COVID-19's Unequal Toll: An assessment of small business impact disparities with respect to ethnorace in metropolitan areas in the US using mobility data

TL;DR

COVID-19 mobility restrictions affected small urban restaurant visitation, with inequitable effects linked to ethnoracial composition. The study combines SafeGraph POI-level visitation data with ACS demographic data to track daily changes in visits to non-chain restaurants at the Census Block Group level and to compare recovery trajectories across majority-race groups using time-series and KS tests. It finds deeper declines and slower recoveries in Asian-majority and American Indian-majority neighborhoods, highlighting spatially explicit disparities in the urban restaurant sector. These findings provide localized evidence to inform targeted policy interventions and support for vulnerable small businesses during and after the pandemic.

Abstract

Early in the pandemic, counties and states implemented a variety of non-pharmacological interventions (NPIs) focused on mobility, such as national lockdowns or work-from-home strategies, as it became clear that restricting movement was essential to containing the epidemic. Due to these restrictions, businesses were severely affected and in particular, small, urban restaurant businesses. In addition to that, COVID-19 has also amplified many of the socioeconomic disparities and systemic racial inequities that exist in our society. The overarching objective of this study was to examine the changes in small urban restaurant visitation patterns following the COVID-19 pandemic and associated mobility restrictions, as well as to uncover potential disparities across different racial/ethnic groups in order to understand inequities in the impact and recovery. Specifically, the two key objectives were: 1) to analyze the overall changes in restaurant visitation patterns in US metropolitan areas during the pandemic compared to a pre-pandemic baseline, and 2) to investigate differences in visitation pattern changes across Census Block Groups with majority Asian, Black, Hispanic, White, and American Indian populations, identifying any disproportionate effects. Using aggregated geolocated cell phone data from SafeGraph, we document the overall changes in small urban restaurant businesses' visitation patterns with respect to racial composition at a granularity of Census Block Groups. Our results show clear indications of reduced visitation patterns after the pandemic, with slow recoveries. Via visualizations and statistical analyses, we show that reductions in visitation patterns were the highest for small urban restaurant businesses in majority Asian neighborhoods.

Figures (5)

• Figure 1: Urban Census Block Groups representing all the large metropolitan areas used in our analysis.
• Figure 2: Count of CBGs associated to each majority race.
• Figure 3: Racial composition at selected urban locations.
• Figure 4: Heatmap matrices for percentage changes in visit patterns
• Figure 5: Average percentage change time series across all CBGs for each ethnicity or race. The vertical dashed lines separate the pre-pandemic phase from phases 1, 2 and 3.