市场营销学系学术讲座

时   间：2024年5月14日（周二）13:30-15:00

地   点：思源教授楼624室

主   题：Price Transparency in Healthcare: Understanding the Impacts of the Price Disclosure Policy in Maine

主讲人：张玲玲，中欧国际工商学院市场营销学助理教授

主持人：庄雷，复旦大学管理学院青年副研究员

摘   要：

This research examines the effects of the Price Transparency Regulation (PTR) in healthcare. The theoretical framework of our model explores how prices influence consumer choices on the demand side and how competition affects pricing decisions on the supply side. We utilize the launch of a price transparency website in Maine as an exogenous shock and construct a Difference-in-Differences (DID) model to identify the causal effects of PTR. Our findings indicate that PTR has significantly altered the price elasticity of demand and the pricing negotiations dynamics. Notably, PTR increased the elasticity of demand, leading patients to choose less expensive providers and reducing market share concentration for procedures with price disclosure. This shift resulted in generally lower total payments for these procedures compared to procedures without price disclosure. On the supply side, larger insurers leveraged the price information more effectively than smaller ones, achieving lower payments to hospitals. The effects of PTR also varied by hospital, with those attracting stronger patient demand able to negotiate higher payments. Our results have important policy implications. On one hand, they suggest that the outcomes of the PTR policy in Maine align with its intended objectives, at least broadly. On the other hand, the uneven benefits among market participants indicate that policymakers should consider measures to level the playing field, ensuring that smaller and newer entities are not disadvantaged.

简   介：

Prof. Zhang Lingling is an Assistant Professor of Marketing at CEIBS. Prior to joining CEIBS, she was an Assistant Professor of Marketing at the Robert H. Smith School of Business, University of Maryland. Her research interests focus on digital marketing, healthcare marketing, and AI-powered consumer insights. Trained as an empirical modeler, she uses large-scale field data to understand market competition and consumer behavior. Her research utilises various modelling methods, including econometrics, machine learning, causal inference, and industrial organization. Her papers focused on price bargaining on platforms and resource allocation across multiple channels have been published in Marketing Science, MIS Quarterly, and Decision Support Systems. Her recent projects combine machine learning and causal inference to examine consumer behavior and provide implications for firms and policy makers. Her recent research also investigates how AI and data analytics can help improve service quality in the home care market for the seniors.

市场营销学系

2024-5-8