Do stay-at-home orders cause people to stay at home? Effects of stay-at-home orders on consumer behavior

Abstract

We link the county-level rollout of stay-at-home orders to anonymized cell phone records and consumer spending data. We document three patterns. First, stay-at-home orders caused people to stay home: Countylevel measures of mobility declined 8% by the day after the stay-at-home order went into effect. Second, stay-at-home orders caused large reductions in spending in sectors associated with mobility: small businesses and large retail stores. However, consumers sharply increased spending on food delivery services after orders went into effect. Third, responses to stay-at-home orders were fairly uniform across the country, and do not vary by income, political leanings, or urban/rural status.

Multidimensional economic deprivation during the coronavirus pandemic: Early evidence from the United States

“…There is increasing evidence, which shows that consumer spending in the United States sharply reduced in sectors such as hotels, transportation and food-services, which require inperson interaction (e.g. [13][14][15]). Other sectors, which did not require physical contact-such as landscaping, construction or financial services, experienced much smaller losses.…”

Fear, lockdown, and diversion: Comparing drivers of pandemic economic decline 2020

“…One involves studies using cellular phone data to track how fear of the virus or lockdown orders have affected personal mobility and interactions. Examples include Alexander and Karger (2020), Alfaro et al (2020), Barrios et al (2020), Chen et al (2020), Cicala et al (2020), Couture et al (2020), Dave et al (2020a), Fang et al (2020), , . Goldfarb and Tucker (2020) tie personal mobility to retail activity by evaluating which retail industries have the most social interaction.…”

Changes in consumer behaviors and financial well-being during the coronavirus pandemic: results from the U.S. Household Pulse Survey

“…3 This is both because the rich account for a larger share of spending to begin with and because they cut spending more in percentage terms: top-quartile households spent 13% less as of mid-July than in January 2020, whereas bottom-quartile households spent only 4% less. Spending reductions were concentrated in services that require in-person physical interaction, such as hotels and restaurants, consistent with contemporaneous work by Alexander and Karger (2020) and Cox et al (2020). These findings suggest that high-income households reduced spending primarily because of health concerns rather…”

“…In the macroeconomic measurement literature, our work is most closely related to recent studies showing that private sector data sources can be used to forecast government statistics (e.g., Abraham et al 2019, Aladangady et al 2019, Ehrlich et al 2019, Cajner et al 2019, Gindelsky, Moulton, and Wentland 2019, Dunn, Hood, and Driessen 2020. In the COVID-19 pandemic literature, several recent papers -whose results we compare to ours in the course of our analysis below -have used confidential private sector data to analyze consumer spending (e.g., Baker et al 2020, Chen, Qian, and Wen 2020, Cox et al 2020, business revenues (e.g., Alexander and Karger 2020), and labor market trends (e.g., Bartik et al 2020, Cajner et al 2020, Kurmann, Lalé, and Ta 2020, Forsythe et al 2020.…”