Mining and quantitative evaluation of COVID-19 policy tools in China

Abstract

Policy quantitative analysis can effectively evaluate the government’s response to COVID-19 emergency management effect, and provide reference for the government to formulate follow-up policies. The content mining method is used to explore the 301 COVID-19 policies issued by the Central government of China since the outbreak of the epidemic in a multi-dimensional manner and comprehensively analyze the characteristics of epidemic prevention policies. Then, based on policy evaluation theory and data fusion theory, a COVID-19 policy evaluation model based on PMC-AE is established to evaluate quantitatively eight representative COVID-19 policy texts. The results show that: Firstly, China’s COVID-19 policies are mainly aimed at providing economic support to enterprises and individuals affected by the epidemic, issued by 49 departments, and include 32.7 percent supply-level and 28.5 percent demand-level, and 25.8 percent environment-level. In addition, strategy-level policies accounted for at least 13 percent. Secondly, according to the principle of openness, authority, relevance and normative principle, eight COVID-19 policies are evaluated by PMC-AE model. Four policies are level Ⅰ policies, three policies are level Ⅱ policies and one policy is level Ⅲ policies. The reason for its low score is mainly affected by four indexes: policy evaluation, incentive measures, policy emphasis and policy receptor. To sum up, China has taken both non-structural and structural measures to prevent and control the epidemic. The introduction of specific epidemic prevention and control policy has realized complex intervention in the whole process of epidemic prevention and control.