Government Subsidies and Policies for Mask Production Under COVID-19: Is It Wise to Control Less?

补贴 公共经济学 业务 经济盈余 政府(语言学) 社会福利 经济干预主义 经济 福利 市场经济 政治学 语言学 政治 哲学 法学
作者
Xiaoyan Xu,Tsan‐Ming Choi,S.H. Chung,Bin Shen
出处
期刊:IEEE Transactions on Engineering Management [Institute of Electrical and Electronics Engineers]
卷期号:71: 3172-3188 被引量:11
标识
DOI:10.1109/tem.2022.3198101
摘要

Under the COVID-19 pandemic, governments worldwide have subsidized manufacturers or consumers on the production or purchase of masks. However, the impacts of these subsidies on the mask supply chain (MSC) operations are unclear. Motivated by our interview with a mask manufacturer as well as the observed real-world practices, we establish consumer utility-based stylized models to analytically examine government subsidies and policies in the MSC. We utilize the infection transmission model to capture the social health risk during the COVID-19 outbreak. The government aims to maximize social welfare, which includes the manufacturer's profit, consumer surplus, social health risk, and government subsidy expenditure. Results indicate that when the price is not controlled (i.e., the manufacturer decides it), the manufacturer and consumer subsidy programs are equally efficient in enhancing consumer surplus as well as reducing harm to social health risk under COVID-19. Thus, the government can conduct a subsidy scheme that is easier to implement in practice. However, we surprisingly find that the government's excessive intervention will cause disequilibrium in the MSC. When the price or the manufacturer's dishonest behavior is fully controlled by the government, subsidizing the MSC is not always advisable. Besides, our findings are consistent with the public interest theory; that is, the proper implementation of dishonesty prevention and pricing control policies can improve social welfare but sacrifice consumer surplus. Our results contribute to healthcare operations management and generate managerial insights for MSC management during COVID-19 with industrial validation.
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