<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The COVID-19 pandemic was triggered on December 2019 in the city of Wuhan, China, spreading across the world causing global economic crisis and public health emergency. One could ask: what are the socio-economic factors that catalyse the spread of the disease and why are some countries more affected by the COVID-19 pandemic. Therefore, the goal of this paper is to investigate these socio-economic catalysers of the COVID-19 spread. For that purpose, a cross-country regression analysis was conducted at three time points (April 1st, 2020, April 15th 2020 and April 29th, 2020) using OLS, Tobit and PPML estimators. The results of the analysis have shown that countries with higher gross domestic product per capita, population, HDI and HFI indices have been hardely hit with the global COVID-19 pandemic. When some variables were transformed with by dividing it with the population variable, POPDEN and TOUR variables appeared to be significant. The AGE variable was important in the model taking into account total deaths due to the COVID-19 infection. The limitations of the paper are related to data unavailability for some variables in the most recent year. The results obtained from this analysis should be repeated, taking into account other time points and additional COVID-19 socioeconomic catalysers.</p>
</abstract>

The COVID-19 pandemic was triggered on December 2019 in the city of Wuhan, China, spreading across the world causing global economic crisis and public health emergency. One could ask: what are the socio-economic factors that catalyse the spread of the disease and why are some countries more affected by the COVID-19 pandemic. Therefore, the goal of this paper is to investigate these socio-economic catalysers of the COVID-19 spread. For that purpose, a cross-country regression analysis was conducted at three time points (April 1st, 2020, April 15th 2020 and April 29th, 2020) using OLS, Tobit and PPML estimators. The results of the analysis have shown that countries with higher gross domestic product per capita, population, HDI and HFI indices have been hardely hit with the global COVID-19 pandemic. When some variables were transformed with by dividing it with the population variable, POPDEN and TOUR variables appeared to be significant. The AGE variable was important in the model taking into account total deaths due to the COVID-19 infection. The limitations of the paper are related to data unavailability for some variables in the most recent year. The results obtained from this analysis should be repeated, taking into account other time points and additional COVID-19 socioeconomic catalysers.

The socio-economic catalysers of COVID-19 pandemic

Faculty of Economics and Business in Zagreb

Croatian Review of Economic, Business and Social Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"The socio-economic catalysers of COVID-19 pandemic"}

The COVID-19 pandemic was triggered on December 2019 in the city of Wuhan, China, spreading across the world causing global economic crisis and public health...

The socio-economic catalysers of COVID-19 pandemic

Data publikacji: 05 sty 2021

Zakres stron: 12 - 26

Otrzymano: 19 paź 2020

Przyjęty: 23 lis 2020

DOI: https://doi.org/10.2478/crebss-2020-0008

Słowa kluczowe
COVID-19, PPML, socio-economic catalysers, Tobit

© 2020 Hrvoje Jošić, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

The socio-economic catalysers of COVID-19 pandemic

Data publikacji: 05 sty 2021

Zakres stron: 12 - 26

Otrzymano: 19 paź 2020

Przyjęty: 23 lis 2020

DOI: https://doi.org/10.2478/crebss-2020-0008

Słowa kluczoweCOVID-19, PPML, socio-economic catalysers, Tobit

© 2020 Hrvoje Jošić, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Słowa kluczowe
COVID-19, PPML, socio-economic catalysers, Tobit