CEPN, CNRS, Université Paris Nord, Villetaneuse, France.
EconomiX, CNRS, Université Paris Nanterre, Nanterre, France.
Eur J Health Econ. 2021 Jun;22(4):629-642. doi: 10.1007/s10198-021-01280-6. Epub 2021 Mar 22.
Often presented as a global pandemic spreading all over the world, COVID-19, however, hit not only countries but also regions differently. The objective of this paper is to focus on the spatial heterogeneity in the spread of the COVID-19 pandemic and to contribute to an understanding of the channels by which it spread, focusing on the regional socioeconomic dimension. For this, we use a dataset covering 125 European regions in 12 countries. Considering that the impact of the COVID-19 crisis differed sharply not only across countries but also across regions within the same country, the empirical strategy is based, on the one hand, on an exploratory analysis of spatial autocorrelations, which makes it possible to identify regional clusters of the disease. On the other hand, we use spatial regression models to capture the diffusion effect and the role of different families of regional factors in this process. We find that the share of older people in the population, GDP per capita, distance from achieving EU objectives, and the unemployment rate are correlated with high COVID-19 death rates. In contrast, the number of medical practitioners and hospital beds and the level of social trust are correlated with low COVID-19 death rates.
虽然 COVID-19 通常被描述为一种在全球范围内传播的大流行病,但它不仅对各国,而且对各地区的影响也不尽相同。本文的目的是关注 COVID-19 大流行传播的空间异质性,并深入了解其传播途径,重点关注区域社会经济层面。为此,我们使用了涵盖 12 个国家的 125 个欧洲地区的数据。考虑到 COVID-19 危机的影响不仅在各国之间而且在同一国家内部的不同地区之间存在明显差异,因此,实证策略一方面基于空间自相关的探索性分析,这使我们能够识别疾病的区域集群。另一方面,我们使用空间回归模型来捕捉扩散效应以及不同类型的区域因素在这一过程中的作用。我们发现,人口中老年人的比例、人均国内生产总值、距离实现欧盟目标的距离以及失业率与 COVID-19 高死亡率相关。相比之下,医生和病床数量以及社会信任水平与 COVID-19 低死亡率相关。