讲座主题：Minimum Performance Targets, Multitasking, and Incentives: Theory and Evidence from China's Air Quality Controls

主讲嘉宾：翁翕，北京大学光华管理学院应用经济学系长聘教授

讲座时间：2021年5月13日（周四），下午15:00-16:30

讲座地点：学院11号楼308会议室

嘉宾简介：翁翕，现为北京大学光华管理学院应用经济学系长聘教授。北京大学经济学学士、硕士，美国宾夕法尼亚大学经济学博士。研究兴趣主要研究领域为信息经济学、组织经济学和行为经济学。研究成果发表或即将发表于国外顶级学术期刊，如Journal of Finance, Management Science, Economic Journal, American Economic Journal: Microeconomics, Journal of Economic Theory (两篇), International Economic Review (两篇), Economic Theory, Journal of Economic Behavior & Organization, 和Journal of Economics & Management Strategy等国际知名期刊。

主持国家自然科学基金面上项目“组织经济学理论与应用”。曾获奖项有：2020第八届高等学校科学研究优秀成果奖（人文社会科学）青年成果奖，2020中国信息经济学优秀成果奖，2019光华管理学院第十三届厉以宁科研奖，2019北京大学教学优秀奖，2017中国信息经济学青年创新奖，2017北京大学教学优秀奖，2017第十三届北京大学人文社会科学研究优秀成果一等奖，2016北京大学北京银行奖教金，2016中国信息经济学乌家培奖等。

内容摘要：This paper examines how local Chinese officials respond strategically to minimum air quality control targets when they care more about pursuing regional economic development, which is closely linked to their career prospects. Using a novel prefecture-day level dataset on air quality and applying a regression discontinuity design, we find strong evidence that air quality tends to improve when the air quality target is doomed to fail, but deteriorates significantly after the early fulfillment of the target is guaranteed. These "asymmetric'' strategic responses are mainly driven by "outsiders'' – local officials with no previous exposure to the regions to which they are assigned. Greater pressure to promote local economic development reinforces outsiders’ asymmetric responses. For "non-outsiders'' who have been promoted from the local area and who are more likely to intrinsically value the local environment, air quality performance is stable in both cases of target fulfillment. We build a simple theoretical model to rationalize these key findings. Our study sheds light on how minimum air quality targets have functioned in China's context and highlights the role of intrinsic motivations in mitigating strategic responses to minimum performance targets in a multitasking environment.