The impact of ethnic segregation on neighbourhood-level social distancing in the United States amid the early outbreak of COVID-19

Wei Zhai, Xinyu Fu, Mengyang Liu and Zhong-Ren Peng
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Wei Zhai: Hong Kong Baptist University, China
Xinyu Fu: University of Waikato, New Zealand
Mengyang Liu: Huazhong University of Science and Technology, China
Zhong-Ren Peng: University of Florida, USA

Urban Studies, 2023, vol. 60, issue 8, 1403-1426

Abstract: The COVID-19 pandemic has been argued to be the ‘great equaliser’, but, in fact, ethnically and racially segregated communities are bearing a disproportionate burden from the disease. Although more people have been infected and died from the disease among these minority communities, still fewer people in these communities are complying with the suggested public health measures like social distancing. The factors contributing to these ramifications remain a long-lasting debate, in part due to the contested theories between ethnic stratification and ethnic community. To offer empirical evidence to this theoretical debate, we tracked public social-distancing behaviours from mobile phone devices across urban census tracts in the United States and employed a difference-in-difference model to examine the impact of racial/ethnic segregation on these behaviours. Specifically, we focussed on non-Hispanic Black and Hispanic communities at the neighbourhood level from three principal dimensions of ethnic segregation, namely, evenness, exposure, and concentration. Our results suggest that (1) the high ethnic diversity index can decrease social-distancing behaviours and (2) the high dissimilarity between ethnic minorities and non-Hispanic Whites can increase social-distancing behavior; (3) the high interaction index can decrease social-distancing behaviours; and (4) the high concentration of ethnic minorities can increase travel distance and non-home time but decrease work behaviours. The findings of this study shed new light on public health behaviours among minority communities and offer empirical knowledge for policymakers to better inform just and evidence-based public health orders.

Keywords: big data; COVID-19; ethnic segregation; social distancing; å¤§æ•°æ ®; æ–°å† è‚ºç‚Ž; ç§ æ— éš”ç¦»; ç¤¾äº¤è· ç¦» (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:sae:urbstu:v:60:y:2023:i:8:p:1403-1426

DOI: 10.1177/00420980211050183

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