Political regimes and deaths in the early stages of the COVID-19 pandemic

Abstract

This article provides a quantitative examination of the link between political institutions and deaths during the first 100 days of the COVID-19 pandemic. We demonstrate that countries with more democratic political institutions experienced deaths on a larger per capita scale than less democratic countries. The result is robust to the inclusion of many relevant controls, a battery of estimation techniques and estimation with instrumental variables for the institutional measures. Additionally, we examine the extent to which COVID-19 deaths were impacted heterogeneously by policy responses across types of political institutions. Policy responses in democracies were less effective in reducing deaths in the early stages of the crisis. The results imply that democratic political institutions may have a disadvantage in responding quickly to pandemics.

Modeling the Social Determinants of Official COVID-19 Reports in the Early Stages of the Pandemic

“…In accordance with Cepaluni et al (2020), the former effect is even more pronounced in terms of the reported death toll and cases per capita (see Models 2–4 and 6–8). The impact of regulation and transparency on the reported death toll is significant and highly positive for both Europe and Africa, although these coefficients decrease with the extension of the time window.…”

Are Stringent Containment and Closure Policies Associated with a Lower COVID-19 Spread Rate? Global Evidence

“…Thus, we highly recommend that future research can better answer this question. Second, it is suggested that stringency may have some negative effects on a society along with its positive effect on the control of the COVID-19 pandemic [14,18]. However, this study primarily focused on the bright side of stringent government containment and closure policies, and provided little evidence on the dark side of it.…”

“…Diao et al [8] also noted that as the Chinese government implemented a strict lockdown policy at the beginning of the COVID-19 pandemic, there was no association between population density and the COVID-19 spread in China. According to the Oxford COVID-19 Government Response Stringency Index, which are widely used in extant studies [1,14] (refer to the Codebook for the Oxford Covid-19 Government Response Tracker for detailed information, https://github.com/OxCGRT/covid-policy-tracker/blob/master/documentation/ codebook.md#codebook-changelog, access on 25 September 2020), governmental policies can be classified into four types: (1) containment and closure policies; (2) economic policies;…”

“…Unlike other types of governmental policies, containment and closure policies have been under considerable debate because it not only erodes civil liberties, but also impairs economic growth [14,18]. Thus, government responses to the COVID-19 pandemic vary dramatically from one country to another, from China's full-scale lockdown of Wuhan to Sweden's "never-went-into-lockdown", and from South Korea's centralized control and communication [3] to the UK's "based on the science" approach [19].…”

“…On the other hand, Hale et al [1] noted that the effect of policy responses was highly contingent on local political and social contexts. In this regard, Cepaluni et al [14] found that stringent containment and closure policies were not effective in mitigating the COVID-19 pandemic in democratic countries. Brodeur et al [23] found that stay-at-home orders exhibited larger effects in high-trust counties rather than low-trust counties in the U.S.…”