数字技术与经济金融学术午餐会（第27期）

摘要：

Using iterative experiments to uncover causal links between critical policy details and outcomes helps to optimize policy design. This paper studies a large-scale staged fiscal stimulus program conducted during the COVID-19 pandemic, in which a provincial government in China disbursed digital coupons to 8.4 million individual accounts in consecutive waves and updated the program design each time. We find that ruling out unproductive program features leads to a pattern of increasing treatment effects over the waves and that program design matters more than the size of the fiscal stimulus in boosting spending. Our results show that (i) general coupons with no constraints on where the vouchers can be redeemed are more effective than specialized coupons in stimulating consumption in the targeted sectors; (ii) coupon packets with fewer denominations and shorter redemption windows tend to be more effective; and (iii) low-income residents and non-local residents are equally or even more responsive to the coupon program than other groups. Our results illustrate that generating variations in iterative policy experiments, combined with a timely assessment of individuals’ responses to marginal incentives, optimizes program design.

主讲人介绍：

王腾慧，北京大学光华管理学院金融学博士，香港大学博士后研究员。主要研究方向为实证公司金融、发展经济学、ESG，曾在Management Science, Accounting and Business Research,《金融学季刊》发表学术论文，参与中国消费券、住房公积金改革等多项政策试点研究，学术研究成果曾入选FMA、AsianFA、CFRIC等国际金融学术会议。