Title: Selection Bias in Urban-Rural Resident Medical Insurance Enrollment and Its Welfare Analysis

Speaker: Professor Feng Jin, Distinguished Professor, Ph.D. Advisor, and Head of the Department of Public Economics, Fudan University

Moderator: Professor Liu Hong, Department of Social Security, School of Labor and Human Resources, Renmin University of China

Date and Time: Thursday, October 31, 2024, 13:00 - 14:30

Location: Room 347, Qiushi Building

About the Speaker:
Professor Feng Jin is a distinguished professor and Ph.D. advisor at Fudan University, where she serves as Head of the Department of Public Economics. Recognized as a “Changjiang Scholar” by China’s Ministry of Education and included in the Ministry’s “New Century Talent Support Program,” Professor Feng’s main research fields include social security, health economics, and labor economics. She has published over 120 papers in peer-reviewed journals both domestically and internationally, including Economic Research Journal, Management World, Social Sciences in China, Journal of Economic Behavior and Organization, Journal of Comparative Economics, Social Science and Medicine, and World Development. Her notable works include The Economics of Social Insurance, Sustainable Pension Levels, and The Economics of Pension Insurance.

Lecture Abstract:
In recent years, the enrollment rate in China’s resident medical insurance program has declined. To analyze residents' enrollment behavior, this study constructs a utility-based model and utilizes large-scale micro-survey data to examine whether inefficiencies exist in enrollment choices and to estimate the resulting welfare losses. The study further investigates heterogeneity across variables such as age, income, health, and risk preference. In response to these issues, the State Council issued the Guiding Opinion on Establishing a Long-Term Mechanism for Basic Medical Insurance Enrollment in August 2024. This presentation will also simulate several policy proposals outlined in this directive, examining their potential impacts on enrollment choices and assessing their welfare implications for residents.