Stay-at-Home Orders, Mobility Patterns, and Spread of COVID-19

Abstract

Objectives. To understand how stay-at-home orders changed mobility patterns and influenced the spread of COVID-19.Methods. I merged 2020 data from the Virginia Department of Health, Google Mobility Reports, and the US Census to estimate a series of 2-way fixed-effect event-study regression models.Results. A stay-at-home order caused people to increase the amount of time spent at home by 12 percentage points and decrease the time the spent at work by 30 percentage points, retail and recreation venues by 40 percentage points, and grocery stores and pharmacies by 10 percentage points. People did not sustain changes in mobility and gradually returned to prepandemic levels before the stay-at-home order was lifted. In areas where people spent the most time at indoor locations, there was a large increase in COVID-19.Conclusions. A more robust and stricter policy response coordinated at the national level combined with a strong economic response from policymakers could have increased the effectiveness of the stay-at-home order.

FIGURE 1—

Map of Virginia With Major Metropolitan Statistical Areas Source. Author’s calculations using shapefile data from the US Census Bureau.

FIGURE 2—

Daily New Cases of COVID-19 per 1000 People in Virginia Source. Author’s calculations using data from the Virginia Department of Health. Note. Graph shows a 7-d moving average of COVID-19 cases in Virginia per 1000 people.

FIGURE 3—

Two-Way Fixed Effect Event-Study Results for Mobility Patterns in Virginia by Metropolitan Statistical Area for (a) Places of Work, (b) Home, (c) Retail and Recreation, and (d) Grocery and Pharmacy: February 2020–August 2020 Note. Each line in each graph represents the results of a separate 2-way fixed-effect event-study regression of the specified mobility pattern. The solid line represents the point estimates of D_i,t from equation 1. The base date of comparison is February 20, 2020. Controls included in the regressions are the lagged inverse hyperbolic sine of total cases, population density, urban status, a month fixed effect, and a county fixed effect. The shaded area represents a 95% confidence interval of the point estimate clustered at the county level.

FIGURE 4—

Correlation Between Changes in Mobility and Change in COVID-19 Cases in Virginia for (a) Places of Work, (b) Home, (c) Retail and Recreation, and (d) Grocery and Pharmacy: February 2020–August 2020 Source. Author’s calculations using Google Mobility Reports and data from the Virginia Department of Health. Note. Each panel shows the correlation between changes in mobility patterns for that venue and percentage change in new COVID-19 cases 11 d later. Significance is P < .05.