Another indicator that #WFH is permanent: public transit journeys stabilizing at 35% below 2019 levels.
This raises concerns over the survival of public transit systems. Costs are heavily fixed - think train and subway networks - but revenue is way down with 35% less journeys.
Thanks for great comments.
Yes shopping (see figure), eating out and entertainment are almost back at pre-pandemic levels. So commuting must have dropped >35%.
Yes this is big cities - 30% NY, 30% DC, Chicago etc - so a heavy office commuting skew. So aligns with Kastle data.
Replying to @p_ganong on city variations. Below is NY which shows about at 50% drop adjusting for seasonality.
Generally you see transit systems with more rail and subway have bigger drops as more office commuters. Transit systems that are more bus focused have smaller drops.
Many people have asked about social distancing as a factor deterring the return to public transport. That is absolutely right - in our monthly survey 24% of respondees state they definitely would avoid this and another 30% likely would.
Most public transit is in cities, and these have seen the largest increases in post pandemic WFH.
Cities tend to have more tech and finance, more graduates and more office jobs, all of which drives higher rates of WFH.
Making this harder for public transit agencies is the increasing peak loading on Tuesday and Wednesday, which have become the popular office days.
Long-run public transit may need a 3-tier pricing scheme:
High - Tue/Weds
Medium - Mon/Thur/Fri
Low - Sat/Sun
A number of questions on international data - I would think this is a global issue (or at least for large cities in high-income countries). Our survey data shows substantial post-pandemic levels of WFH amongst professionals around the world.
bit.ly/3Wc2Cvq
In response to a question on the UK (my homeland)
it appears that the UK, as well as Canada, have had very similar increases in WFH. Possibly the best time-series data on this is from Google Workplace Mobility which tracks journeys to work and back (using phone location data).
I thought it could help to provide a replication file for the figure which is in the link below. There is a ton of great data from the NTD website.
Any corrections, further results for different cities, modes, periods etc post as a reply here thanks
bit.ly/3GvLRpV
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