Abstract
This paper examines health care expenditure (HCE) disparities between the European Union countries over the period 1995–2010. By means of using a continuous version of the distribution dynamics approach, the key conclusions are that the reduction in disparities is very weak and, therefore, persistence is the main characteristic of the HCE distribution. In view of these findings, a preliminary attempt is made to add some insights into potentially main factors behind the HCE distribution. The results indicate that whereas per capita income is by far the main determinant, the dependency ratio and female labour participation do not play any role in explaining the HCE distribution; as for the rest of the factors studied (life expectancy, infant mortality, R&D expenditure and public HCE expenditure share), we find that their role falls somewhat in between.
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Notes
In the same line, the empirical literature on the determinants of HCE, pioneered by Newhouse (1977), mainly considers four groups of determinants: income, demographic, heterogeneity of health care systems, and technological progress related variables.
Although in a different context, Meijer et al. (2013) also stress the fact that, with reference to health care expenditure, we need not only to account for its growth “but also explain changes in its distribution” (p. 88).
Quantile regressions, sometimes used to study convergence/divergence, are somewhat better suited than conventional regresssion methods to use the information for the whole distribution. However, they do it to a much lesser extent than the distribution dynamics approach.
Although we are well aware that OECD Health Data is the largest available source of statistics to compare OECD health care systems, here we have opted for the National Health Accounts database of the WHO because the OECD database does not provide information for some EU countries (Bulgaria, Cyprus, Latvia, Lithuania, Malta and Romania).
The PPPs are given in national currency units (NCU) per US dollar.
Another important and recent paper on this issue, devoted to Indian States, is Apergis and Padhi (2013). Other papers have also studied the issue of convergence/divergence across regions/states of a country but from the point of view of health outcomes. Montero-Granados and Dios Jimenez (2007), for the Spanish case, and Gächter and Theurl (2011), for the Austrian one, are among the most relevant.
As stressed by Hartwig (2008), “the share of current health expenditure (HCE) in the gross domestic product (GDP) rises rapidly in virtually all developed nations” (p. 603).
The bandwidth election gives a trade-off between bias and variance. Small bandwidths produce small bias and large variance, while large bandwidths yield large bias and small variance.
The “plateau” between around 30 and 130 % of the average is “taller” in 2010 than in the other selected years.
We refer the reader to Maza et al. (2010) for technical details regarding the main differences between the traditional and Hyndman continuous approaches.
These authors proposed a three-steps strategy for bandwidth selection: first, bandwidth selection with the traditional rule suggested by Silverman (1986); second, a bootstrap bandwidth selection approach for estimating conditional distribution functions (Hall et al. 1999); third, a regression-based bandwidth selector (Fan et al. 1996).
Then, we have 16 years \(\times \) 27 countries = 432 observations.
Although Wilson (1999) has argued for the need to count with “a formal theory to explain or predict the per capita medical care expenditure of a nation” (p. 160) and Hartwig (2008) has intended to meet this demand by revisiting Baumol’s model of unbalanced growth, it happens that, as stated by Hoffmeyer and McCarthy (1994) “there is just one, very clear, very well-established statistical fact relating to health expenditure care: its correlation with GDP. No other robust and stable correlation has yet been found” (p. 67).
However, it is necessary to stress that there is no consensus on this point in health economics literature. As rightly pointed out by an anonymous referee, there are doubts about whether a link exists, the signs of the coefficients, and even the direction the link runs.
We use total R&D data because there are no homogeneous data on health R&D spending for all the countries included in our sample.
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Acknowledgments
We gratefully acknowledge an anonymous referee and the Editor (P. P. Barros) for comments and suggestions on an earlier version of the paper.
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Villaverde, J., Maza, A. & Hierro, M. Health care expenditure disparities in the European Union and underlying factors: a distribution dynamics approach. Int J Health Care Finance Econ 14, 251–268 (2014). https://doi.org/10.1007/s10754-014-9147-8
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DOI: https://doi.org/10.1007/s10754-014-9147-8