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We study the heterogeneous impacts of COVID‐19 on restaurants in the postlockdown United States, from lens of social interactions. We use the data structure of chain restaurants to disentangle restaurant attributes such as food and service types (which vary across chains) and local market conditions such as infection risks (which vary with each establishment's geographical location). We find that visits to chains with higher social indices experienced larger drops as local new cases increased in 2020, but also faster recovery later when vaccination programs expanded. Moreover, demand for restaurants in city centers recovered faster than demand for those in suburbs.

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