A quantile regression approach to panel data analysis of health-care expenditure in Organisation for Economic Co-operation and Development countries
A quantile regression approach to panel data analysis of health-care expenditure in Organisation for Economic Co-operation and Development countries
复制标题
经济合作与发展组织国家医疗保健支出面板数据分析的分位数回归方法
DOI：
10.1002/hec.3811
复制
发表时间：
2018-12-01
期刊：
影响因子：
2.1
通讯作者：
Yang, Ke
中科院分区：
文献类型：
--
作者：
Tian, Fengping;Gao, Jiti;Yang, Ke
本文研究了各种决定因素对人均医疗保健支出的影响的变化。 1990年至2012年期间，总共对28个经济合作与发展组织国家进行了研究，采用工具变量分位数回归方法构建具有固定效应的动态面板模型。研究结果表明，人均医疗卫生支出增长的决定因素，包括滞后卫生支出增长、人均国内生产总值增长、医生密度增长、老年人口增长、预期寿命增长、城镇化增长和女性劳动力参与率等，随着医疗卫生支出增长的条件分布而变化，但变化模式不同。此外，我们还表明，鲍莫尔的“不平衡增长”模型对人均医疗支出增长具有显着的正向影响，并且其影响在整个分布上相当稳定。然而，“鲍莫尔变量”的组成部分（工资增长和劳动生产率增长）与卫生支出增长之间的相关性则更为多样。作为比较，在条件均值回归中，只有滞后的医疗支出、人均 GDP 和 Baumol 变量（或其组成部分）的增长与医疗支出增长相关。分位数回归动态面板工具变量模型与线性面板数据模型的预测结果也存在较大差异。卫生支出研究中需要更多地关注决定因素的不同影响。
This paper investigates the variation in the effects of various determinants on the per capita health-care expenditure. A total of 28 Organisation for Economic Co-operation and Development countries are studied over the period 1990-2012, employing an instrumental variable quantile regression method for a dynamic panel model with fixed effects. The results show that the determinants of per capita health-care expenditure growth, involving the growth of lagged health spending, of per capita gross domestic product (GDP), of physician density, of elderly population, of life expectancy, of urbanization, and of female labor force participation, do vary with the conditional distribution of the health-care expenditure growth, while the changing patterns are dissimilar. Moreover, we show that Baumol's model of "unbalanced growth" has a significantly positive effect on per capita health spending growth, and its effect is quite stable over the entire distribution. However, the correlation between the components (wage growth and labor productivity growth) of the "Baumol variable" and health expenditure growth is more varied. As a comparison, only the growth of lagged health spending, per capita GDP, and the Baumol variable (or its components) are found related to health spending growth in conditional mean regressions. The prediction results were also quite different between the quantile regression dynamic panel instrumental variable models and linear panel data models. More attention needs to be paid to the varying influence of determinants in health expenditure study.