دانلود رایگان مقاله عوامل موثر بر مخارج بهداشتی در ایالات متحده

Abstract

Using 1960–2012 annual time-series data for modelling, we apply the Autoregressive Distributed Lag Cointegration (ARDL) approach, to identify some major drivers of per capita real U.S. health spending. The ARDL Bounds testing procedure (Pesaran et al., 1999; 2001) has several econometric advantages compared with the standard Johansen and Juselius cointegration method. One distinguishing feature of this ARDL Bounds testing procedure is its ability to estimate the long-run economic relationship without requiring pre-testing the time-series for the presence of unit roots in the data generating process incorporated in the cointegration model. The empirical findings in this study indicate that per capita real income (INCOME), the population percent above 65 years (AGE) and the level of health care technology (HRD), measured as the level of Research & Development expenditure in health care are cointegrated. INCOME, AGE and HRD exert positive effects on U.S. health expenditure per capita. Unlike prior studies, this paper presents new empirical evidence indicating that the U.S. health care is a necessity, with an income elasticity estimate of around 0.92. We also find that medical technology advances play a major role in the long run rise of the U.S. health expenditure. We discuss implications of these findings.

5. Conclusions and policy implications

This paper is the first study to apply the Autoregressive Distributed Lag (ARDL) approach developed by Pesaran and Shin (1999), Pesaran et al., 2001) to empirically identify some major drivers of U.S. health expenditure (HEXP) during the period 1960–2012. By extending the U.S. healthcare expenditure model and time-series data length estimated by Okunade and Murthy (2002) and using a novel and simple cointegration procedure, our paper demonstrates that the major drivers of health expenditure are income (INCOME), health research and development expenditure (HRD), and the percentage of population that is above 65 years of age and older (AGE). Furthermore, the paper econometrically determines that although all the variables used in the study have experienced structural breaks, they are non-stationary in levels and stationary in first-differences. Hence, they are integrated of the order one I (1). However, together they form a long-run link exhibiting a cointegrating relationship among HEXP, INCOME, HRD and AGE. Our estimated ARDL cointegration model passed all of the diagnostic econometric tests, besides yielding stable coefficients. It is interesting to compare findings of our ARDL model with those of studies from other countries using the same procedure. However, it is interesting to note here that none of the studies reviewed (see, Appendix Table 1) are of higher dimension. However, a direct comparison of our study findings with those from other structurally different countries is challenging, as they used time-series data of varying lengths and estimated ARDL models with lower dimensions. Nevertheless, previous ARDL models of health care expenditure and income (or economic growth) together with one or more other variable(s) include Nasiru and Usman (2012) for Nigeria, Halicioglu (2013) for Finland, and Khandelwal (2015) for India. Moreover, similar to our current study finding for the U.S. (a high-income economy), Chaabouni and Abednnadher (2014) found Tunisia (a middle-income economy) health care to be a technical necessity, although the implications of this finding would differ for the U.S. While previous studies found the U.S. health care to be a luxury good, the current study findings indicate that the statistically significant estimate of the income elasticity of health expenditure is less than unity; therefore, healthcare is presumed to be a necessity. The aggregate U.S. health care as a necessity justifies arguments for more inevitable government intervention in subsidizing insurance premiums of the underinsured (especially, for small business operators and their employees) and expanding access of the indigent population groups and the elderly to the redesigned Medicaid and Medicaid public insurance programs, expected in the near future. The findings of this paper that the observed income elasticity of demand for health care is less than one has the important implication that additional factors are driving the recent increases in the U.S. health expenditure. Therefore, in this paper, we have furnished evidence to support‘the march of science’ hypothesis in health care that continues as an important long run driver (on both the supply and demand sides) of the escalating healthcare costs. Moreover, it is possible that economic growth and the prevalence of relatively liberal insurance, including Medicare and Medicaid, facilitated the role of medical technology in health care. Another core driver of health expenditure is population ageing. Improving the Medicare program, increasing health-literacy among the old by providing health-related information, and facilitating preventive health care can be efficient strategies for containing elderly health care costs.