Data di Pubblicazione:
2024
Abstract:
This work presents a comprehensive analysis of the economic impact of the COVID-19
pandemic, with a focus on OECD countries and the Chilean case. Utilizing a clustering approach, the
research aims to investigate how countries can be categorized based on their pandemic mitigation
strategies, economic responses, and infection rates. The methodology incorporates k-means and
hierarchical clustering techniques, along with dynamic time warping, to account for the temporal
variations in the pandemic’s progression across different nations. The study integrates the GDP into
the analysis, thereby offering a perspective on the relationship between this economic indicator and
health measures. Special attention is given to the case of Chile, thus providing a detailed examination
of its economic and financial indicators during the pandemic. In particular, the work addresses
the following main research questions: How can the OECD countries be clustered according to
some health and economical indicators? What are the impacts of mitigation measures and the
pension fund withdrawals on the Chilean economy? The study identifies significant differences
(p-value < 0.05%) in the GDPs and infection rates between the two identified clusters that are
influenced by government measures, particularly in the banking sector (55% and 60% in clusters
1 and 2, respectively). In Chile, a rebound in the IMACEC index is noted after increased liquidity,
especially following partial pension fund withdrawals, thereby aligning with discrepancies between
model forecasts and actual data. This study provides important insights for evidence-based public
policies, thus aiding decision makers in mitigating the socioeconomic impact of global health crises
and offering strategic advice for a sustainable economy.
Tipologia CRIS:
14.a.1 Articolo su rivista
Keywords:
Economic sustainability; COVID-19; dynamic time warping (DTW); k-means; hierarchical clustering
Elenco autori:
Nicolis, Orietta; Maidana, Jean Paul; Contreras, Fabian; Leal, Danilo
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