Trade Heterogeneity in the EU: Insights from the Emergence of COVID-19 Using Time Series Clustering

Abstract

Objective: The objective of the paper is to analyse segmentation of EU-27 countries based on quarterly growth rates of exports and imports by using time series clustering. Research Design & Methods: We applied a time series clustering algorithm using TS nodes in SAS Enterprise Miner. To analyse the impact of the pandemic, we considered clusters based on export and import growth rates for two time periods, pre-emergence and post-COVID-19 emergence. Findings: We find that grouping based on export and import growth rates vary for EU-27 countries. Also, clustering results change significantly for post-COVID-19 emergence compared to pre-COVID-19 emergence. Cyprus emerged as an exception based on export growth rates, while Malta came out as an outlier based on the segmentation of its import growth rates. Implications / Recommendations: The impact and severity of COVID-19 has varied across EU countries, which have shown a varied impact in their trade patterns characterised by growth rates of exports and imports. The clustering analysis presented in the paper helps to explain similarities and differences in trade patterns of EU members during the COVID-19 pandemic to effectively implement and harmonise EU specific trade policies to member countries. Contribution: The study contributes to the literature on EU trade by providing an approach to analysing EU-27 segments using time series clustering analysis. It also enhances the growing literature on the impact of the pandemic on international trade by separating clustering analysis for the COVID-19 period and investigating the drivers for the segmentation.

Keywords

• COVID-19
• international trade
• European Union
• exports
• imports
• time series