COVID 19… 84? The stringency of responses to COVID-19 across the world

Abstract

In December 2019, the world heard about the COVID-19 virus for the first time. It has been almost two years since the date, yet political science and public policy disciplines cannot predict or explain the stringency level of introduced anti-coronavirus measurements. This MA thesis strives to fill in the lacuna from both theoretical and empirical viewpoints. For the former aspect, the paper deconstructs the process of COVID-19 policymaking on the basis of “policy diffusion” theory by Berry and Berry (2006) and other complementary theories (i.e., “external shock”, “state capacity”, “diffusion of innovations” theories). Such a complex framework allowed the paper to unpack every element of “policy diffusion” theory and, thus, produce a more detailed description of principles within the decision-making procedure. The theory computed thirteen hypotheses, extrapolated on 185 countries in the sample, which were tested in a large-N quantitative empirical analysis via the visualisations, correlations, the OLS regressions, and Bayesian Network methods. The analysis results are the following: the salience of the coronavirus crisis (number of cases/deaths; Cases Per Capita; Case Fatality Rate) is the primary explanatory variable for the high level of stringency embedded in the policy response. At the same time, as specified in the literature, the autocracies do apply more stringent policies; the malfunctioning accountability mechanism might explain this. What is more vital in terms of policy recommendations is that sufficient healthcare capacities (i.e., the number of hospital beds and healthcare expenditures) might mitigate the side-effects of the coronavirus, thus states introduce more lenient anti-COVID-19 policies. Additionally, the population density, institutional trust, and state’s economic support have a positive association with the stringency. Apart from testing the connections between the variables, the paper has also left some clues for the following research, for instance, there is a regional pattern in terms of COVID-19 responses: some regional units might be more stringent than others.