Abstract
Using monthly zip-code level data on credit card transactions covering 16 U.S. cities, this paper investigates changes in consumption at local commercial places during the early coronavirus disease 2019 (COVID-19) era. Since using aggregate-level data can suppress valuable information on consumption patterns coming from zip codes, the main contribution is achieved by estimating common factors across zip codes that are controlled for factors that are zip-code and time specific as well as those that are zip-code and sector specific. The estimation results based on common factors across zip codes show that relative consumption of products and services that can be consumed at home (e.g., grocery, pharmacy, home maintenance) has increased up to 56% amid COVID-19 compared to the previous year, whereas relative consumption of products and services that cannot be consumed at home (e.g., fuel, transportation, personal care services, restaurant) has decreased up to 51%. Similarly, after controlling for the corresponding factors, online shopping has increased up to 21%, while its expenditure share has increased by up to 16% compared to the pre-COVID-19 period.
Highlights
Consumption within the U.S is reduced significantly due to the coronavirus disease2019 (COVID-19)
The results based on the sector-level data show that relative consumption of products and services that can be consumed at home has increased by up to 56% amid COVID-19, whereas relative consumption of products and services that cannot be consumed at home has decreased by up to 51%
The results based on online versus offline shopping show that online shopping has increased by up to 21%, while its expenditure share has increased by up to 16% compared to the pre-COVID-19 period
Summary
Consumption within the U.S is reduced significantly due to the coronavirus disease2019 (COVID-19). (2020), this reduction is evident widely across sectors (except for grocery) and especially for products purchased through offline (rather than online) shopping These observations based on nationwide data can suppress valuable information on consumption patterns coming from more disaggregated areas as their effects may cancel each other out during the aggregation process. The main strategy is to identify common factors across zip codes representing sector-level or online versus offline consumption patterns at the U.S national level that do not suffer from an aggregation problem This is achieved by estimating sector-time fixed effects or shopping channel-time fixed effects in the monthly zip-code level data, where factors that are zip-code and time specific as well as those that are zip-code and sector specific are controlled for
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