Research Paper: Social Distancing, Internet Access, and Inequality

Wanted to re-surface this incredible research paper by of MIT and Lesley Chiou of UC Berkeley: “Social Distancing, Internet Access, and Inequality”

Using SafeGraph data, they were able to show how the digital divide impacts people’s ability to follow health directives around social isolation. Specifically, they found that the combination of having high income and high-speed internet appears to be the biggest driver of the propensity to stay home.

Anything else you want to add that we should know about this paper?

https://www.nber.org/papers/w26982

They also gave a seminar on this topic. You can check it out anytime here: Social Distancing, Internet Access and Inequality


This topic was automatically generated from Slack. You can find the original thread here.

@Jeremy_Ney I can’t recall if you connected with Catherine or Lesley, but this would be interesting to add to American Inequality.

Thought you might want to see this, since you work on urban mobility study. This was an interesting finding that correlated income and high-speed internet access to health outcomes.

@Catherine_Tucker Did we ever introduce you to @Alan_Kwan? His work is incredibly relevant to your own.

Check it out: Slack

@Vesa_Pursiainen_University_of_Hong_Kong @Ben_Charoenwong_National_University_of_Singapore Have either of you met @Catherine_Tucker? Her work on social distancing and internet access in the context of inequality reminded us of your paper!

What has been the reception of your paper so far? Are there any resources you would have shared with Catherine, or that you’d share here for people working on something similar?

This is awesome! I wrote an article about this very issue here and founded on a nonprofit around this very issue too! NoOneLeftOffline.org - let me know if you want to connect (I’m also a Sloan alum too)!

This is a very convincing economic proof of something I had thought was true, but didn’t have any evidence behind. A few comments, if you are still looking for comments:

  1. In the descriptive statistics, the mean percentage of 60+ stood out to me. I’m very confused how this is 2.9%, when about 16.5% of Americans are over 65. It also seems that 75% of blocks have no seniors; this seems really high. Is this correct?
  2. I realize it’s been a while since this WP was posted, so you may have addressed this already, but Sun and Abraham 2020 shows that there can be issues with staggered timing in event studies, and that (alas!) there is a possibility of bias in TWFE. Have you done a robustness check with the Calloway and Sant’Anna estimator?
  3. When determining if a trip is for shopping or for work, is it possible to use the amount of time the phone is away from “home”? I don’t know if you have that information, but it would seem unlikely that a trip >4 hours is shopping, and probably a trip of 20 minutes is not going to work. That might be a more accurate way to predict what is work/non-work than using weekdays vs. weekends.