CC BY
81
Вестник Челябинского государственного университета. 2019. № 9 (431). Экономические науки. Вып. 66. С. 100—105.
УДК 339.9 DOI 10.24411/1994-2796-2019-10911
ББК 65.9 (5Ирн); 65.495
INVESTIGATING THE RELATIONSHIP BETWEEN STATE HEALTH EXPENDITURE ALLOCATION AND ECONOMIC GROWTH
(CASE STUDY: IRAN)
A. Seifi, H. Makhmali2, S. Motaghi
1 Allameh Tabataba'i University, Tehran, Iran 2 Payame Noor University
The present study aims to study the relationship between state health expenditure and economic growth of Iran during 2003-2018 seasonally using a descriptive-analytical approach and applying techniques related to vector self-regression models. Accordingly, in order to answer the research questions in the short term and long term, the VAR and VECM methods were used together with the co-integration and Wald tests (to test the coefficients' significance). State health expenditures in the long run and in the short run are affected by the fluctuations in GDP. On the other hand, if the state health expenditure fluctuates, GDP in the short run will be affected by these expenditures. In the long run, this will lead to the disappearance of the effect and the return of GDP to equilibrium and the main trend.
Keywords: Health Expenditure, Economic Growth, Iran, VECM.
1. Introduction
Economic growth (the steady increase in a country's GDP) has always been one of the key ideals of governments and an indicator of their performance evaluation. Achieving economic growth, above all, lies in a proper understanding of the potential routes and factors affecting it. The most important factor influencing this macro index was physical capital and technological advancements prior to the 1960s. With the emergence of new and endogenous growth models and the failure of traditional growth models due to the downward return of physical capital, human capital has been proposed by Becker (1964), Chadwick (1965) and Mincer (1974) as an influential factor on economic growth. It was entered into production function by Menquio, Roemer & Well (1992) as an agent, along with other factors, paving the way for the entrance of health and its expenditures into the economic growth by Grossman (1972), Fogel (1991), and Nulles and Owen (1995).
According to these theories, improving health in a number of ways, including reducing sick days off due to illness, increasing productivity, access to better job opportunities with higher incomes, and extending workers and employees' lives, reduces production losses due to absence and illness of the workforce and physical and mental inefficiency, ultimately resulting in economic growth (Grossman, 1972a). Since human health is a reserve of capital that is depreciated over time and the disease causes abnormal depreciation, investment in the treatment and health compensates for this depreciation and any investment in this sector will contribute to the growth and development of countries (Finally, 2007: 30).
In addition, limited investment in the health sector (low health costs) will increase the death rate and reduce the return on investment in human capital. This negative effect will first be revealed on the profitability of investing in health and, later, in the whole economy, and the closed-loop growth of human capital investment will result in slowing economic growth (Lee, Lee & Chiu, 2013). Also, the heavy burden of disease and its multilateral effects on production capacity, demographics, education, etc. play a prominent role in the chronic poverty of less developed countries and slow down the growth (Asefzade, 2010).
Accordingly, Fogel has shown the relationship between physical dimensions of body and nutrition with long-term labor productivity and economic growth, stating that one-third of French and British economic growth in the last 200 years has been due to improved nutrition and health (Fogel, 1997, 1991). Rivara and Quariz demonstrated the strong statistical relationship between health spending and economic growth in their research (Wanat, 2016). In a study on World Bank, Jami Sen found that health spending accounted for 11% of economic growth in selected countries and Baltaji and Muskein showed the strong links between the 20 OECD countries and economic growth (Muye, 2016).
But the important point in this regard is that health can be seen as an indicator of human development and thus of economic growth and development, which should be provided in societies and all members of society should enjoy it. This cannot be achieved unless governments invest in this sector. This is because, on the one hand, planning and policy-making for social welfare in order to guarantee financial access to indi-
vidual and public health services are fundamental requirements for achieving community health and, on the other, governments' intervention in treatment and community health is essential because of market failure in financing and providing health services by private sector (such as information asymmetry) or due to instability in health markets such as health insurance (including selection of appropriate risk by insurers and ethical risk) as well as to reduce inequality and prevent poverty caused by the miserable costs of treatment (Catlin et al., 2007).
Accordingly, scholars such as Elmi and Sadeghi (2012) demonstrated the significant impact of state health expenditure on economic growth for developed countries from 1990 to 2009, Hassan (2012) for Pakistan from 1972 to 2009, Giorgio (2013) for OECD countries, Lee, Eng, Chiang and Kaplan (2016) for China, South Korea, and Taiwan, and Lee, Lee, and Chiu (2017) for 38 developed and developing countries.
Based on what was said, the present study aims at investigating the causal relationship between state health expenditure and economic growth in Iran by examining the state health spending in Iran. Statistical models and econometric analysis are used to analyze this relationship in different time periods (short and long terms) in order to clarify the role and presence of government in Iranian health sector by explaining the effectiveness of government health sector policies. Thus, decision-makers in various areas of the health sector and politicians in the country can achieve the desired economic growth that is also applicable to other developing countries (like Iran). Therefore, the present research questions will be as follows:
But the important point in this regard is whether the impact of health spending on economic growth can be verified for all countries and regions at different times, or it is different depending on the extent of the participation of governments in the health sector in different countries. Also, is this causal relationship (the impact of state health expenditure on economic growth) unilateral or bilateral? In other words, will economic growth lead to improved health spending?
2. Research methodology
For this research, the Vector Auto-regression (VAR) model, which is widely used today, has been selected. In fact, in traditional concurrent methods, the variables are first divided into two endogenous and exogenous categories and a series of constraints, especially the zero constraints should be considered on the structural equation coefficients by default in order to estimate structural coefficients. But in vector
autocorrelation models, the variables are defined as a function of their lagged values and other variables, as well as random components of e t. None of the coefficient matrices are presumed to be equal to zero; in other words, zero constraints are not imposed on the model coefficients. Although pure exogenous variables can be included in the VAR model, there is no context for optional division of endogenous and exogenous variables, as is the case in traditional concurrent methods. The degree of lagging that determines the dynamics of the model is determined by criteria such as Akaike, Schwarz- Bayesian, Hannan-Quinn, and the maximum value of the Log-Likelihood function. The reason for choosing this model for this research is that it determines the long-run relationship between variables and on the other hand, is able to explain short-run relationships between variables. The VAR relation is in line with the short-run and longrun relationship between variables. The reasons why the VAR model was used in this study are as follows:
1. The causal relationships can be studied using time series of this system in any particular economy. This approach is very useful for macroeconomics and some other surveys in Third World countries that lack coherent economics theory (such as advanced and market-based economies), and thus, can identify key variables in that particular economy and develop the obtained theory in this economy.
2. Another useful application of VAR systems is the study of the timing of economic shocks.
The first step in developing a VAR model is to select the appropriate variables. The variables used in the present study are as follows (Tab. 1):
Table 1
Research variables
Variable Symbol
1 Gross Domestic Production GDP
2 Incurred Government health expenses INCO
3 Ratio of financial intermediaries' credits to private sector to GDP PRIGDP
4 The ratio of total exports and imports to GDP XMGDP
Source: World Bank / Central bank.
All time-series information is annual and seasonal for the period of 2003-2018. Data are extracted from the Central Bank and the Iranian Health Insurance Organization. Eviews 10 software was used for statistical analysis and econometric methods. Before estimating the VAR model, it is necessary to first evaluate whether all the variables used in the research are stationary or not. Then, the number of optimal lags is determined based on valid criteria.
2.1. Evaluation of model variables stationary state One of the major problems of researches is not considering variables stationary state and their change over time as an effective factor in regression analysis. Stationary state of data prevents false regression between variables. Therefore, before performing any analysis to ensure that they are not fictitious and then yield reliable results, it should be tested whether the variables are stationary or not. Also, their coin-tegration degree should be tested. In this study, the Augmented Dickey Fuller (ADF) test was used. In this test, the null hypothesis is that the variable is stationary. Therefore, the computational statistic must be lower than the ADF test statistic to confirm that the variables are stationary. The result of this test for all the variables indicates that all variables become stationary after a one-time difference and thus, the data are valid. According to the results of the Eviwes10 software output, Tab. 2 presents the data stationary report.
2.2. Optimal lag determination Since it is very important to determine optimal lags in the cointegration test, it is necessary to determine the optimal lags in an appropriate way. To be more precise, it is best to determine the optimal number of lags with the appropriate cointegration test. Here, the optimal lags are determined by combining different test results. Table 3 shows the optimal lag test. According to the Akaike criterion (ALC), the optimal number of lags in this study is 8.
2.3. Johansson Cointegration Test (Matrix and Maximum Eigenvalues Statistics)
In the maximum eigenvalue test, "the null hypothesis of the lack of cointegration relationship r = 0 versus one cointegration r = 1," "existence of one cointegration relationship versus two cointegration relationships r = 2," and "existence of two cointegration relationships versus three cointegration relationships" are tested. In the test of effect, "the hypothesis of lack of cointegration relationship versus cointegration relationship," "existence of one or less cointegration relationship versus more than one cointegration relationships," and "existence of two or less cointegration relationships versus more than two cointegration relationships" are tested. If the test statistics for these variables exceeds the critical values at the 5% level, the null hypothesis is rejected. This gives the number of cointegration vectors.
The values of the \ m and test statistics are now
max trace
used to determine the number of cointegration vectors. As shown in Tab. 4 and 5, the existence of one cointegration vector is confirmed based on the matrix statistics and the maximum eigenvalue statistics. Cointegration test confirms the existence of at least one cointegration vector between the research variables. Therefore, the desired communication pattern can be estimated using VECM method.
Cointegration test with intercept and trend and 8 lags confirm the existence of three cointegration vectors among gross domestic production, incurred government health expenses, ratio of financial intermediaries' credits to private sector to GDP, and the ratio of total exports and imports to GDP. Therefore, it is possible to estimate the ap-
Table 2
Augmented Dickey Fuller (ADF) test results
Without differentiation After a one-time differentiation
Variable ADF ADF critical value at 5% State ADF ADF critical value at 5% State
LGDP -0.610400 -3.500495 Not stationary -3.567994 -3.500495 Stationary
LNINCO -0.109105 -3.513075 Not stationary -12.32754 -3.515523 Stationary
LXMGDP -3.244630 -3.495295 Not stationary -5.185434 -3.495295 Stationary
LPRIGDP -0.368253 -3.493692 Not stationary -6.320775 -3.495295 Stationary
Table 3
Optimal Lags Test
AIC FPE LR Lag
-3.918665 6.81e-05 - 0
-5.353384 1.63e-05 65.95391 1
-5.832386 1.01e-05 25.46370 2
-6.164863 7.28e-06 18.73645 3
-6.930761 3.41e-06 32.89338 4
-7.188447 2.67e-06 14.24277 5
-7.092332 2.99e-06 2.411722 6
-7.028861 3.27e-06 3.202820 7
-7.370462* 2.50e-06* 13.90206* 8
-6.988292 3.51e-06 3.600302 9
-7.296192 2.83e-06 2.291805 10
Source: research finding.
propriate communication model using the VECM method, the results of which will be presented in the next section.
2.4. Short-term and long-term dynamics
This section presents the results of estimating the impact of GDP fluctuations on state health expenditure in the health insurance organization and its reversal pattern. According to the results of the cointegration test, the VECM model is estimated with 8 lags. The results of significant and effective coefficients are presented in Tab. 6. Now, using the results, the short-term dynamics known as error correction patterns can be examined. The error correction model (ECM) relates the short-term fluctuations of variables to their long-run values.
Confirmation of the probability level of the ECM coefficient plus its negative coefficient mean that there is a long-term causality between the GDP variable and the government health expenditure dependent variable. The coefficient of error correction factor also indicates that in each period, a few percent of the short-term imbalance of state health expenditure in the health insurance organization is adjusted to achieve long-term equilibrium. Or, in other words, this coefficient indicates that it takes some time for
the state health expenditure in the health insurance organization to return to its long-term trend when a declining or rising shock is imposed on the country's economy.
The results of the error correction model here show that the error correction term coefficient (ECM) is 39%. The high numerical value of this figure indicates that the adjustment rate is too high to achieve long-term equilibrium. That is, the effects of a change in GDP over two and a half time periods are eliminated from state health expenditure, and state health expenditure reacts to price shocks to GDP. Wald test is now used to investigate the short-term impact coefficients and their causality, the results of which are shown in Tab. 7.
In the Tab. 7 C (40) to C (47) are the coefficients of the variables d (lgdp (-1)) to d (lgdp (-8)). The above results show that the short-run effect of GDP fluctuations on state health expenditure is significant (non-zero), equal to 0.3014 and 0.32 and statistically significant at 1°% confidence level. The findings of the study suggest that state health expenditure responds to both long-term and short-term economic growth fluctuations. The results of the inverse model (the effect of state
Table 4
Values of the ^trace test statistics to determine the number of cointegration vectors with 8 lags
Ho Hi Matrix statistics Critical values (0.05)
R - 1 R > 1 377.0588 0.0001
R < 1 R > 2 128.7075 0.0000
R < 2 R > 3 31.84994 0.0001
R < 3 R > 4 0.657212 0.4175
Source: research findings.
Table 5
Values of the Xmax test statistics to determine the number of cointegration vectors with 8 lags
Ho Hi Matrix statistics Critical values (0.05)
R - 1 R > 1 248.3514 0.0001
R < 1 R > 2 96.85753 0.0000
R < 2 R > 3 31.19273 0.0001
R < 3 R > 4 0.657212 0.4175
Source: research findings.
Table 6
Results of estimating the impact of GDP fluctuations on state health expenditure
Variable Estimated coefficient Prob. Level t-statistics SD
Ecm(-1) -0.396569 1.108147 0.357867 0.0272
D(LGDP(-1)) -0.301414 0.772121 -0.390372 0.0037
D(LGDP(-2)) -0.320007 0.665504 -0.480849 0.0400
D(LNINCO(-1)) 0.353266 0.461022 0.766266 0.0096
D(LNINCO(-3)) 0.407541 0.335785 1.213696 0.0003
D(LXMGDP(-1)) -1.524964 0.556613 -2.739721 0.0192
Source: research findings.
Table 7
Wald test results for investigating the causality of short-term coefficients
H0 Chi-square Prob. Level Result
C(40) = C(41) = C(42) = C(43) = = C(44) = C(45) = C(46) = C(47) = 0 15.447355 0.0006 Rejected Significant effect of short-term coefficients
health expenditure fluctuations on GDP) are shown in Tab. 8.
Confirmation of the probability level of the ECM coefficient plus its positive coefficient mean that there is no long-term causality between the government health expenditure independent variable and the GDP dependent variable. This result means that GDP will not be affected by fluctuations in health sector spending in the long term and the effects of changing state health expenditure will be quickly eliminated from GDP. Also, long-term GDP does not significantly respond to government spending shocks in the long term. There was no significant response. Wald test is now used to investigate the short-term impact factors and their causality, the results of which are shown in Tab. 9.
In the Tab. 9 C (12) to C (19) are the coefficients of the variables d (lninco (-1)) to d (lninco (-8)). The above results show that the short-run effect of state health expenditure fluctuations on GDP is significant (non-zero), equal to 4, 67, 67, 70, and 70 and statistically significant at 1°% confidence level. The findings of the study suggest that economic growth in the long run does not significantly respond to state health expenditure fluctuations. However, it significantly responds to state health expenditure fluctuations in the short run.
3. Conclusion
The present study is based on an analytical-descriptive approach using the VAR and VECM method to investigate the relationship between economic growth fluctuations, on the one hand, and state health expend-
iture, on the other. In this regard, it was attempted to study the relationship between government health expenditure and economic growth and the impact of economic growth on improving government health expenditure (alternative investment index in health sector by government) in Iran through the study of theoretical bases and history of activities in this regard. The research was done during the seasonal period of2003:1 to 2018:4 using the data of World Bank and Central Bank of Iran. The results of the present study showed that:
State health spending in Iran shows a significant reaction to short-term and long-term GDP fluctuations, which means that any fluctuation in economic growth in Iran leads to significant changes in health expenditures. On the other hand, Iran's economic growth in the short run also shows a significant response to fluctuations in state health expenditure, but this effect is not significant in the long run, which means allocating more state health expenditure in the short run has a positive effect on Iran's economic growth. But in the long run, it will be quickly eliminated from GDP. Accordingly, it is not possible to give a definite opinion about the impact of health spending on economic growth in Iran. However, since this relationship is significant in the short run, health policy makers and government budget providers are suggested to take aligning health spending with economic growth into account in their decisions and negotiations and mitigate the crises of GDP behavior using its optimistic effects.
Table 8
Results of estimating the impact of state health expenditure shocks on GDP
Variable Estimated coefficient Prob. Level t-statistics SD
Ecm(-2) 1.382356 0.341590 4.046830 0.0019
D(LGDP(-1)) 1.257812 0.393968 3.192677 0.0086
D(LGDP(-4)) 0.741909 0.264229 2.807828 0.0170
D(LNINCO(-1)) -0.046979 0.235233 -4.450821 0.0010
D(LNINCO(-2)) -0.679170 0.182660 -3.718223 0.0034
D(LNINCO(-3)) -0.674527 0.171331 -3.936975 0.0023
D(LNINCO(-4)) -0.701104 0.182309 -3.845684 0.0027
D(LNINCO(-5)) -0.709253 0.212948 -3.330644 0.0067
D(LXMGDP(-2)) 1.199251 0.438162 2.737004 0.0193
D(LXMGDP(-3)) 0.856046 0.330552 2.589750 0.0251
D(LXMGDP(-6)) 3.226962 0.766365 4.210737 0.0015
D(LXMGDP(-8)) 3.200104 0.781115 4.096842 0.0018
D(LPRIGDP(-1)) 0.754142 0.159224 4.736354 0.0006
D(LPRIGDP(-2)) 0.928608 0.216984 4.279624 0.0013
D(LPRIGDP(-3)) 0.849033 0.200216 4.240588 0.0014
D(LPRIGDP(-4)) 0.475149 0.138593 3.428381 0.0056
Source: research findings.
H0 Chi-square Prob. Level Result
C(12) = C(13) = C(14) = C(15) = = C(16) = C(17) = C(18) = C(19) = 0 30.36366 0.0002 Rejected Significant effect of short-term coefficients
Table 9
Wald test results for investigating the causality of short-term coefficients
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Сведения об авторах
Anahita Seifi — Assistant Professor of social science, Allameh Tabataba'i University, Tehran, Iran. a.seifi@atu.ac.ir
Hassan Makhmali — Assistant Professor of Economics, Payame Noor University. H.Makhmali@gmail.com
Samira Motaghi — Assistant Professor of Economics, Payame Noor University. samira.motaghi@gmail.com
