时间：2022年6月29日（周三） 15:00

地点：腾讯会议  会议号：911-634-271

主讲人：韩晓祎 副教授

题目：Policy Effectiveness on the Global Covid-19 Pandemic and Unemployment Outcomes: A Large Mixed Frequency Spatial Approach

主讲人简介：韩晓祎，2014年获美国俄亥俄州立大学经济学博士，现为厦门大学王亚南经济研究院与经济学院长聘副教授、博士生导师，主要研究领域为计量经济学、应用计量经济学、区域经济学和劳动经济学。多篇论文发表在PNAS、Journal of Business & Economic Statistics、Econometric Theory和Regional Science and Urban Economics、《数量经济技术经济研究》等国内外权威学术期刊上，长期为国内外近二十余种经济学、统计学、计算科学、区域科学和环境科学等领域权威学术期刊担任评审。主持国家自然科学基金面上项目、青年项目各1项。

摘要：We propose a mixed frequency spatial VAR (MF-SVAR) modeling framework to measure the effectiveness of policies conditional on the spillover and diffusion effects of the global pandemic and unemployment. We study the effects of two aspects of policy effectiveness, namely policy start date and policy timeliness, from a spatio-temporal perspective. The spatial panel data contain weekly new case growth rates and monthly unemployment rate changes for 68 countries across six continents at mixed frequencies from January 2020 to August 2021. We find that government policies have a significant impact on the growth of new cases, but only a marginal effect on the change in unemployment rates. A policy’s start date is critical for its effectiveness. In terms of both immediate impact on the near term and total impact over the following four weeks, starting a policy in the 4th week of a month is most effective at reducing the growth of new cases. At the same time, starting in the 2nd or 3rd week is counterproductive for a one-time policy start date. In addition, our estimates suggest that the spillover and diffusion effects are much stronger than a country’s temporal effect during a global pandemic, both for new case growth and changes in unemployment. We also find that new case growth influences changes in unemployment, but not vice versa. Counterfactual experiments provide further evidence of policy effectiveness in various scenarios and also reveal the main risk-vulnerable and risk-spillover countries.