vancouver dumping its homeless on Winnipeg? That’s probably just as bad a take as the converse. A better take is that people move. And not just to Vancouver. And that people counted as homeless are first and foremost people.

But do people who show up in homeless counts move for different reasons than other people? We don’t actually have that data for Vancouver or Winnipeg. But Calgary has it! So just for comparison purposes, let’s set reason for move to Calgary in the past year for those who show up in Calgary’s Homeless Count alongside reason for move for a more general selection of the population. In this case, the most similar question and options on reason for move actually come from the USA’s Current Population Survey (Mobility Table 17), so we’ll plot the two together. (If you want to see more on reason for move data and comparability, have I got the post for you!)

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The options are worded differently in places, but I’ve attempted to harmonize them as possible, and the correspondence is pretty clear. Main reasons for move fit into the same four broad categories (work & opportunity, family, housing, other) for those who end up homeless in Calgary as for all movers in the USA, and in roughly the same proportions. Where responses differ, they tend to indicate that migrants who end up counted as homeless are taking slightly bigger risks than migrants overall. For instance, fewer people who ended up homeless in Calgary moved with a job already secured, compared to those who moved looking for work. But overall, the patterns suggest that people who move and then show up in homeless counts seem to move for pretty much the same reasons as everybody else.

People move. And moving is actually kind of risky.

Mostly moving works out pretty well, and people find work and a place to live. But sometimes it doesn’t work out. So some people move on again or return to where they came from. Others, for various reasons, find themselves homeless. Are recent movers more likely to find themselves homeless than long-time residents? Let’s compare homeless count data to general mobility data to find out.

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And there it is. Even though most people who show up in homeless counts are long-time residents, being a recent mover to a region is much, much riskier. For both intraprovincial and interprovincial migrants, moving to a new place is a brave thing. This makes intuitive sense. Recent movers have to find housing without the benefit of already having any. They join a much smaller pool of local residents displaced from their housing in the search for a new place to live without the benefit of an old place to hold onto. So overall, recent movers are much more likely to find themselves out of luck in the search for housing than long-time residents. This seems to be exactly what we see for both intraprovincial and interprovincial migrants. Why doesn’t the same pattern fit for international migrants? Several studies have aimed to answer this question, and the short answer is: because international migrants are both selected and supported differently. As a result, they’re much closer to long-term residents in terms of their reduced risk of becoming counted as homeless, even though the risk is still there.

Seeing as how they’re at greater risk for being counted as homeless, we should probably be doing more to support recent movers to our cities. ALL of our cities. How? By making more housing more accessible for them.

The resistance to making more housing more accessible sometimes comes from the xenophobic notion that housing should only go to local residents. That movers should be somebody else’s problem. There are many who’d prefer to erect walls around our cities, keeping new folks out. Other times it comes from the idea that anyone who can’t find housing must be defective, which is right where we started. And maybe it even comes from the notion that our mild weather means people don’t need housing quite as badly as elsewhere in Vancouver.

We can probably make the case for that last point by looking at how many people are left unsheltered here in Vancouver compared to Calgary and Winnipeg. Vancouver has fewer people counted as homeless per capita compared to Winnipeg and Calgary, but many more people left unsheltered. Our mild weather doesn’t seem to be drawing people here in any disproportionate fashion, but it might be enabling a callous disregard for housing needs.

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On a final note, the high proportion of those without shelter among the people counted as homeless in Vancouver might also account for the recent reactionary stance taken by many local politicians and activists. The visibility of those left without shelter makes homelessness seem a bigger problem here than elsewhere. Interpreted correctly, the statistics tell us something else. It’s not a bigger problem here. And the problem is not a floating problem population that ends up in Vancouver. The biggest problem we have is a local lack of generosity leaving less shelter space and less housing available for those who need it in Vancouver. We can fix that. And we should.

  • Methodological note: While the Metro Vancouver count covers the entire metro area, the coverage of the Calgary and Winnipeg counts may be more constrained to the central cities of each metro area. This may result in a slight conservative bias, undercounting those who would show up in a homeless count in Calgary and Winnipeg covering the entire metro areas involved. At the same time, Calgary and Winnipeg dominate the populations of their metropolitan areas in a way which Vancouver, as a central city, does not. So I use metro populations as denominators in all cases in assessing the relative prevalence of homelessness in those cities relative to general populations and migration streams. I obtain comparative statistics on metro areas via StatsCan Tables 17-10-0136-01 ; 17-10-0135-01 ; 17-10-0141-01 for homeless count reference years, or, in the case of estimating migration-based risks, for the periods leading up to reference years. I use the data to estimate populations of non-migrants (stayers & local movers), intraprovincial, interprovincial, and international migrants for each metro to use as baselines for establishing risk of showing up in homeless counts. All data and calculations are available in this spreadsheet. Please send any corrections or questions my way!

Keeping the Leavers

co-authored by Jens von Bergmann and cross-posted at mountainmath

Do people select cities from diverse alternatives? Or do cities select residents from diverse flows of people?

The answer is pretty much: both.

People can look around and consider where they want to end up. And cities, through municipal policies, can and do work to select their residents. EXCEPT cities can’t do this directly. At least across North America, cities generally aren’t allowed to establish and maintain their own immigration policies. When they try to do so, the courts shoot them down, because both Canada and the USA enshrine the right of people to move within their borders. Cities can’t stop them. But cities have a big role in deciding how much room to make for people. And they also generally get to decide what form any added room should take. Many, for instance, only allow the most expensive forms of new housing, like single-family detached on large lots, selecting for wealthier residents. So that’s how cities select their residents.

The fact that it’s a two-way selection process, with both people and cities doing the selecting, makes it quite difficult to forecast something like future housing needed to prepare for a city’s population growth. Yet this is what cities, including Vancouver, are often tasked with doing by way of justifying their policies.

One way of going about this is to argue that past population growth is our best estimate to forecast future housing demand. This is a bad argument on many levels as we have explained at length before. In expensive gateway cities, like Vancouver, this often gets accompanied by nativist notions that population growth is driven almost entirely by international migration as net domestic migration is small. But net estimates obscure the actual size of flows, where local and domestic movers predominate and make up the majority of those occupying new housing.

More troubling is the implicit logic that elevates domestic in-migrants over international in-migrants, providing only the former a legitimate claim to the place freed up by a domestic out-migrant. So far, freedom for movement in Canada extends to immigrants, as it should. And not all immigrants come from outside of Canada. Increasingly non-permanent residents turn into immigrants (including both of us!) This simply results in a drop in net non-permanent residents and an increase in immigrants in these stats, without anyone actually moving. This speaks to the complexity of how cities select their residents from diverse flows of people. A thought experiment might be helpful to better illuminate how it works in practice.

Creating room for people to stay

First let’s look at past population growth. BC Stats splits this up neatly into several sub-categories, which we can think of as flows.

metro-van-migration-1

Net population growth for Metro Vancouver has hovered around 28k people a year. But it’s not like this is a one-way flow, about 50k people leave Metro Vancouver every year and somewhere around 75k people come. Some people have a really hard time making room for newcomers. But maybe people are more sympathetic to people leaving. Of course many people leave Metro Vancouver for greener pastures, a better job, move for university or other personal reasons. But the “Leaving Vancouver” letters (practically a genre at this point) are testament that not all people moving away think of their moves in positive terms. Many feel squeezed out. People keep talking about friends that left because they could not find adequate housing in Vancouver.

So let’s say, for the sake of argument, that one out of five people moving out of Metro Vancouver to elsewhere in Canada really wanted to stay but could not make it work. And, of course, we already know that feeling “forced” to move is strikingly common in Vancouver, even for those who remain. So let’s say we are sympathetic to the people who leave town and would actually like to insure enough room for them to stay. What would that take?

That’s easy to check, all we need to do is reduce the size of the inter- and intra-provincial out-migrant buckets in the above graph by 20%.

keeping-the-leavers-1

The net effect is that fewer people would have been leaving Metro Vancouver, while the same people came. And our population growth went up by about 30%. Which means that we should have built 30% more housing than we did over the years to make that possible.

Now some readers will argue that that’s not how things work. If we had built 30% more housing, that does not mean that one in five of the people that moved would have gotten to stay. Some of that housing would have been taken up by people that wanted to move to Metro Vancouver but could not find adequate housing, but with more housing they could have made it work and would have out-bid some of those that were hoping to stay.

And with more housing available, some new households might be created that might otherwise not exist. Maybe someone will move out of their parents place earlier and take up one of those new units without adding to population growth at all. And in return one of the 1 in 5 people that had hoped to stay might still end up feeling forced to leave again.

And people arguing that are of course exactly right. That’s the point of this exercise, housing and population growth are endogenous. Which is kind of a fancy way of saying that people select cities from diverse alternatives AND that cities select residents from diverse flows of people.

Empty homes – the ultimate anti-housing red herring

Here in Vancouver, those resisting making room for more people to stay and arrive like to point toward a supposed mismatch of housing growth to household growth between 2001 and 2016, supposedly leaving lots of empty homes. This time window is of course chosen deliberately to include the change in census methods 2001-2006, and this talking point mostly goes away when properly accounting for that. To avoid adding homes people will still point to some vague notion of dwellings being left empty, even though we have better data on empty homes than ever before and there are very few problematic cases paying the Empty Homes Tax or Speculation and Vacancy Tax left in the region.

How should we do population projections?

So given the endogeneity issues: how should we be doing future population projections? In high demand areas like Metro Vancouver we should start from housing growth. That’s what cities can control. How many condos will be built? How many rental homes? How many non-market homes? How many infill homes? And given a scenario of housing growth, we can model what population growth might look like. How many people would move here from elsewhere in BC? How many from elsewhere in Canada? How many from outside the country? How many people would move away? It’s not an exact science, but demographers can build decent models once we know how much housing is being built and how cities are trying to select their residents. And the public can look at different scenarios of housing growth and the resulting scenarios of population change and use that to have a more informed discussion about where they want the city to go as well as who they want to enable to stay.

As usual, the code for this post is available on GitHub for anyone to reproduce or adapt for their own purposes.

Why People Move in Canada & the USA: Comparing CHS, AHS, & CPS results

Why do people move? I’ve taken up this question in a series of recent posts (some co-authored), and though the available data to address the question remains sparse, it’s getting richer all the time. Today I want to compare three different sources of information, highlighting how much it matters just how we ask people about their reasons for moving.

The Canadian Housing Survey (CHS) is the newest source of information on reason for move. Its format borrows heavily from the American Housing Survey (AHS). But the Current Population Survey (CPS) also provides information on reason for move in the USA. Each survey asks about reason for move in slightly different ways.

In the USA, the CPS and the AHS ask about reason for move in different ways that might at first seem subtle, but have a big impact on results. The CPS tracks individuals, and asks where they lived one year ago. If they lived somewhere different from their current residence, they’re asked “what was your main reason for moving to this house?” This directs them to choose only one reason as their main reason, with options to specify reasons not on the list. The AHS, but contrast, tracks households, and asks only the reference person for the household if they moved in the last two years.  If so, they’re directed to a “recent movers” section, providing a little preamble and asking them repeated yes or no questions about their move, each of which might constitute one of multiple reasons to characterize their last move.

Reason4Move-A

There are a few major differences in these questions which I’ll detail in a moment, but one is worth talking about insofar as it’s especially subtle given its possible impact. Researchers often think of two separable but related processes as involved in moving. There are the “push” reasons you might leave a home and the “pull” reasons that might draw you to a new one. Reading the different questions carefully, the CPS clearly cues for “pull” reasons in specifying “reason for moving to this house.” The implicit comparison is “as compared to some other house” you might’ve moved to, rather than “why did you leave your old house.” The AHS more neutrally refers to moves overall, letting respondents sort through push or pull factors relevant to each option. I’ll come back to why this might be importantly in a moment. First let’s jump over to the Canadian Housing Survey question, which asks the responding member of each household about their previous residence and the move to their current residence, no matter how long ago it occurred.

Reason4Move-B

The set up is then quite similar to the AHS, except the CHS appears to provide all of the options at once instead of one at a time (people can still choose more than one). There is significant overlap (one might say “copying”) in the language of each option, though the CHS also provides a few extra options unavailable in the AHS, concerning moves for school, personal health, and to become a homeowner (all closely related to options available in the CPS).

Let’s quickly summarize major points of difference:

  1. individual (CPS) v. household (AHS, CHS)
  2. one-year (CPS) v. last move within two years (AHS) v. last move (CHS)
  3. different option lists (CPS, AHS, CHS)
  4. choose only “main” option (CPS) v. all relevant explanations (AHS, CHS)
  5. cued for place moving to (CPS) v. cued for many reasons for moving (AHS, CHS)

All of these differences create real problems for comparing results, but its also clear that the CHS and AHS are closest (rather than the CHS and CPS, which I’ve compared before). So let’s compare CHS (StatCan 46-10-0036-01) and AHS (Interactive Table) first. Here I’ll compare countries overall and also the four biggest metro areas within each country to get at some of the variation.

Reason4Move-C

The Canadian data is themed in the “cool” colors of blue, purple, and green, while the USA is in “hot” shades of red, orange, and yellow (“hot zone” references entirely unintentional, but perhaps apt). Here we see only the categories where AHS and CHS options map – almost identically – onto one another. For many options, the percentage of movers indicating the option at least partially explains their last move matches pretty closely. In particular, the “forced move;” “new job;” “change in household size;” and maybe “upgrade to bigger dwelling” all look like the AHS and CHS could plausibly be drawing upon the same distributions. But there are some big differences with the other options, with Americans reporting greater likelihood a move relates to “form own household;” “be closer to family;” “reduce commuting time;” “reduce housing cost;” and move to a “more desirable neighbourhood.” Are these real differences between countries or artifacts of the different surveys themselves?

Let’s zoom in on a few areas and add in the CPS comparison (here accessed via IPUMS for contemporary metro data) to provide more information. First up: forced moves!

Reason4Move-D

I’ve written about “forced moves” before, with special attention to those relating to landlords, banks and other financial institutions, and government actions in Canada and evictions and foreclosures in the USA. I puzzled over the differences between Canadian (CHS) and American (CPS) data. But looking across all surveys, we can see that the CHS and AHS data actually look very similar. It’s the CPS that seems to report an unusually low percentage of evictions and foreclosures rather than forced moves. So what’s happening? If one were reporting only the main “reason for move,” it would seem like being forced out of one’s previous residence would rise to the top, so it’s probably not just a matter of choosing a single “main” reason vs. multiple reasons. BUT let’s remember that the CPS also conditions peoples’ choices toward “pull” factors relating to the “main reason for moving to this house.” So CPS respondents are likely drawn toward considering why they ended up in their current residence, as opposed to other possible places they could’ve moved, rather than reporting on why they left their old place. Like I said, it’s a subtle difference in question wording, but here it probably has a big impact.

Returning to the AHS and CHS comparison, it looks like forced moves have been a little bit more common in the USA than in Canada, which matches with my rough expectations given differences in tenant protections, mortgage finance regimes, and economic turmoil. (If anything, I suspect these differences may become more stark, with more Americans experiencing forced moves as pandemic restrictions loosen). There remains big variation within each country, with Metro Vancouver topping forced moves in Canada and Chicago topping forced moves (and exceeding Metro Vancouver’s rate) in the USA. Of note, the CPS data is probably less reliable at distinguishing between Metros, but it’s notable that Chicago still stands out.

Let’s try moving for work!

Reason4Move-E

We can consider two different work-related options explaining moves: moving for a new job and moving to reduce commuting time. Interestingly, new jobs or job transfers account for more moves than reducing commutes in Toronto, Calgary, Vancouver, and Dallas. This is likely related to the high in-migration to these metro areas. Reducing commutes accounts for more moves in generally slower-growing metros (Montreal, NYC, LA, and Chicago). A notably smaller proportion of respondents in the CPS chose job transfer or reducing commute as the MAIN reason for moving to their current house, indicating lots of people considered a job-related move as likely just one of multiple reasons for moving – and possibly less related to why they chose a particular residence from multiple possibilities.

Let’s take a look at a suite of other, more housing-oriented reasons people might choose to move.

Reason4Move-I

“Form own household” as a reason to move is commonly thought of as capturing people like young adults (and/or divorcees) splitting off from existing households to start their own. This is a pretty regular demographic process, so it’s somewhat surprising that it seems to be related to so many more moves in the AHS than the CHS. Is this a Canadian-USA difference? Maybe, maybe not. Here the CHS and the CPS actually look more similar. What’s going on? One likely possibility is related to the fact that the AHS doesn’t have an option for people to choose “to become a homeowner” unlike both the CHS and the CPS. The closest SOUNDING option is “to form own home.” It seems entirely possible that this ambiguity in the meaning of “own home” – whether it means to become a homeowner or to separate from a previous household – explains much of the difference between the AHS results relative to both the CPS and the CHS.

Let’s compare moving for a larger dwelling with moving because of new household members.

Reason4Move-G

Change in household or family size and upgrading to a larger dwelling might be understood as related options. Again, very basic demographic processes – having children, partnering, etc. – often motivates a move to a larger home. Other demographic processes can result in smaller households, of course, but it’s less often people move in direct response. If a change in household size typically operates as a “push” (e.g. “this place is too small for us now”) then moving to a bigger dwelling operates as a “pull” (“this place is just right!”). What’s interesting here is that the CPS is predisposed to capture the “pull” part of this kind of move, and has no option at all for the “push” part. Perhaps as a result, here the CPS seems to “overperform” with “new or better home” as the MAIN reason for move almost reaching the prevalence of “upgrade to a larger of better dwelling” as one of many reasons for a move in the AHS.

Finally, let’s consider neighbourhood desirability and reduced housing costs

Reason4Move-H

Comparing the CHS and the AHS alone would make it appear that neighbourhood desirability is much more important as a reason for move in the USA than in Canada. We could spin all kinds of possible reasons for this (e.g. greater neighbourhood segregation and inequality in the USA). But adding information from the CPS reveals that moving for a better neighbourhood is very seldom the MAIN reason for a move. People mostly don’t move in search of better neighbourhoods, it’s just a kind of side feature. So maybe it doesn’t actually tell us much that Americans mention this feature more often as describing their reason for moving (when presented with it as a “yes/no” option) than Canadians (provided as one of many options). By contrast, the CPS results more closely track both the AHS results (which still run higher) and the CHS results for moving to cheaper housing as a reason for moving.

LONG STORY SHORT: every move is a story in itself. We only partially capture this story with survey questions about why people move, and how we structure those survey questions really matters for the results we get. Compare with caution!

Metro Flows

Sometimes we talk about cities as if they’re settlements, where people become fixed to place. But in fact, if you track movements of people, cities look more like rivers. People churn through the urban landscape. Net migration numbers are really useful in some contexts, but also obscure the full extent of this churning. Fortunately, BC Stats has numbers that attempt to break down actual flows of people through regions. We can break out Metro Vancouver (a.k.a. Greater Vancouver) and see just how many people we think might be flowing through. Here’s a little graphic I made to highlight this churn, while I continue playing around with the best way to present it.

Flows-MetroVan-2

The numbers and categories for inflows and outflows are straight from the BC Stats regional district migration file for Greater Vancouver (which itself is derived from a more detailed version of Stats Can table 17-10-0140-01 on components of population change). Population, birth, and death figures similar come from BC Stats and StatCan files. I’ve rounded them off and expressed them in millions here both for ease of reading and in recognition of some of the underlying uncertainty in accounting for population shifts.

BC Stats figures divide up international flows into immigration, emigration, returning emigrants, net temporary emigrants, and net non-permanent residents. The many categories reflect both legal statuses and movements of people, which is part of why there are so many and starts to get at some of the complexities of international migration regimes. Then we get interprovincial in and out migration (to Metro Van from other provinces) and intraprovincial in and out migration (to Metro Van from elsewhere in BC). I find it super-cool to see all the flows laid out.

The basic takeaway for me is that over the course of thirteen years, from 2006 to 2019, we see enormous churn through Metro Vancouver. From a base population of 2.2 million, an additional 1.1 million arrivals came to the region. A smaller 0.7 million left. Wow! That’s a lot of turnover! The total 1.8 million moves into and out of the region over the thirteen year period nearly approaches the starting size of Metro Vancouver as a whole, and represents a much bigger number than the net migration of 0.4 million. Adding in 0.3 million births and subtracting 0.2 million deaths, and there’s your growth of roughly half a million people in Metro Vancouver through 2019.

What’s even more striking is that the moves into and out of the region are dwarfed by the moves within the region. That’s because, as I’ve previously discussed, local moves are a lot more common than regional ones.

Mobility1

Heck, most moves are within municipal boundaries, and well within metropolitan ones (see previous post for more discussion of this figure).

So the churn we see in metropolitan flows is only a small part of residential churn overall. People move! When we think of cities, we need to recognize this movement as fundamental to how they work. Our “settlements” really aren’t very settled at all.

UPDATE June 10th

For comparison’s sake, let’s update the figure above by adding an estimate of internal moves. These are moves from one location to another within the metro area of Vancouver, and as such they don’t add or subtract from the metro population as a whole. Instead they just highlight the centrality of mobility to urban life.

Migration_Flows_Mobility_Add_2006-19_MetroVan

We don’t have a straightforward estimate of these moves from StatCan data. So here I draw upon Census microdata from CHASS. I hold the internal moves constant by averaging the estimates of how many people recorded a move within the Vancouver CMA across three census years (2006, 2011, and 2016). The estimates vary a bit between years, dipping from 260,590 moves in 2006 to 251,635 in 2011, before rising again to 278,632 in 2016, but I don’t have data for every year and I like the graphic impact of treating it as a constant for comparison with in- and out- flows.

Takeaway: when you add in local moves, the city looks even more like a river. In fact, the total number of moves between 2006 and 2019 adds up to roughly 5.2 million. The population in motion more than doubles the population of the “settlement” at the start (2.2 million) and nearly doubles it at the end (2.7 million) of the period in question. You say settlement, I say river.

 

Projections and Self-Fulfilling Prophecies

jointly authored with Jens von Bergmann at MountainMath

 

When people want to live in your city, how many should you let in? On the one hand, this is a moral question. Do you have an obligation to people who don’t already live here? On the other hand, it’s a moot question. At least in Canada, cities don’t have the power to control migration.

BUT WAIT! Cities DO have power over how many new dwellings to allow. This actually changes our moral question a bit. Cities can’t keep people out, but because they have power over dwellings, municipalities can control how many people get to remain in. As a result, if you don’t allow any new dwellings when people want to live in your city then rich people will generally outbid poor people for the housing that’s left.

It may be the case that municipal politicians are fine with rich folk replacing the poor folk in their cities while their own housing rapidly appreciates in price. Why let any new housing get built? “No thanks, we’re full!” But they can’t always SAY this. Especially in cities full of renters that generally support progressive and inclusive values.

So what to do? Two paths are readily available. One: transform the moral question (“isn’t it terrible that developers make money off building housing?”) Two: turn the moral question into a narrow technocratic one instead. Let’s explore this latter option a bit more, because it’s really interesting and sits well within our wheelhouse (mathematician and demographer).

Here in the City of Vancouver, a new motion was just launched, titled Recalibrating the Vancouver Housing Strategy (RVHS). There are some good initiatives in this motion, but the main thrust and motivation is to turn the moral question of how many people get to remain in Vancouver into the narrow technocratic question of how do we forecast population growth? As any demographer can tell you, this can be tricky, especially when it comes to forecasting for municipalities. But there’s a naive kind of work-around some people use when they don’t follow demographic techniques and concerns very closely and don’t want to think too hard about the question at hand. They simply turn the population forecast into a projection forward from how a city grew in the past.

This is a neat trick! Especially if you’re in a city that’s limited new dwellings in the past and thereby kept its population growth to a minimum and you want to keep it that way. “The evidence suggests we haven’t been growing very fast, so we shouldn’t add much more housing.” With a little bit of hand-waving, the number of dwellings allowed by the city is reimagined as something that can be tailored to meet the forecast rather than the central determinative factor of the forecast.

Is this the kind of thing that could happen in Vancouver? Before we get into the motion, let’s just quickly look at Vancouver’s recent past. We know prices and rents rose rapidly through 2016 (and beyond), which is pretty good evidence that we didn’t add enough housing for the people who wanted to live here all by itself. But how did the City of Vancouver grow relative to the rest of the region? It grew more slowly. (“No thanks! We’re full!”) Did we lose poor people and replace them with rich people as a result? Yap, this is exactly what has happend in the City of Vancouver, which has lost lower and middle income people, and gained high-income people, at a faster pace than the surrounding Metro area.

2005-2015_rel_change-1

 

The Motion

Now let’s get back to that RVHS motion, starting with part A:

THAT Council direct staff to revisit the Housing Vancouver Strategy targets to align with historical and projected population growth based on census data.

This is a vague statement. There are, of course, many ways to “align” something (Dungeons and Dragons fans may be immediately reminded of the nine different alignments readily found therein). There are also many ways to project population growth. These often rely upon multiple sources of data. Birth rates, death rates, age structure, labour market statistics, and net migration rates serve as typical baseline sources of information for demographers, and are usually gathered from all manner of data (e.g. vital statistics, surveys, policy-based immigration projections, etc.) rather than simply historical census data. So how is the author of this particular motion imagining more specific alignments and projections? The answer can probably be found in the WHEREAS sections 4 and 5:

Population growth has been consistent at approximately 1% per annum over the past 20 years according to Statistics Canada census data. Based on this historical trend, a similar growth rate for the coming decade would amount to a population increase of around 66,000. In the City of Vancouver, the average household size is 2.2 individuals per dwelling unit (or “home”);

The target of 72,000 new homes across Vancouver in the next 10 years multiplied by 2.2 would mean a population increase of 158,400 – more than twice the historical rate. A projected historical rate of population growth would imply instead a need for roughly 30,000 new housing units over the coming decade;

We’ve left the refined techniques of demography behind here, as well as the determinative forces of births, deaths, and moves. Indeed, people pretty much disappear and their dwellings get only scare-quotes as homes. But let’s follow the math we do get and try and understand what projecting past trends means in terms of numbers (leaving aside if we agree that things went splendid and we should just keep going the same way). Let’s try and reproduce the estimation of new housing units assuming we hold the 20 year trends in the two mentioned metrics, population and household size, constant.

The 1% annual growth rate roughly checks out, although there have been variations.

cov-vs-metro-pop-growth-1

 

And population in the City has grown consistently at a lower rate than overall Metro Vancouver population. In fact, if the City of Vancouver had grown at the same rate as Metro Vancouver over those 20 years, Vancouver would have had 60,000 more people within city limits in 2016. But maybe people would just rather live farther out in the surrounding suburbs? Again, there are variations, but overall that is not what the price and rent data tell us.

rent-unnamed-chunk-3-1

 

People want to live in Vancouver. But they often settle for living farther out, based on the specifics of what they want and can afford. The competition for the limited number of dwellings in Vancouver drives up prices here relative to surrounding municipalities.

So what to make of the close relationship between population growth and dwelling units added? It’s a real relationship.

dwelling-pop-unnamed-chunk-4-1

 

The motion, as presented, seems to suggest that this close relationship is evidence that we’re projecting population growth really well, thereby allowing almost perfectly enough new housing to meet population needs. Is this what we’re doing? Well, no. In fact, the amount of new housing allowed sets a cap on population growth that can only be exceeded by increasing household size (which in many cases cities have also made illegal)1 or decreasing the number of empty dwellings.

There is broad support for decreasing the number of empty dwellings, and both the City of Vancouver and the Province of British Columbia have put in place taxes on vacant properties and their owners to do just that. Have they succeeded? Quite possibly! But compared to other municipalities, Vancouver’s vacancies (as recorded in the Census) looked relatively normal prior to the new taxes, despite persistent rumours of some mythical oversupply. After the new taxes, administrative data reveals there aren’t many taxable units left vacant at all (~1%).

What about household size? The motion suggests imposing a constant for Vancouver, expecting 2.2 people per household. But household size is not staying constant. It’s falling all across Canada, due to a combination of forces (aging of the population, declining childbearing, changes in partnership, the rise of people living alone). We also know that as people get richer, they tend to occupy more space. And, as pointed out above, Vancouver’s been getting richer.

hh-size-chunk-5-1

 

As we see, household size in the City of Vancouver has continuously declined over the years, a trend that has significant impact on the relationship between housing and population growth. Sticking with the bad assumption that past population growth should be predictive of future housing needs, we can see that we’re still going to need more housing per person than in the past. Projecting these trends forward, lazily anchored at the 2016 census data, gives an increase in population in private households of about 67,000 and a corresponding increase in 41,000 households (aka occupied dwelling units). And that is not yet accounting for the increase in population in non-private households that Vancouver has experienced, like retirement homes or similar institutional housing.

So if the RVHS motion points us toward a bad way to do population projections, then how should one do it? There are lots of models to look at, but given that people want to live in Vancouver, a key ingredient in any model should be how much housing will be allowed. Conditional on allowing a given amount of housing, we can attempt to forecast how many people will come. But this moves us back from narrow technical questions (which we’re more than happy to continue exploring in depth!) toward the central moral question at hand. How many people are we comfortable allowing to live in Vancouver? Because if we allow more housing, more people will come. And if we allow more housing, we’ll also allow more of those currently at risk of feeling unwanted in Vancouver to stay.

That begs the question: What would be the problem with allowing more housing? The last WHEREAS of the RVHS motion holds an answer to that.

A revised and more accurate understanding of demographic needs and demand will assist in properly planning for the post COVID-19 reality. Setting excessively high targets will pressure the City of Vancouver to grant significant amounts of density at a low price, in an attempt to induce housing construction approaching the HVS targets. This will cost the City of Vancouver potential revenue, and will mean that the City abandons its commitment to having growth pay for itself.

In short, housing might get cheaper. Which incidentally is quite in line the goals of the Vancouver Housing Strategy.

But there are a couple things here that need a bit more unpacking. First, from the title throughout the motion and showing up here again are mentions of planning for a “post COVID-19 reality.” To put it bluntly, this is odd. These parts of the motion caution us against assuming what comes next will reflect what came before. But, as discussed above, this is exactly the assumption the rest of the motion says we should make, resting as it does upon a very selective reading of Vancouver’s recent population growth. Weird contradiction. But then again, pretty much the same language has been employed way before COVID-19 was on anyone’s radar, suggesting that COVID-19 has just been tacked on for extra effect.

Second, the notion that “growth pay for itself” sounds quite reasonable, but it’s not clear what that means in practice. In Vancouver, new housing projects pay a variety of municipal fees, DCLs, CACs and additional engineering fees upfront, and annual property taxes thereafter. How much of the overall cost of living in the city should be charged upfront, and how much should be charged over the lifetime of the housing as property taxes? That’s a political question that Vancouver should have a discussion on.

Charging high entry fees keeps prices high, not just of new housing but of all housing. It encourages treating housing as an investment, with low holding costs (property taxes) and high barriers to increasing housing even as population pressures keep prices and rents rising.

Charging a lower entry tax and collecting a higher portion as property taxes later can lower the entry point to housing and spreads the costs out over the lifetime of the dwelling unit. This treats housing as a place to live, lowering the barriers to new housing construction and asking people to pay for city services and amenities over their time living in the city.

The (sort of) good parts of the motion

Let’s end with a few bright notes. There are some good parts to the motion! We like data and Part B asks:

THAT Council direct staff to provide annual historical data since 2000 on the number of units approved through rezoning, the breakdown of housing types that have been approved, housing starts and net housing completions, and estimated zoned capacity for the City of Vancouver.

This part of the motion is asking for better data, but it needs refinement. As it is right now it is hard to see what it will accomplish.

Number of units approved through rezoning is hard to interpret unless it is accompanied by more detail on how many of these units actually got built. Take the approved first version of the Oakridge development for example. A massive number of units got approved, yet the project died when drilling found an aquifer that precluded the project from going forward as approved. Several years later, a different proposal got approved, for the data on approvals to be useful we need to know what happened to those units.

Monthly data on housing starts is already easily available, asking the data be reproduced adds zero value and amounts to a waste of staff time.

Net housing completions is an important number, but very hard to do in Vancouver, given our high reliance on informal housing. It is still worthwhile to try and approximate this, but the motion should be clearer what part staff should focus on beyond the data on completions, demolitions and secondary suite estimates that we already have.

Estimates of zoned capacity is a great stat to get clarity on. Some vague estimate has been making the rounds for a while after surfacing in a consultant report, with next to no detail how it was derived. Having an estimate with a clear methodology would be a great addition to inform Vancouver housing policies.

Part B is a good and simple ask:

THAT Council direct staff to clarify whether the Vancouver Housing Strategy targets refer to net housing completions or gross housing completions.

Part E is mostly redundant:

THAT Council direct staff to provide detailed inventory data through the Open Data Portal4 of housing starts, development projects anticipated in the pipeline (including form and type of units), and existing zoned capacity (disaggregated by local area) to inform this work.

The open data portal already has detailed information on housing units in the pipeline. The information could be improved, but this ask is useless unless it specified how. As mentioned before, detailed information on housing starts is already easily available as open data, monthly stats by structural type and intended market, down to the census tract level. It is less helpful than the other parts above and risks directing staff resources away from other project just to replicate what’s already out there.

Bottom line

There’s no way around it. How many dwellings to allow in a city is ultimately a moral question rather than a technocratic one. Given the overwhelming evidence that people want to live in places like Vancouver, population forecasts necessarily reflect first and foremost how many new dwellings we’re willing to allow. In technical terms, it’s silly to imagine we’re meeting the needs of population growth when we’re in fact setting a hard cap on population growth. In moral terms, we come back to the central question: Are we planning for kicking poor people out? Or are we open to inviting more people in?

As usual, the code underlying the stats and graphs is available on GitHub for anyone to reproduce or appropriate for their own use. And if you want to read (much) more about how to know if you have enough housing, check our simple metrics post.


  1. For example the City of of Vancouver only allows at most one kitchen per dwelling unit and limits the number of unrelated individuals sharing a dwelling to 3 (+ 2 boarders or lodgers) to restrict sharing of homes. [return]

COVID deaths in context by weeks

co-authored with Jens von Bergmann & cross-posted over at MountainMath

In our previous post on COVID mortality in context, we tried to place COVID deaths, as recorded so far this year, in the context of expected deaths from previous years. There have been a lot more developments since that post. And unfortunately a lot more deaths too.

Here we’re providing an update to our previous post, but also expanding on that post by talking a bit more about new mortality analyses and the progression of outbreaks in terms of expected deaths on a weekly basis. First, an update! We previously placed COVID deaths in the context of expected deaths at the national level, starting after the 20th death was recorded. What does that look like now?

COVID-2-a

COVID-2-b

As visible in the mortality data, Belgium has moved to the forefront of the COVID outbreak in Europe in terms of COVID deaths relative to expected deaths from years prior. Ireland, the UK, and the US appear to continue to climb. By contrast, Spain and Italy, early centres of the outbreak in Europe, have largely leveled off. Though the USA “leads” in deaths from COVID-19, this doesn’t (yet) show up in the relationship between COVID deaths and expected deaths because the USA is enormous, with a lot of expected deaths every year, and the outbreaks of COVID deaths have been heavily concentrated in a few locales so far.

Overall, and as mentioned previously, there’s still a lot we don’t know in these comparisons. For instance, we don’t know if we’re actually counting all of the deaths due to COVID. Lots of people don’t get tested, and cause of death is always tricky to determine in the best of times, let alone with an overloaded medical system and coroners’ offices. As a result, revisions to the data can add dramatically to the death toll, as happened recently in New York City. In addition to good COVID death data, we’d also like updated data on mortality overall. We’ve seen recent – and very preliminary – data out of NYC and scattered other locales suggesting that all-cause mortality has risen dramatically in places with severe COVID outbreaks.

Where we have it, we can put updated all-cause mortality in conjunction with COVID mortality and expected mortality all together. Putting this on a weekly basis really provides a sense of the progression of outbreaks and how overloaded they leave medical systems in terms of the normal deaths they have to deal with. Given some of the data from NYC, here’s roughly what that looks like.

COVID-2-c

 

We notice a downturn in deaths as recorded by the CDC FluView for the last week they report data (the week ending on 2020-04-12). This is not a REAL downturn. Rather it illustrates the reporting lag for data on deaths. It can take several weeks for the numbers to fill in and stabilize. We added the reported Covid-19 related deaths as assembled by the JHU for reference. JHU data was aggregated up the week ending 2020-04-19, so it’s nominally a week ahead of the FluView data. However, these deaths are coded by date reported, unlike the CDC data that is coded by date of death, which causes the JHU data to lead a bit. Even accounting for a possible time shift in JHU data, it appears that JHU data does not account for the full increase in all-case mortality, hinting at likely under-reporting of Covid-19 deaths in the JHU data.

Unfortunately we still don’t have updated all-cause mortality on the country level. As suggested by the lag in NYC data, it takes awhile to compile in the best of times (here’s a look at efforts to gather some of the European data). So here we’ll provide a replication of our previous analysis, but breaking out COVID deaths against expected deaths on a weekly basis for countries instead of across the entire length of the outbreak.

COVID-2-d

COVID-2-e

 

Overall, weekly COVID deaths as a percentage of expected deaths looks broadly similar to our earlier figure, which charted the rise in COVID deaths as a percentage of expected deaths since outbreak deaths began. But there are a few significant differences. The weekly chart better highlights the evolving overload on hospitals and health systems, as well as coroners’ offices, and this is reflected in the y-axis, demonstrating that COVID deaths in Belgium have more than doubled the expected deaths in the last week for which we have data. The weekly chart also more quickly identifies declines in the relative impact of COVID deaths in places where the worst of the outbreak has passed, like Spain, Italy, and France. It will take a long time for the expected death toll to diminish the impact of the overall death toll of COVID in our figures at the top of the post. But on a weekly basis, we can already see the toll of COVID receding in many places.

As we’ve noted previously, it will still take a long time to sort out the overall effects of COVID on mortality. Why? Well, we’re still nowhere near done with the outbreak, and we can expect deaths to continue until we have a vaccine and have reached some level of “herd immunity.” But we’ll also be sorting through the mortality data for years to come. Also important: the toll at national levels, while helpful in assessing cross-national differences, masks the impact at local levels where outbreaks often occur. So it is that the estimate from Belgium, where most recent weekly COVID deaths appear to have more than doubled expected mortality, is dwarfed by the estimate from New York City, where the most recent weekly COVID deaths appear to be more than six times the expected (pre-COVID) mortality.

As usual, the code for the post is available on GitHub in case anyone wants to refine or adapt it for their own purposes.

Context for COVID-19 Mortality so far

co-authored with Jens von Bergmann & cross-posted over at MountainMath

Unfortunately, more and more people are dying due to COVID-19. We won’t know the full toll from COVID-19 for quite some time. But we can at least start to get a sense of its impact. One useful way of assessing the impact, of course, is just to plot deaths attributed to COVID-19. This highlights the real loss of human lives associated with outbreaks. But as any demographer can tell you, deaths are a normal part of life. Within a given population, we can reliably expect a certain number of deaths to occur over any given time period. So another way of visualizing COVID-19 deaths is also useful: How many deaths attribute to COVID-19 are occurring as compared to the deaths we would normally expect to occur?

Below we follow the rise in deaths attributed to COVID-19 through time relative to the expected number of deaths that likely would have occurred without COVID-19 during the same time. [UPDATE April 20: our newest post plots this on a weekly basis]

 

COVID-mortality1

 

This visualization places deaths reported from COVID-19 in the context of expected deaths overall. This helps establish where we know the mortality toll has already been enormous. As of March 31, the end-point of the animation, Italy leads the overall count in deaths attributed to COVID-19. Here we can also report that in just over a month, Italy’s deaths so far attributed to COVID-19 already add more than 20% to its expected deaths. But Spain’s toll relative to its expected number of deaths is ever higher. In just over three weeks time, we can see that COVID-19 already accounts for more than a 30% rise over the deaths that would’ve been expected without COVID-19.

 

COVID-mortality2

 

Unfortunately, most curves are still rising. So far. Initially curves grow exponentially, until aggressive containment or mitigation strategies flatten them. Curves that stabilize and flatten, or even begin to turn downward, reflect countries where deaths attributed to COVID-19 are being overtaken by deaths that might’ve been expected to occur anyway. Hopefully this reflects an outbreak coming increasingly under control – GOOD NEWS – rather than a data gap.

But the possibility for data gaps is very real. It will be quite awhile before we can properly estimate the overall toll from COVID-19. We already have preliminary data on deaths attributed to COVID-19 rolling in. But this data will be messy, excluding cases where COVID-19 was missed as a cause, despite being present, and possibly over-including cases where the cause was actually not COVID-19 (e.g. instead common influenza), or COVID-19 was present but the death should be attributed primarily to a different underlying condition claiming the life. Cause of death data is never clean to begin with. As COVID-19 overwhelms medical systems and coroners’ offices, we should fully expect that data quality will suffer further.

More concretely, COVID-19 deaths will show up in the mortality databases with code U07.1 or U07.2 in the current ICD-10 classification system (or RA01.0 and RA01.1 once ICD-11 comes into effect). But many will likely also get classifed as J11, J18 or J22. When the dust settles, we will have to check how these cases have evolved over time and estimate how many cases in 2020 (or late 2019 in the case of China) are likely misclassified COVID-19 cases.

 

COVID-mortality3

 

We will also eventually get data about overall mortality. We will likely see deaths increase beyond those attributed directly to COVID-19. Deaths will rise both in response to complications introduced by COVID-19 in those with pre-existing conditions, and in response to people dying due to failure of overloaded medical systems to be able to respond to non COVID-19 cases they way they normally would. At the same time, some other non-COVID deaths may go down. This can happen when COVID-19 claims lives that otherwise might’ve been claimed by something else (e.g. an underlying condition). But it can also relate to deaths that don’t occur due to lockdown and the measures related to dealing with COVID-19. For instance, the regular toll of influenza may diminish in response to the lockdown targeted at Coronavirus (making it unclear what the “expected” baseline case count for 2020 should be). Similarly, fewer cars on the road will likely result in fewer deaths from car accidents. For references, see the most common causes of death in Canada in normal years here. A similar discussion of the eventual breakdown we’ll need in mortality data can be found in this demographer thread attempting to summarize some of this complexity via twitter feed.

The mortality data coming in bears watching, both in terms of COVID-19 attributed deaths and deaths overall. Some analysts (e.g. in Italy and Spain) as well as some China skeptics, are already drawing upon anecdotal mortality data to suggest that the toll from COVID-19 is far greater than revealed in the official data so far. These kinds of analyses are especially potent when applied to cities and regions as opposed to countries. But ultimately it will take years for demographers to sort this all out. In the meantime, we can at least get a rolling sense of COVID-19’s toll by looking at deaths attributed to Coronavirus relative to deaths otherwise expected based on past data from the same rough period of time.

As usual, the code for the post is available on GitHub in case anyone wants to refine or adapt it for their own purposes.

 

Update (2020-04-06)

It’s been a week since we posted this, and things are changing fast with covid-19 related deaths increasing exponentially and background mortality estimates only increasing linearly with time. The traces in the animated GIF already highlight this, but here is a quick update of what the graph looks like using data from a week later.

covid_mortality2

And for completeness, here is the static graph with the latest available numbers.

covid-19-mortality-final2-1

 

Overnight Visitors and Crude Travel Vectors

co-authored with Jens von Bergmann & cross-posted over at MountainMath

The spread of Coronavirus is reminding us of just how often people travel around, especially as various locations become quarantined and international travel corridors get shut down. So let’s take a look at some basic data on travel patterns here of relevance to us here in Vancouver. Then we’ll put them back in the context of Coronavirus.

TLDR: travel data is really interesting, don’t be frightened of travelers, and there’s still a lot we don’t know about coronavirus.

We’ve looked at the movement of people before in terms of migration, immigration and commuting patterns. But these are movements that are either regularized, everyday, and routine (e.g. commuting) or shuffle people between one settled set of routines and another (e.g. migration). Travel data gives us something different, representing something more like the unsettled movement of people. People travel for work, to visit family, and of course, for tourism. The Tourism Industry is interested enough in travel data that they ask Statistics Canada to compile data for them. Stats Canada combines Canadian travel surveys and border crossing administrative data to get us a decent look at overnight stays. So it is that we get overnight stayer data for Vancouver!

Let’s look at where people are visiting Metro Vancouver from. The Tourism Vancouver data has an interesting selection of countries available, with special breakdowns for Canada and the USA. More than a quarter of all overnight stays in Metro Vancouver are trips from elsewhere in British Columbia. Another quarter plus of trips arrive from elsewhere in Canada, with Ontario and Alberta leading the way. The USA accounts for just under a quarter of overnight visits. Altogether, Canada and the USA account for over 8 million of the roughly 10 million visits. Most American visitors to Metro Vancouver arrive from nearby neighbours down the Pacific Coast (WA, OR, CA), which together account for over half of travel from the USA. About as many people visit from all of Mexico as from nearby Oregon (140k).

Overnight1

Of the slightly less than two million international visitors from beyond NAFTA borders, a little over half arrive from Asian/Pacific countries, with most of the remainder from Europe. China, the UK, and Australia, Japan, India, and Germany each accounted for more than 100k visitors in 2019, South Korea, Hong Kong, and Taiwan not far behind. Let’s put all these flows together on a map (click for interactive access).

Overnight1a

Of some concern, lots of the places identified above have had recent outbreaks of Coronavirus. We’re still in early days of tracking the virus. And we know it’s already having major effects on travel. But can we look at current prevalence estimates and recent travel patterns to give some insights into crude vector risks for Metro Vancouver? Maybe. It’s worth keeping in mind that everything is still pretty much up in the air in terms of what we know!

First let’s look at up-to-date active confirmed Coronavirus cases drawing on data collected at Johns Hopkins.

Overnight2

Wuhan, of course, appears as the centre of the outbreak, and Hubei Province in China contains most of the active confirmed cases to date (as of March 03, 2020!) The number of cases is important to track, obviously, and the starting point for healthcare workers and epidemiologists alike. But focusing on these numbers can provide a misleading impression of how widespread the Coronavirus has become. So let’s come up with a crude estimate of prevalence instead of case numbers. Here we’re going to use active confirmed cases as our starting point. Another option is to track all confirmed cases, including those who have recovered (no longer testing positive) or died from coronavirus. But active confirmed cases might arguably give us a better sense of current spread.

We can plot the evolving nature of active confirmed cases in terms of prevalence estimates across places, effectively dividing total number of active confirmed cases by population for our data reported so far. Setting this to motion, we can track outbreaks by prevalence across time. Even just looking at active confirmed cases, we get a sense that recorded prevalence has recently stopped climbing for Hubei province. Meanwhile, outbreaks in South Korea, Iran, Hong Kong, and the nearby state of Washington continue to grow. Also worth noting, some countries (e.g. South Korea) seem to have a better handle on testing the virus, providing better confidence in their numbers. The numbers coming out of other locales (Iran and the USA) seem far less reliable, either because of inconsistent testing, untrustworthy reporting by officials, or both. This sets a real limit on what we can know so far.

overnight3

Overall it needs to be stressed that – given the numbers we have so far – the prevalence of coronavirus is still very low. Even in Hubei province, the centre of the outbreak, not much more than a single active confirmed case per thousand people has been confirmed. Comparing locations of cases to surrounding populations, most places around with the world with outbreaks still see only about one active confirmed case per hundred thousand people. Even setting aside the hyper-cautious mood around the world and its effects on travel, if you met a visitor from one of these places in Metro Vancouver, fairly unlikely that they would be a carrier. There’s little reason to be scared of individual travellers!

But what about travel patterns writ large? Surely even if any individual presents a very low risk as a vector, by sheer number, the masses of people travelling through Vancouver from places with coronavirus outbreaks represent a risk. Indeed, that’s how the coronavirus has spread so far. We can very crudely estimate this risk by setting a base likelihood that each individual traveller from a given outbreak location is coronavirus-free (1 – cases / population). In other words, we might use currently active confirmed cases as our measure of prevalence, estimating we can be 99.99975% certain that a given traveller from Washington State will not be a carrier for coronavirus. But what if a LOT of people travel from Washington? Then we exponentiate 99.99975% by the number of visitors (126,493 for the first three months of 2019 as a proxy) to come up with an estimate that none of these travellers carry the virus (we really should be drawing without replacement here, but this is a good approximation), with the complement giving a rough estimate of at least one visitor being a carrier. This comes out at 27% using our current estimates. This only considers Washington residents travelling to Vancouver and still neglects Vancouver residents travelling to Washington and getting infected there. And it relies on current active confirmed cases, it does not include active but not yet confirmed cases. And it assumes travel patterns similar to a year ago. Still, it provides us with a measure of vector risk to Metro Vancouver that combines risk of coronavirus with travel volumes.

Let’s run with this for recent coronavirus outbreak data based on travel volumes similar to past years – EXCEPT excluding cases from Hubei province in China after January 23rd (when the quarantine went in place). What does our crude evolving overnight travel vector risk look like?

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Here we can see rapidly changing vector possibilities. Conditions are changing fast! Still, it’s hard to know how much to trust these numbers. Given what we understand about testing at the moment, it’s likely we’re still overstating the risk from high quality testing locales (South Korea), as well as understating the risk from places where testing has been poor (Washington) and places where we don’t have any visitor data at all (Iran). We’re also missing current data on how travel is changing as well as data on where people from Metro Vancouver are traveling, which is a big deal given that most of our cases so far represent returned travelers from abroad.

Here is a still of the most recent snapshot as of the writing of this.

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Upshot

So here are the big takeaways from our exercise: 1) Visitor data to Metro Vancouver is actually really interesting, even for those outside of the tourism business. 2) Don’t shun travelers from abroad! The likelihood of anyone you meet, even coming from an outbreak centre, being a carrier of coronavirus is very, very low. 3) The combination of travel patterns plus coronavirus prevalence gives us some interesting ways to model evolving vector risks in Metro Vancouver. 4) But it’s not clear how much we should trust our data. Travel patterns have surely altered, and we need better coronavirus testing fast, especially in places like Washington State.

Overall, integrating travel data with coronavirus data may, if nothing else, help people and agencies prepare and plan better. Practically any planning is better than some of the ad hoc decisions being made out there, as when American Airlines suspended its flights to Milan only after pilots refused to fly there. For most people, the important thing is to listen to local health agencies, like the BC Centre for Disease Control, wash your hands, and be kind to those around you, wherever they come from.

As usual, the code for the post is available on GitHub in case anyone wants to refine or adapt it for their own purposes.

 

UPDATE: For a look at how the professionals are joining international travel data to coronavirus data, see Gardner (et al) here (now unfortunately outdated!)

Keep On Moving

co-authored with Jens von Bergmann & cross-posted over at MountainMath.

More results from the new Canadian Housing Survey dropped earlier this week! And they provide new insights into why Canadians move.

Last time we only got provincial results. Now we can break down reasons for the last move by metro area and current tenure, but this time around we looking at the last move no matter when it happend, as opposed to only considering moves in the past five years as in the previous data release. So the stats aren’t directly comparable to the numbers from the previous release. But as we’ll show, the trends are pretty similar.

First to the question guide. Lots of good stuff here, but we’re interested in the questions about peoples’ previous residence: “People move for a variety of reasons, either voluntary or non-voluntary. Why did you move from your previous dwelling?” Importantly, respondents are allowed to choose more than one, and only the respondent (rather than other household members) counts. Let’s look at the proportion of people selecting each reason for their last move by metro and by current tenure.

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Overall the reasons for moving is fairly uniform across major metro areas, with generally positive housing moves explaining most moves, as we’ve noted before. Hence people move to “upgrade” their dwelling in size or quality; to “become a homeowner”; and to “be in a more desirable neighbourhood.” More ambiguous housing moves, including those to “reduce housing costs”, vie with family-related moves (“change in family size”; “form own household”; “be closer to family”) and work-related moves (“new job”; “reduce commute”) as explanations.

Separating by current tenure (did people move into a place they rent or a place they own), the stories are still pretty similar. The first big takeaway is that mobility is pretty normal and common, and most people move for positive reasons. But there are a couple of notable differences. Moving “to reduce housing cost” or “to reduce commute time” factor more into renter’s than into homeowner’s decisions to move.

Finally, there’s are two reasons for moving that seem unambiguously negative for those involved, reflecting “forced moves.” One set of “forced moves” occur due to “natural disasters and fires.” The other comes down to social causes: “Because you were forced to move by a landlord, a bank or other financial institution or the government.” This happens far more often to renters and far more often in Metro Vancouver.

This brings us to the second big takeaway. In terms of forced moves, Vancouver sticks out like a sore thumb.

keepmoving-chs2-2

While Vancouver stands out, the other CMAs and rural areas in BC follow closely behind. Exposure to socially forced moves (e.g. evictions) seems to reflect something province-wide. Like our provincial protections for renters (Residential Tenancies Act) and how they’re enforced (or not) by the RTB. Or like our profound lack of rental options overall (low vacancy rates coupled with sometimes predatory landlords). Or like our heavy reliance upon the least secure kinds of rental stock (basement suites and condominium rentals) within secondary rental markets and subject to landlords reclaiming for their own use.

The results we have so far may reflect past conditions rather than the present. After all, we’re looking at peoples’ last moves here, many of which occurred more than five years ago. But we’ve got lots to follow up on in future analyses. And hopefully further releases from the CHS will clarify just what mechanisms are at work driving outsized displacement in Metro Vancouver.

As usual, the code for the post is available on GitHub for anyone interested.