The COVID-19 pandemic outbreak caused many negative effects on both the global and national economies. To implement effective policies to mitigate the negative impact of a pandemic, it is necessary to identify particularly vulnerable areas. The objective of this paper is to rank the EU countries in terms of the level of vulnerability of their economies to the impact of the pandemic. For this purpose, the COVID-19 Economic Vulnerability Index (CEVI) was constructed. It replaces the 15-dimensional set of characteristics of the countries with one aggregate, synthetic indicator estimated for 27 EU member states. In the study multivariate statistical methods, including agglomerative clustering and multi-attribute methods of object assessment were used to analyse the effects of the pandemic. The research shows that EU countries have different levels of economic vulnerability to the impact of the COVID-19 pandemic. The southern European countries (Spain, Croatia, Greece and Italy), where the tourism sector plays an important role in GDP composition, are the most fragile. Germany and the Scandinavian countries proved to be the least sensitive to the negative impact of the pandemic. The CEVI can be an important part of the decision support system. It enables the identification of countries that show greater vulnerability to the economic impact of the COVID-19 pandemic and may help support countries that need help the most. The proposed index also indicates certain areas in the country’s economy that make it more vulnerable. The CEVI in combination with other instruments can be a very useful tool to improve the economy’s resilience and help it recover faster in the event of a pandemic shock.

Purpose: The paper's objective is to identify the similarities of EU countries in terms of the entire sets of indicators. Hence, the multi-dimensional cluster analysis is applied to evaluate the economic vulnerability to the impact of the COVID-19 pandemic. Design/Methodology/Approach: A hierarchical and non-hierarchical cluster analysis method was used in the paper. At the first step, EU countries were clustered with Ward's method, and the following k-means method was applied for grouping countries. Findings: In the study, four clusters were identified. Southern European countries grouped in the 1st Cluster performed the highest level of vulnerability to the negative impact of the COVID-19 pandemic. Germany, the Netherlands, Ireland, and the Scandinavian countries appeared to be the least vulnerable EU economies to the impact of the COVID-19 pandemic and were grouped in the 4th Cluster. The countries of Central and Eastern Europe, most of which joined the EU in the 21st century, were characterized by moderate vulnerability and belonged to the 2nd and 3rd Cluster. Practical Implications: The results obtained can be used by policymakers to make better decisions to mitigate the negative impact of the pandemic on economies. Originality/Value: Most clustering of countries according to the impact of a COVID-19 pandemic examines how the virus spreads from a medical point of view. There is little literature on the economic impact of a coronavirus pandemic. This study will fulfill this gap.

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