This study used a panel data set, which is including 15 OECD countries that have high income per capita for the time period of 1995-2011. Following causality and autoregressive distributed lag (ARDL), paper yields: 1) respectively the largest and the smallest impacts on health expenditures are caused by public spending and the influences of Age Dependency Ratio Young (ADRY); 2) income and Age Dependency Ratio Old (ADRO) on health expenditures are positive; 3) another striking inference is that while young working population rate is increasing, health expenditure is decreasing.
The development of modern economic growth literature begins with the research title, “A Contribution to the theory of economic growth” of Solow [
Neoclassical growth theories suggest that the long-run rate of growth is determined by the rate of change of intangible technology. Furthermore, this change in technology is independent from the savings and the investment rate of the economy. Human capital is commonly thought to be the knowledge and the skills that are embodied in the labor force. The general well-being of the labor force, concerning health and nutrition, is also sometimes considered as a part of the human capital. Human capital accumulation depends on the fraction of total savings per worker that is allocated to education, job training and health, etc. [
Schultz [
The rest of this article will be monitored as follows: Section 2 discusses the relationship between human capital, health expenditure and economic growth. Then the estimation results of the econometric analysis will be included in section 3. Finally in section 4, conclusions take place.
In recent decades, health expenditure in the OECD countries differs considerably over time and across countries. In the other words, the ratio of health spending to GDP has been increased in OECD countries. For example, in USA health expenditures—GDP ratio is 13.59 until 1995, and it became 17.85 in 2011, according to the statistics of WHO (World Health Organization). In the same period, these rates are increased for developed countries (For example Netherlands (8.32 - 11.95).
The policy implication of the effect of human capital investment and health expenditure on economic growth has been an important subject of academic research, in recent decades. Since the studies of Kleiman (1974) and Newhouse (1977), income has been identified as the most important factor, explaining differences across countries in the level and growth of health care expenditure [
Various studies have explored the performance of health expenditure in time series and panel data. Most of the studies are based on a simple relationship between health expenditure and economic growth. Important examples include Baltagi and Moscone [
This study used a panel data set, which includes 15 OECD countries that had high income per capita, for the period 1995-2011 (Austria, Belgium, Denmark, Finland, France, Germany, Italy, Luxembourg, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland and United Kingdom). There are two reasons for selecting these countries. Firstly these countries form a group of high-income countries. The other reason, according to the statistics of WHO [
Description of the variables used, such as health expenditure, national income, population and age structure, is shown in
The economy policies show their effect on the applications of the macro and micro variables, in a delay of a specific time period. For example, the impact of an investment made today, in the health sector, shows its effect in future periods. Therefore in this study, ARDL method, which is a co-integration technique and introduced by Pesaran and Shin [
The analysis becomes more complicated when the variables are difference-stationary, or integrated of order 1 (I(1)). The recent literature on co-integration is concerned with the analysis of the long-run relations between (I(1)) variables. Consequently, a large number of alternative estimations and hypotheses on testing procedures have been specifically developed for the analysis of (I(1)) variables [
where
It is needed to determine the order of integration before using co-integration techniques. For this aim; Levin, Lin & Chi (LLC), Im, Pesaran and Shin (IPS), ADF Fisher Chi-square (ADF Fisher) AND PP-Fisher unit root tests are used in the paper. Panel unit root tests have been developed on the similar manner that underlie conventional ADF test.
It is obvious from the ADF results that, some of the data sets are integrated of (I(0)) or (I(1)). The unit root test results of individual effect indicate that, LHE, LY, ADRO and LGE series are (I(1)), ADRY series are (I(0)) in
In our study, the model of Baltagi and Moscone [
According to the causality results which are provided in the table 3; while there is no causality to the variable
In this study we employed PMG estimation introduced by Pesaran, Shin and Smith [
autoregressive distributive lag (ARDL) can be represents equation 1; in equation 1
dependent variable (LHE),
LGE) for group.
MG estimation method proposed by Pesaran and Smith [
MG estimator seems not to be valid but
significant in SR and LR period. According to PMG in LR while all co-efficients are statistically significant, ADRO and ADRY only in the short term are meaningless. This study highlights several implications in LR. At first, respectively the largest and the smallest impact on health expenditures is caused by public spending
come and ADRO on health expenditures is positive
when the young working population
situation is adverse for the age dependency ratio old
health expenditures. The demographic change will have an explicit influence on health policies in the future.
This study used a panel data set, which includes 15 OECD countries which had high income per capita, for the
Variables | Description |
---|---|
LHE | Logarithm of Health Expenditure per Capita |
LY | Logarithm of GDP per Capita |
ADRO | Age Dependency Ratio, Old (% of Working Age Population) |
ADRY | Age Dependency Ratio, Young (% of Working Age Population) |
LGE | Logarithm of General Government Final Consumption |
Individual Intercept | ||||
---|---|---|---|---|
LLC | IPS | ADF Fisher | PP-Fisher | |
LHE | −3.4015 (0.0003) | 1.7600 (0.9608) | 14.3959 (0.9927) | 20.5466 (0.9015) |
LY | −6.7574 (0.0000) | −2.5400 (0.0055) | 48.3752 (0.0182) | 74.9286 (0.0000) |
ADRO | −0.8565 (0.1959) | 0.6446 (0.7404) | 81.0329 (0.0000) | 20.6911 (0.8973) |
ADRY | −20.2487(0.0000) | −27.2769 (0.0000) | 232.590 (0.0000) | 83.9225 (0.0000) |
LGE | −5.4892 (0.0000) | 0.5595 (0.7121) | 28.2008 (0.5598) | 42.4360 (0.0656) |
−1.5224 (0.0639) | −2.2097 (0.0136) | 45.3447 (0.0358) | 83.0441 (0.000) | |
−6.2180 (0.0000) | −3.1361 (0.0009) | 53.6936 (0.0050) | 75.9320 (0.0000) | |
−4.8418 (0.0000) | −4.4227 (0.0000) | 100.890 (0.0000) | 8.0994 (1.0000) | |
−9.2499 (0.0000) | −4.3374 (0.0000) | 87.8882 (0.0000) | 68.4171 (0.0000) | |
−1.4138 (0.0787) | −2.4874 (0.0064) | 55.2328 (0.0033) | 83.1283 (0.0000) | |
Individual Intercept and Trend | ||||
LHE | 3.4618 (0.9997) | 1.98380 (0.9764) | 18.9048 (0.9420) | 9.5295 (0.9999) |
LY | −2.4113 (0.0079) | 1.4735 (0.9297) | 19.4546 (0.9300) | 7.8585 (1.0000) |
ADRO | −7.6865 (0.0000) | −7.3390 (0.0000) | 139.631 (0.0000) | 7.2918 (1.0000) |
ADRY | −16.4703 (0.0000) | −7.3958 (0.0000) | 116.251 (0.0000) | 72.4029 (0.0000) |
LGE | 2.0645 (0.9805) | 2.3090 (0.9895) | 20.3599 (0.9068) | 11.7933 (0.9988) |
−2.4042 (0.0081) | −0.4707 (0.3189) | 32.8224 (0.3303) | 66.9182 (0.0001) | |
−8.0592 (0.0000) | −3.9904 (0.0000) | 64.9759 (0.0002) | 113.367 (0.0000) | |
−12.7757 (0.0000) | −10.5698 (0.0000) | 110.165 (0.0000) | 3.3232 (1.0000) | |
−7.5380 (0.0000) | −7.4602 (0.0000) | 78.3868 (0.0000) | 48.6058 (0.0172) | |
−2.9817 (0.0014) | −1.9436 (0.0260) | 52.2095 (0.0072) | 66.8027 (0.0001) |
Short-Run Causality | Long-Run Causality | ||
---|---|---|---|
ECT | |||
0.9417 (0.6245) | 5.36E−05 (1.1E−05) | ||
13.0111 (0.0015) | −0.26147 (0.0291) |
Variables | MG Estimation | PMG Estimation | Hausman Test | |
---|---|---|---|---|
LR | LY | 2.4045 (0.022) | 0.7762 (0.000) | |
ADRO | −0.5329 (0.224) | 0.4751 (0.000) | ||
ADRY | 0.8852 (0.218) | −0.0165 (0.000) | ||
LGE | 9.7306 (0.127) | 1.7269 (0.000) | ||
SR | ECT | −1.3179 (0.000) | −0.9668 (0.000) | |
−2.6683 (0.048) 0.7891 (0.129) | −0.9662 (0.000) 0.3045 (0.032) | |||
0.3553 (0.680) −0.029 (0.986) | −0.1814 (0.818) 0.1288 (0.636) | |||
0.8847 (0.363) −2.6742 (0.024) | −0.0737 (0.384) −0.4021 (0.075) | |||
LGE | −1.9269 ((0.01) | −0.8513 (0.002) |
period of 1995-2011. Applications in the economy of macro and micro variables can be influenced by lagged values from policies. For example, the impact of an investment made today in the health sector exist in future periods. Therefore this paper was used to analyze lagged values of ARDL cointegration method that was proposed by Pesaran, Shin and Smith [
This study highlights several implications in LR. At first, respectively the largest and the smallest impact on health expenditures is caused by public spending and age dependency ratio: young. Another result influence of income and ADRO on health expenditures is positive. Another striking inference is that when the young working population rate increases, health expenditure is decreasing. This situation is the opposite for age dependency ratio: old. In OECD countries, the share of young population ratio is low. Thus instead of increasing the share of health expenditures, demographic structure rejuvenation policy would be a better choice in the long run.