ABSTRACT
The outbreak of COVID-19 at the end of 2019 has become the most devastating public health event of the 21st century. The different performances of governments and people in different countries and regions show that national values may play an important role in the prevention and control of COVID-19. Based on data from the seventh wave of World Values Survey (WVS-7) and the Human Freedom Index (HFI) report in 2020, three national value factors are extracted in this manuscript, including religious belief, government satisfaction and individual freedom. Then ordinary least squares regression (OLS) regression model is constructed to explore the influence of these three value factors on the prevention and control of COVID-19 and some heterogeneity analysis is implemented. The results show that religious belief and individual freedom significantly increased the COVID-19 infection rate, while government satisfaction significantly reduced the COVID-19 infection rate. The study findings have the ability to hold up after a range of robustness. For countries and regions with different COVID-19 testing policies, the influence of national values is different. Only in countries and regions with high testing rate policies and complete systems of the prevention and control of COVID-19, the influence of national values is significant. Based on these findings, a series of targeted policy recommendations for building national values in the post-epidemic era are proposed.
ABSTRACT
Integrating informatization into the circulation industry has led to the concept of circulation-industry intelligence. By reducing transportation costs and increasing total factor productivity, the incomes of rural-area residents can be improved;a new pattern of regional economy can be established;urban, rural, social, and economic development can become more coordinated;and social sustainable development can be promoted. In this study, we used China’s provincial panel data corresponding to the 2007–2019 period to measure the intelligence index of the circulation industry in each region and determine the factors that affect the urban–rural income gap;thereafter, we conducted comparative analyses. Further, a fixed-effects model was established based on the theory of agglomeration and diffusion effects to analyze the relationship between these two variables. Our analysis identified innovation investment as a significant intermediary mechanism. The robustness of this finding was verified by substituting variables and controlling for endogeneity. Thus, the effect was shown to be regionally heterogeneous. This study innovatively integrated informatization into the circulation industry, and the results obtained provide a reference for formulating transportation infrastructure as well as informatization strategies for promoting urban–rural coordination and sustainable development globally.
ABSTRACT
This paper uses the unbalanced panel data of 156 countries during the period of 2002 to 2018 to explore the possible impact of government efficiency on health outcomes. Firstly, we used the fixed-effect model to examine the relationship between government efficiency and health outcomes and found that the increase in government efficiency can significantly improve health outcomes. Then, a series of robustness checks were carried out, which confirmed the reliability of the above result. Thirdly, this paper conducted a heterogeneity analysis from the perspective of life cycle. Fourthly, this paper investigated the mechanisms of the impact of government efficiency on health outcomes from the perspectives of economic growth, health innovation, education and corruption control. Finally, this paper studied the moderating effects of the ruling party's ideology and democracy on the relationship between government efficiency and health outcomes. The findings of this study provide some references for governments to improve health outcomes.
Subject(s)
Economic Development , Government , China , Efficiency , Outcome Assessment, Health Care , Reproducibility of ResultsABSTRACT
Studying the influence of weather conditions on the COVID-19 epidemic is an emerging field. However, existing studies in this area tend to utilize time-series data, which have certain limitations and fail to consider individual, social, and economic factors. Therefore, this study aimed to fill this gap. In this paper, we explored the influence of weather conditions on the COVID-19 epidemic using COVID-19-related prefecture-daily panel data collected in mainland China between January 1, 2020, and February 19, 2020. A two-way fixed effect model was applied taking into account factors including public health measures, effective distance to Wuhan, population density, economic development level, health, and medical conditions. We also used a piecewise linear regression to determine the relationship in detail. We found that there is a conditional negative relationship between weather conditions and the epidemic. Each 1 °C rise in mean temperature led to a 0.49% increase in the confirmed cases growth rate when mean temperature was above -7 °C. Similarly, when the relative humidity was greater than 46%, it was negatively correlated with the epidemic, where a 1% increase in relative humidity decreased the rate of confirmed cases by 0.19%. Furthermore, prefecture-level administrative regions, such as Chifeng (included as "warning cities") have more days of "dangerous weather", which is favorable for outbreaks. In addition, we found that the impact of mean temperature is greatest in the east, the influence of relative humidity is most pronounced in the central region, and the significance of weather conditions is more important in the coastal region. Finally, we found that rising diurnal temperatures decreased the negative impact of weather conditions on the spread of COVID-19. We also observed that strict public health measures and high social concern can mitigate the adverse effects of cold and dry weather on the spread of the epidemic. To the best of our knowledge, this is the first study which applies the two-way fixed effect model to investigate the influence of weather conditions on the COVID-19 epidemic, takes into account socio-economic factors and draws new conclusions.