Readings: Millennial drivers, Racehorses, Crypto closures, & credit default swaps


I generally find it more satisfying to find out that I’m wrong about something material than finding out that I’m right. After all, I usually expect that I’m right, so finding out that I’m still right about something isn’t very exciting. Finding out I’m wrong, however, is gratifying, a sort of reassuring signal of both brain plasticity and exposure to people and data defensibly different from my biases. 

I got to thinking about that recently when I asked myself why I was so convinced that millennials were different from the rest of us with respect to driving. I cited data to me showing that millennials drive less, and are less likely to have cars. And I murmured LyftUberCarSharing to myself over and over. These all seem like good reasons, but I was still worried, given that Millennials Don’t Want To Drive (MDWTD) is a pat story, almost too good to check. After all, I want millennials to want to drive less, even if it always seemed wildly unlikely we could have such a rapid shift in a single quasi-generation. But hey, It is a good story! It is good for us!

But it seems increasingly likely that MDWTD is wrong, or at least highly, highly incomplete. A new NBER paper argues fairly convincingly that, adjusted for demographic and macroeconomic variables, not only do millennials not want to drive less, it seems like they drive more, at least as measured by vehicle-miles traveled (VMTs).  The real story may be that MDWTD was because of various choices — locations, jobs, etc. — that manifested themselves in somewhat less driving, but adjusted for those choices millennials are just as car-happy as the rest of us cheery planet-wreckers.

Whew. Apocalypse, on. More seriously, this is a nice example — like another one was needed — why the early presentation of an unlikely solution to a problem so easily blocks out more plausible ones, whether we are talking card tricks or young humans not wanting to get out there and race around.






Energy researcher Carey King gives a fascinating talk on how economic models need biophysical principles. Long, but rich and full of data.