题    目：The Effect of AI on Marketing Employment: Evidence from 110m+ Online Employment Records

嘉　宾：朱睿智 副教授 中国科学技术大学

主持人：曾智宇 助理教授 上海交通大学安泰经济与管理学院

时　间：2026年4月28日（周二）14:00-15:30

地　点：安泰楼A507室

内容简介：

Marketing is widely considered one of the job functions most exposed to generative AI, but large-scale evidence on marketing-specific employment effects remains limited. We study how the public release of ChatGPT in November 2022 affected marketing employment using a panel of over 110 million employment records from Revelio Labs covering 164,780 firms. Comparing marketing to non-marketing employees within the same firm in a difference-in-differences design, we find that marketing is significantly more affected than non-marketing employees by AI, with marketing headcount decreasing by 0.94 percent more than non-marketing headcount after ChatGPT's release. We find that this relative decline in marketing headcount is strongest among junior and senior employees, while mid-level marketing employees are least (relatively) affected, as compared to same-seniority non-marketing employees. We also show that this is driven by a relative decline in non-digital marketing in particular; that customer-service headcount and non-sales marketing headcount decline relatively more than sales headcount; and is even higher when restricting to only-US firms. Lastly, we show in triple-differences analyses that this relative decline in marketing headcount is significantly higher in firms and industries with higher pre-period AI exposure.

演讲人简介：

Ruizhi Zhu is an non-tenured Associate Professor of Marketing at the Faculty of Business for Science and Technology, School of Management, University of Science and Technology of China. He received his Ph.D. in Economics from the University of Toronto in 2023 and was previously a Postdoctoral Research Associate in Marketing at Northeastern University. His research broadly examines privacy, the platform economy, branding, and the economic impact of AI. His methodological approach integrates analytical modeling, causal inference, and machine learning. His work has been published in Marketing Science.

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