Working from a distance: Who can afford to stay home during COVID-19? Evidence from mobile device data
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Abstract
As the COVID-19 pandemic continues, local and state governments must weigh the costs and benefits of social distancing policy. However, the effectiveness of such policies depend on individuals’ willingness and ability to comply. We propose a simple method to infer sociodemographic heterogeneity in social distancing as measured by Safegraph mobile device data. We document evidence that people’s ability to work from home is a determinant of time spent at home since the beginning of the pandemic. On April 15th, census block groups that are more likely able to work from home spent 3 more hours at home compared to those who were not. We see supporting trends among block groups with differences in income and educational attainment.
JEL
J19, J69, Z00
Article activity feed
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SciScore for 10.1101/2020.07.20.20153577: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:This simple approach to parsing distancing metrics has its limitations. It is possible that the population of device users in a CBG does not align with the category that it has been assigned in our analysis. Mobile device ownership is unequally distributed across society with younger people more likely to have smartphones and access to …
SciScore for 10.1101/2020.07.20.20153577: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:This simple approach to parsing distancing metrics has its limitations. It is possible that the population of device users in a CBG does not align with the category that it has been assigned in our analysis. Mobile device ownership is unequally distributed across society with younger people more likely to have smartphones and access to the internet. In 2018, 95% of individuals ages 18-34 in the U.S. had smartphones while only 67% of individuals older than 50 owned smartphones (Silver, 2019). Our classification method may be more robust to these effects since we focus on more homogeneous CBGs. However, more research is needed to understand the extent to which mobile device users are representative of the population at large.
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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