A tale of lockdown policies on the transmission of COVID-19 within and between Chinese cities: A study based on heterogeneous treatment effect

Jingjing Li, Chu Zhuang and Wei Zou

Economics & Human Biology, 2024, vol. 53, issue C

Abstract: During the early outbreak phase of COVID-19 in China, lockdowns prevailed as the only available policy tools to mitigate the spread of infection. To evaluate the impact of lockdown policies in the context of the first phase of COVID-19 pandemic, we leverage data on daily confirmed cases per million people and related characteristics of a large set of cities. The study analyzed 369 Chinese cities, among which 188 implemented lockdowns of varying severity levels from January 23 to March 31, 2020. We use nationwide Baidu Mobility data to estimate the impact of lockdown policies on mitigating COVID-19 cases through reducing human mobility. We adopt a heterogeneous treatment effect model to quantify the effect of lockdown policies on containing confirmed case counts. Our results suggest that lockdowns substantially reduced human mobility, and larger reduction in mobility occurred within-city compared to between-city. The COVID-19 daily confirmed cases per million people decreased by 9% - 9.2% for every ten-percentage point fall in within-city travel intensity in t+7 timeframe. We also find that one city’s lockdowns can effectively reduce the spillover cases of the traveler’s destination cities. We find no evidence that stricter lockdowns are more effective at mitigating COVID-19 risks. Our findings provide practical insights about the effectiveness of NPI during the early outbreak phase of the unprecedented pandemic.

Keywords: COVID-19; Pandemic; Lockdown Policies; Non-pharmaceutical interventions; Heterogeneous Treatment Effect (search for similar items in EconPapers)
JEL-codes: C54 E61 E65 H75 I21 I28 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ehbiol:v:53:y:2024:i:c:s1570677x24000170

DOI: 10.1016/j.ehb.2024.101365

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