Much has changed in the wake of the COVID-19 pandemic, consumer behavior notwithstanding. In this study, we explore the drivers of retail mobility in several metropolitan areas in the United States under the perceived risks of COVID-19. We measure risk using both local and national case and death counts of COVID-19 and assess the response to these risks using real-time Google Trends searches for personal protective equipment (PPE) including masks, hand sanitizer, and disinfectant. We further investigate the effects of perceived risk, Google Trends searches for PPE, and COVID-19 tweets on retail mobility, as measured via real-time Google Mobility data. We focus our analysis on the period immediately preceding and following the formal declaration of a pandemic when the first nation-wide social distancing policies were implemented, and uncertainty regarding the severity and transmissibility of the virus was the highest. To conduct our investigation, we develop a novel optimization scheme and procedure to induce linear models containing strictly significant covariates and no multicollinearity, which we then analyze. Our findings suggest that there are a variety of unique factors that contribute to and drive retail mobility in different geographic locations, as well as several factors that are common to all locations.
Predicting Retail Mobility during the COVID-19 Pandemic