Analysis of Policies Based on the Multi-Fuzzy Regression Discontinuity, in Terms of the Number of Deaths in the Coronavirus Epidemic

Abstract

It has been a year since the outbreak of the coronavirus epidemic 2019 (COVID-19). In the face of the global epidemic, governments in all countries have taken different prevention measures, such as social isolation, mandatory health protection, and the closure of schools and workplaces. The situation of the epidemic has clearly varied from country to country. In this context, research on the impact of policies for the control of the spread of the global epidemic is of great significance. In this paper, we examined data from a sample of 212 countries between 31 December 2019, and 21 May 2020, using multi-fuzzy regression discontinuity. We found that developed countries had relatively low sensitivity to the policy stringency index; however, policy control measures had a significant effect on epidemic control. In addition, the trend analysis showed that the corresponding management and control came into play only after the policy stringency index reached 50 or the policy management reached level II, and the robustness was optimal at this time. Therefore, the governments in all countries should realize that epidemic prevention and control are of great importance. They can strengthen policy stringency to control the spread of the epidemic, considering their national conditions in terms of the economy and health system.

Impact of Changes to Procedures on the Evaluation of the Effectiveness of Forms of Professional Activation in Poland

“…Similarly, Wang et al (2019) showed that in northern China, air quality has gradually improved due to a series of heating policy changes implemented by the central government in recent years. Wang et al (2021) analysed the correlation between the policy stringency index and the number of deaths from COVID-19 in the world. They showed that policy control measures had a significant effect on epidemic control.…”

“…It began to be used increasingly in socio-economic analysis from the late 1990s onwards. Since then, there have been many articles in the fields of education, the labour market, forensics, environmental protection, and health care, on the economic impact of regulatory change (Lee and Munk 2008;Lee and Lemieux 2010;Dong et al 2019;Lotsu et al 2019;Wang et al 2019Wang et al , 2021Ogunmodede et al 2020;Song et al 2020;Zhao et al 2021).…”

Using Grounded Theory to Identify Online Public Opinion in China to Improve Risk Management—The Case of COVID-19

“…Internet public opinion is an important feedback mechanism for the government’s risk management, and it is a “mirror” for the government to understand the people’s expectations and summarize political gains and losses [ 22 ]. By analyzing online public opinion data, checking and filling gaps in the process of public opinion screening, and taking targeted improvement measures, it is conducive to promoting the scientific and democratization of government risk management, and promoting governance innovation and improvement [ 23 , 24 ].…”

Impact of Subsidy Programmes on the Development of the Number and Output of RES Micro-Installations in Poland

“…One strand of literature has emerged on measuring the effects of potential benefit duration on unemployment duration [15][16][17][18]. Another strand in which RDD has been applied is the impact of regulatory changes on socio-economic phenomenon [19][20][21][22][23][24][25][26][27][28][29].…”