Using Mobile Device Data to Understand the Effect of Stay-at-Home Orders on Residents’ Mobility
利用移动设备数据，通过双重差分模型估计美国居家令对居民居家比例的影响，发现命令使居家比例提高2.832个百分点，但对无保险或低教育程度县的效果较弱，因其工作无法远程完成。
Problem definition: This study addresses three important questions concerning the effectiveness of stay-at-home orders and sociodemographic disparities. (1) What is the average effect of the orders on the percentage of residents staying at home? (2) Is the effect heterogeneous across counties with different percentages of vulnerable populations (defined as those without health insurance or who did not attend high school)? (3) If so, why are the orders less effective for some counties than for others? Academic/practical relevance: To combat the spread of coronavirus disease 2019 (COVID-19), a number of states in the United States implemented stay-at-home orders that prevent residents from leaving their homes except for essential trips. These orders have drawn heavy criticism from the public because whether they are necessary and effective in increasing the number of residents staying at home is unclear. Methodology: We estimate the average effect of the orders using a difference-in-differences model, where the control group is the counties that did not implement the orders and the treatment group is the counties that did implement the orders during our study period. We estimate the heterogeneous effects of the orders by interacting county features with treatment dummies in a triple-difference model. Results: Using a unique set of mobile device data that track residents’ mobility, we find that, although some residents already voluntarily stayed at home before the implementation of any order, the stay-at-home orders increased the number of residents staying at home by 2.832 percentage points (or 11.25%). We also find that these orders are less effective for counties with higher percentages of uninsured or less educated (i.e., did not attend high school) residents. To explore the mechanisms behind these results, we analyze the effect of the orders on the average number of work and nonwork trips per person. We find that the orders reduce the number of work trips by 0.053 (or 7.87%) and nonwork trips by 0.183 (or 6.50%). The percentage of uninsured or less educated residents in a county negatively correlates with the reduction in the number of work trips but does not correlate with the reduction in the number of nonwork trips. Managerial implications: Our results suggest that uninsured and less educated residents are less likely to follow the orders because their jobs prevent them from working from home. Policy makers must take into account the differences in residents’ socioeconomic status when developing new policies or allocating limited healthcare resources. History: This paper has been accepted for the Manufacturing & Service Operations Management Special Section on Responsible Research in Operations Management. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.1014 .