摘要
目的研究我国居民医疗保健消费和医药产业发展的动态关系,并在此基础上提出政策建议。方法采用1995—2017年的人均医疗消费支出和医药产业销售收入的时间序列数据构建向量自回归模型,运用脉冲响应函数和方差分解等方法对两者之间的关系进行实证分析。结果居民医疗保健消费与医药产业发展之间存在双向的格兰杰因果关系;医药产业的发展前期对居民的医疗保健支出有负向影响,但后期变为正向影响;反过来医疗保健消费短期内对医药产业发展有负向影响,但长期有明显而波动的正向影响。结论为促进消费升级和医药产业发展,需要建立医疗保健消费需求供给耦合机制,激发居民医疗保健消费潜力,提高医药产业核心竞争力,改善医疗保健消费环境。
Objective To study the dynamic relationship between Chinese residents'health care consumption and the development of pharmaceutical industry,and to put forward policy suggestions on this basis.Methods The time series data of per capita medical consumption expenditure and pharmaceutical industry sales income from 1995 to 2017 were used to construct the vector autoregressive(VAR)model,and the impulse response function and variance decomposition were used to analyze the relationship between them.Results There was a two-way Granger causality between the per capita health care consumption expenditure and the pharmaceutical industry;the development of the pharmaceutical industry had a negative impact on the residents'health care expenditure in the early stage,but turned into a positive impact later.Conversely,the health care consumption had a negative impact on the development of the pharmaceutical industry in the short term.However,there was a significant but fluctuating positive impact in the long term.Conclusion In order to promote consumption upgrading and pharmaceutical industry adjustment,it is necessary to establish the coupling mechanism of demand and supply,stimulate the potential of residents'health care consumption,enhance the core competitiveness of pharmaceutical industry,and improve the health care consumption environment.
作者
雒敏
黄亚新
LUO Min;HUANG Yaxin(School of Public Health Policy and Management Nanjing Medical University,Nanjing 211166,China;Department of Personnel, the First Affiliated Hospital of Nanjing Medical University,Nanjing 210029,China)
出处
《医药导报》
CAS
北大核心
2022年第5期742-747,共6页
Herald of Medicine
基金
江苏省高校哲学社会科学研究项目(2020SJA0308)
江苏省研究生科研与实践创新计划(SJCX20-0473)。
关键词
医疗保健消费
医药产业
向量自回归模型
脉冲响应函数
Health care consumption
Pharmaceutical industry
Vector autoregressive model
Impulse response function