(Joint with Nathan Lauster and cross-posted at HomeFreeSociology) In this post we look at the most recent population (and household) estimates to see if we can detect any signals concerning how the COVID-19 pandemic may have impacted how (and where) we live. This is inherently tricky; lots of things changed during COVID times, including how well our normal methods of estimation work. That makes time series less reliable, even as we’re especially concerned with how conditions have changed.
We noticed a lot of recent traffic to our blog post on the 2019 elections, so maybe that means that, now that all districts have been called, we should update the post with 2021 data. We will just lazily run the code from the old post with the new data. And given that the outcome was overall quite similar, we can also leave the text/commentary largely unchanged. Which makes life nice and easy for us.
COVID-19 got me thinking about trend lines and the different ways people generate and interpret them. This is a question that’s of course more general than just COVID-19, but let me use this as an example to explain some very basic principles. This post is motivated by discussions I have had with a number of journalists, including Chad Skelton who nerd-sniped me into writing a post on trend lines and a thread discussing trend lines with Roberto Rocha and Tom Cardoso.
At the end of this odd COVID-19 summer we launched a reading group to bring together people interested in diving into papers and books looking at housing issues. Geoffrey Meen and Christine Whitehead’s recently released book Understanding Affordability: The Economics of Housing Markets has been the group’s first read. We highly recommend the book, it’s a good read for anyone looking for a practical understanding of how housing markets work and ways to think about supply and demand and what they mean for housing affordability.
At the end of June StatCan released an interesting census tract level metric, dubbed the D-index, measuring how much the income distribution in each census tract differs from the metro-wide distribution, and we decided to take it for a test drive. We are a bit of a sucker for this kind of fine-geography index. Condensing our wealth of information into a single number is an interesting exercise that involves lots of attention to detail.