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Delivering universal health coverage for an aging population: An analysis of the Chinese rural health insurance program

Wenhui Zhou, Yanfang Zheng, Alec Morton and Feng Luo

China Economic Review, 2018, vol. 52, issue C, 204-224

Abstract: There is now high level international commitment to the goal of universal health coverage. But how can countries make this a reality in the face of a limited budget and an aging population? Since 2008, China has been rolling out an ambitious reform program, which aims to achieve affordable health insurance coverage for all Chinese citizens. Under this reform program, Chinese living in rural areas are eligible to enroll in a subsidized scheme called the New Cooperative Medical System (NCMS). Using a three stage game model involving a government, a private fund manager and population, we explore the impact of population aging on NCMS. Our model highlights the role of government regulation and subsidy in ensuring operation efficiency of the system. We show that at optimality the government sets the operating framework for the fund manager to constrain the potential for monopoly profits. The Government subsidizes the scheme to prevent an adverse selection death spiral. However, the effectiveness of the subsidy in achieving this goal is moderated by the age structure of the population. Our model gives insights into the strengths of the NCMS framework and also can be used to support decisions about resource allocation and understand how scheme dynamics may unfold as the Chinese population ages further.

Keywords: Cooperative medical scheme; Premium subsidy; Contract (search for similar items in EconPapers)
JEL-codes: C70 I13 I18 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chieco:v:52:y:2018:i:c:p:204-224

DOI: 10.1016/j.chieco.2018.07.007

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China Economic Review is currently edited by B.M. Fleisher, K. X. D. Huang, M.E. Lovely, Y. Wen, X. Zhang and X. Zhu

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