Exploratory spatial analysis of the health and economic effects of COVID-19 using global data
DOI:
https://doi.org/10.18559/rielf.2023.2.1Keywords:
croissance, écart de production, COVID-19, autocorrélation spatialeAbstract
Purpose : This article analyses the health and economic effects of the COVID-19 pandemic.
Design/methodology/approach : The sample includes 132 countries, and the methodology is based on Exploratory Spatial Data Analysis. The calculation of the output gap by the Hodrick-Prescott filter allows to highlight the economic impact of the health crisis, through the output gaps in 2020. The health variable, for its part, is measured by the incidence rates of COVID-19.
Findings : The results of the estimations validated the hypothesis of spatial autocorrelation for both the health and economic variables. Examination of the Moran scatter plot confirms the positive local spatial association pattern, i.e. the existence of similarities between neighbouring countries in the manifestation of the pandemic and spatial heterogeneity between groups of countries. More specifically, the results show the existence of clusters with low levels of COVID-19 incidence in Africa and Asia, compared with Europe and North America. In addition, while high-income countries were generally more affected in terms of health, they developed greater economic resilience.
Originality/value : These results show that taking space into account could provide a better understanding of the dynamics of health and economic shocks.
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