Abstract: China's economic recovery relies on the coordination between the pandemic control and socioeconomic development. Based on the Baidu migration big data, this study employs difference-in-difference model to explore the impacts of pandemic prevention and control measures on the population flow at different stages. The results show that the unconventional measures proposed in the early stage have effectively controlled the population flow, with the average intensity of urban population inflow, outflow and intra-city flow reduced by 71.21%, 72.62%, and 45.99% respectively. After the resumption of work and production, the population flow began to rebound, with the inter-city flow more than doubled. The pandemic risk level is a vital factor affecting the resumption of population flow in China. Therefore, differentiated accurate prevention and control measures should be adopted to create favorable conditions for promoting economic development in the era of regular pandemic prevention and control.

Keywords: Big Data, COVID-19 Pandemic, Population Flow, Resumption of Production and Work, Difference-in-Difference Model

摘要： 统筹推进疫情防控和经济社会发展工作是常态化疫情防控阶段促进中国经济恢复的必然选择。基于百度地图迁徙大数据，采用双重差分模型探究不同阶段的疫情防控措施对中国人口流动的影响。结果表明，早期的疫情超常规防控措施有效控制了人口流动，导致中国城市平均人口迁出、迁入强度和城市内部出行强度分别降低了71.21%、72.62%和45.99%，且武汉市的下降幅度尤为显著。在实行差异化防控策略推进复工复产后，全国人口流动开始反弹，城市间人口流动增长了1倍以上，且城市内部出行强度基本恢复到2019年农历同期水平。此外，疫情风险级别是影响中国当前人口流动恢复的重要因素。因此，在常态化疫情防控阶段，需采取差异化的策略实施精准防控，积极为推动经济恢复创造有利条件。

关键词: 大数据, 新冠肺炎疫情, 人口流动, 复工复产, 双重差分模型