International School of Business & Finance, Sun Yat-sen University, Guangzhou, China.
Department of Econometrics and Business Statistics, Monash University, Clayton, VIC, Australia.
Health Econ. 2018 Dec;27(12):1921-1944. doi: 10.1002/hec.3811. Epub 2018 Jul 26.
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.
本文考察了不同决定因素对人均医疗保健支出的影响在不同分位数上的变化。研究使用固定效应动态面板模型的工具变量分位数回归方法，对 1990 年至 2012 年间的 28 个经济合作与发展组织国家进行了研究。结果表明，人均医疗保健支出增长率的决定因素，包括滞后医疗支出增长率、人均国内生产总值（GDP）增长率、医师密度、老年人口增长率、预期寿命增长率、城市化率和女性劳动力参与率，随着医疗保健支出增长率的条件分布而变化，而变化模式则不同。此外，我们表明，鲍莫尔的“非均衡增长”模型对人均医疗支出增长率有显著的正向影响，而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（工资增长率和劳动生产率增长率）与医疗支出增长率之间的相关性则更为多样。相比之下，仅滞后医疗支出增长率、人均 GDP 和鲍莫尔变量（或其组成部分）在条件均值回归中与医疗支出增长率相关。分位数回归动态面板工具变量模型和线性面板数据模型的预测结果也有很大差异。在医疗支出研究中，需要更加关注决定因素的变化影响。
