The magnitude of the coronavirus disease (COVID-19) pandemic has an enormous impact on the social life and the economic activities in almost every country in the world. Besides the biological and epidemiological factors, a multitude of social and economic criteria also govern the extent of the coronavirus disease spread in the population. Consequently, there is an active debate regarding the critical socio-economic determinants that contribute to the impact of the resulting pandemic. In this paper, we contribute towards the resolution of the debate by leveraging Bayesian model averaging techniques and country level data to investigate the potential of 30 determinants, describing a diverse set of socio-economic characteristics, in explaining the outcome of the first wave of the coronavirus pandemic. We show that the true empirical model behind the coronavirus outcome is constituted only of few determinants, but the extent to which each determinant is able to provide a credible explanation varies between countries due to their heterogeneous socio-economic characteristics. To understand the relationship between the potential determinants in the specification of the true model, we develop the coronavirus determinants Jointness space. The obtained map acts as a bridge between theoretical investigations and empirical observations, and offers an alternate view for the joint importance of the socio-economic determinants when used for developing policies aimed at preventing future epidemic crises.
翻译:科罗纳病毒(COVID-19)大流行对世界上几乎每个国家的社会生活和经济活动产生了巨大影响,除了生物和流行病学因素外,还有众多的社会和经济标准制约着人口中传播的科罗纳病毒疾病的范围,因此,对促成由此产生的流行病影响的关键社会经济决定因素进行了积极辩论。在本文件中,我们利用巴耶西亚平均模型和国家一级数据,帮助解决辩论,利用巴耶西亚平均技术和国家一级数据来调查30个决定因素的潜力,描述一套不同的社会经济特点,解释科罗纳病毒流行病第一波的结果。我们表明,科罗纳病毒结果背后的真正经验模型只是几个决定因素,但每个决定因素在多大程度上能够提供因各国社会经济特点不一而异的可靠解释。为了了解真实模型规格中的潜在决定因素之间的关系,我们开发了科罗纳病毒决定因素联合空间。获得的地图作为理论调查与实验性观测结果之间的桥梁,为未来发展社会经济政策的共同重要性提供了替代的决定因素。