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Salam Jabbar L, Sadeghi S K. (2025). Investigating the Long-Run Impact of Public Health Expenditures on Preventable Mortality in the Provinces of Iran: An ARDL Approach. refahj. 25(96), : 2 doi:10.32598/refahj.25.96.4521.1
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Extended Abstract
Introduction
The health sector is one of the most important areas of all countries in the world, which plays an essencial role in the process of economic development. Therefore, financing this sector is very important for the government and private sector. In developing countries, such as Iran, due to the poorer economy, productivity, and health conditions, and the low ability of people to pay out of pocket, government health expenditures are very important. In addition, due to reasons such as market failure, inequality and income redistribution issues, and catastrophic health costs, public health expenditures are more important than private sector health expenditures.
Therefore, in many developing countries, including Iran, it seems necessary to increase public spending on social services such as health status, although this increase by itself is not enough to guarantee an increase in health service supply. Public health expenditures focus on public health programs and services based on disease prevention, as well as health monitoring, in order to delay death and avoid disease complications. On the one hand, increasing public health expenditures leads to improving health status and huma capital and reducing housholds’ catastrophic health spendigs and. On the other hand, it can have negative consequences such as increasing government budget deficit. In addition, the significant differences in the trend and speed of health expenditures in different countries, as well as the different and sometimes contradictory effects that public health expenditures have on macroeconomic indicators and health status of different countries, make it necessary to investigate public health expenditures consequences more and more.
Therefore, due to the importance of health status, calculating different health indicators is done comprehensively and systematically in all countries of the world. One of the most important indicators of health status is preventable mortality, which can largely indicate the effectiveness of healthcare sector. However, this index is still poorly understood. Accordingly, it seems necessary to examine the financing efficiency and its effect on important health indicators such as preventsble mortality. Iran’s health system has faced a sharp increase in health spending. World Bank data reveal significant growth in Iran’s health financing indicators between 2004-2020. The health expenditure-to-GDP ratio increased from 5.4% to 7.5%, while government health spending as a percentage of total public expenditure more than doubled from 10.16% to 22.54% during this period. Notably, per capita health expenditures rose markedly from 142 to142to573 (constant USD), outpacing overall economic growth rates. This disproportionate expansion of health investments relative to macroeconomic indicators underscores the critical need to evaluate their effectiveness in improving population health outcomes. To address this research imperative, the study examines the long-term impact of public health expenditures on preventable mortality across Iran’s provinces from 1388-1401 (2009-2022).
Method
In order to specify the model, this study uses Autoregressive Distributed Lag (ARDL) panel data model. The reason for applying this approach is its permissive features, one of which is that if some of the variables used in the model have unit roots, and are atationary by one-time differencing, The ARDL method can be used. Therefore, the ARDL approach allows some variables to be of order I(0) and some other variables to be of order I(1). The ARDL approach has three different methods which are Mean Group (MG), Dynamic Fixed Effects (DFE), and Pooled Mean Group (PMG). In MG estimator, first a separate regression is estimated for each group, and then the average long-run coefficients are calculated. Therefore, the short-run and long-run coefficients estimated in MG method can be heterogeneous. In DFE estimator, all short-run and long-run coefficients are estimated separately for each section. In the DFE estimator, the short-run coefficients are homogeneous.
PMG is an intermediate estimator that, like MG estimator, allows the intercept and short-run coefficients to differ between groups, while, like DFE estimator, emposes equality of the long-run coefficients between groups. In order to choose the ritht method among these, Housman test is used. The variables used in this study include Preventable mortality (dependent variable), public health expenditures, gross domestic production (GDP) per capita, the ratio of population aged 65 and over, the ratio of the working population over 25 years old, the density of physicians, the proportion of population under 15 years old. The data of the provinces have been collected from the database of Statistical Center of Iran.
Findings
The results of Hausman test show that PMG method provides a more efficient estimation than the other two methods. The results of the PMG estimator indicate that in the long run, public health expenditure has a negative and significant effect on preventable mortality in the provinces of Iran. This result is consistent with previous studies such as Ammi et al. (2024). In addition, based on the results, economic growth has a negative and significant effect on preventable mortality in Iran, and this result is consistent with studies like Ivankova etal. (2022) and Fathollahi (2022). Moreover, consistent with some studies like Daraganic and Wangen (2023), physician density has a negative and significant impact on the dependent variable in the long run. The results of this study also show that unemployment rate of people over 25 years old has a negative and significant effect on preventable mortality in the long run, which is compatible with the studies such as Ruhm (2000), Evans and Graham (1988), Ruhm and Black (2002), and Freeman (1999). It is worth mentioning that the error correction term of the ECM model in this study is equal to -0.60. It means the existence of long-term cointegration and confirms that the specification of the model is correct.
Discussion
According to the results of this study, the following policy recommendations are presented:
In order to reduce preventable mortality and improve the health system, it is suggested that the government increase the budget of the health sector in different provinces.
According to the effect of economic growth, adopting policies aimed at increasing GDP and improving the income level in the provinces can have a positive effect on the health status of the society.
Considering the negative effect of physician density on preventable mortality, government policymakers can help reduce preventable mortality through policies to increase the number of physicians, especially in the areas that have less physician density.
Ethical Considerations
Authors’ Participation
All authors have participated in the preparation of this article with informed consent.
Funding
No direct financial support was received from any institution or organization for the preparation of this article.
Conflict of Interest
There was no conflict of interest between the authors in this article.
Compliance with the principles of research ethics: The authors have complied all the ethical points, especially the non-manipulation and distortion of data in this article.
Draganic, D., and Wangen, K. R. (2023). The Efect of Physician Density on Colorectal Cancer Stage at Diagnosis: Causal Inference Methods for Spatial Data Applied on Regional-Level Data. International Journal of Health Geographics, Vol. 22 (1), https://doi.org/10.1186/s12942-023-00323-w.
Evans, W., and Graham, J. D. (1988). Traffic Safety and the Business Cycle. Alcohol, Drugs, and Driving, Vol. 4 (1), pp. 31-38.
Fathollahi, E. (2022). The Long-Run Effect of Health Expenditures on Health Status in the Provinces of Iran: Empirical Evaluation Using Panel Data Analysis. https://civilica.com/doc/1551737. (In Persian).
Ivankova, V., Gavurova, B., and Khouri, S. (2022). Understanding the Relationships between Health Spending, Treatable Mortality and Economic Productivity in OECD Countries. Public Health, 10:1036058. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2022.1036058/full
Ruhm, C. J. (2000). Are Recessions Good for Your Health? Quarterly Journal of Economics, Vol. 115 (2), pp. 617-650. https://doi.org/10.1162/003355300554872
Ruhm, C. J., and Black, W. E. (2002). Does Drinking Really Decrease in Bad Times? The Journal of Health Economics, Vol. 21 (4), pp. 659-678. https://www.sciencedirect.com/science/article/abs/pii/S0167629602000334
Introduction
The health sector is one of the most important areas of all countries in the world, which plays an essencial role in the process of economic development. Therefore, financing this sector is very important for the government and private sector. In developing countries, such as Iran, due to the poorer economy, productivity, and health conditions, and the low ability of people to pay out of pocket, government health expenditures are very important. In addition, due to reasons such as market failure, inequality and income redistribution issues, and catastrophic health costs, public health expenditures are more important than private sector health expenditures.
Therefore, in many developing countries, including Iran, it seems necessary to increase public spending on social services such as health status, although this increase by itself is not enough to guarantee an increase in health service supply. Public health expenditures focus on public health programs and services based on disease prevention, as well as health monitoring, in order to delay death and avoid disease complications. On the one hand, increasing public health expenditures leads to improving health status and huma capital and reducing housholds’ catastrophic health spendigs and. On the other hand, it can have negative consequences such as increasing government budget deficit. In addition, the significant differences in the trend and speed of health expenditures in different countries, as well as the different and sometimes contradictory effects that public health expenditures have on macroeconomic indicators and health status of different countries, make it necessary to investigate public health expenditures consequences more and more.
Therefore, due to the importance of health status, calculating different health indicators is done comprehensively and systematically in all countries of the world. One of the most important indicators of health status is preventable mortality, which can largely indicate the effectiveness of healthcare sector. However, this index is still poorly understood. Accordingly, it seems necessary to examine the financing efficiency and its effect on important health indicators such as preventsble mortality. Iran’s health system has faced a sharp increase in health spending. World Bank data reveal significant growth in Iran’s health financing indicators between 2004-2020. The health expenditure-to-GDP ratio increased from 5.4% to 7.5%, while government health spending as a percentage of total public expenditure more than doubled from 10.16% to 22.54% during this period. Notably, per capita health expenditures rose markedly from 142 to142to573 (constant USD), outpacing overall economic growth rates. This disproportionate expansion of health investments relative to macroeconomic indicators underscores the critical need to evaluate their effectiveness in improving population health outcomes. To address this research imperative, the study examines the long-term impact of public health expenditures on preventable mortality across Iran’s provinces from 1388-1401 (2009-2022).
Method
In order to specify the model, this study uses Autoregressive Distributed Lag (ARDL) panel data model. The reason for applying this approach is its permissive features, one of which is that if some of the variables used in the model have unit roots, and are atationary by one-time differencing, The ARDL method can be used. Therefore, the ARDL approach allows some variables to be of order I(0) and some other variables to be of order I(1). The ARDL approach has three different methods which are Mean Group (MG), Dynamic Fixed Effects (DFE), and Pooled Mean Group (PMG). In MG estimator, first a separate regression is estimated for each group, and then the average long-run coefficients are calculated. Therefore, the short-run and long-run coefficients estimated in MG method can be heterogeneous. In DFE estimator, all short-run and long-run coefficients are estimated separately for each section. In the DFE estimator, the short-run coefficients are homogeneous.
PMG is an intermediate estimator that, like MG estimator, allows the intercept and short-run coefficients to differ between groups, while, like DFE estimator, emposes equality of the long-run coefficients between groups. In order to choose the ritht method among these, Housman test is used. The variables used in this study include Preventable mortality (dependent variable), public health expenditures, gross domestic production (GDP) per capita, the ratio of population aged 65 and over, the ratio of the working population over 25 years old, the density of physicians, the proportion of population under 15 years old. The data of the provinces have been collected from the database of Statistical Center of Iran.
Findings
The results of Hausman test show that PMG method provides a more efficient estimation than the other two methods. The results of the PMG estimator indicate that in the long run, public health expenditure has a negative and significant effect on preventable mortality in the provinces of Iran. This result is consistent with previous studies such as Ammi et al. (2024). In addition, based on the results, economic growth has a negative and significant effect on preventable mortality in Iran, and this result is consistent with studies like Ivankova etal. (2022) and Fathollahi (2022). Moreover, consistent with some studies like Daraganic and Wangen (2023), physician density has a negative and significant impact on the dependent variable in the long run. The results of this study also show that unemployment rate of people over 25 years old has a negative and significant effect on preventable mortality in the long run, which is compatible with the studies such as Ruhm (2000), Evans and Graham (1988), Ruhm and Black (2002), and Freeman (1999). It is worth mentioning that the error correction term of the ECM model in this study is equal to -0.60. It means the existence of long-term cointegration and confirms that the specification of the model is correct.
Discussion
According to the results of this study, the following policy recommendations are presented:
In order to reduce preventable mortality and improve the health system, it is suggested that the government increase the budget of the health sector in different provinces.
According to the effect of economic growth, adopting policies aimed at increasing GDP and improving the income level in the provinces can have a positive effect on the health status of the society.
Considering the negative effect of physician density on preventable mortality, government policymakers can help reduce preventable mortality through policies to increase the number of physicians, especially in the areas that have less physician density.
Ethical Considerations
Authors’ Participation
All authors have participated in the preparation of this article with informed consent.
Funding
No direct financial support was received from any institution or organization for the preparation of this article.
Conflict of Interest
There was no conflict of interest between the authors in this article.
Compliance with the principles of research ethics: The authors have complied all the ethical points, especially the non-manipulation and distortion of data in this article.
Draganic, D., and Wangen, K. R. (2023). The Efect of Physician Density on Colorectal Cancer Stage at Diagnosis: Causal Inference Methods for Spatial Data Applied on Regional-Level Data. International Journal of Health Geographics, Vol. 22 (1), https://doi.org/10.1186/s12942-023-00323-w.
Evans, W., and Graham, J. D. (1988). Traffic Safety and the Business Cycle. Alcohol, Drugs, and Driving, Vol. 4 (1), pp. 31-38.
Fathollahi, E. (2022). The Long-Run Effect of Health Expenditures on Health Status in the Provinces of Iran: Empirical Evaluation Using Panel Data Analysis. https://civilica.com/doc/1551737. (In Persian).
Ivankova, V., Gavurova, B., and Khouri, S. (2022). Understanding the Relationships between Health Spending, Treatable Mortality and Economic Productivity in OECD Countries. Public Health, 10:1036058. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2022.1036058/full
Ruhm, C. J. (2000). Are Recessions Good for Your Health? Quarterly Journal of Economics, Vol. 115 (2), pp. 617-650. https://doi.org/10.1162/003355300554872
Ruhm, C. J., and Black, W. E. (2002). Does Drinking Really Decrease in Bad Times? The Journal of Health Economics, Vol. 21 (4), pp. 659-678. https://www.sciencedirect.com/science/article/abs/pii/S0167629602000334
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Received: 2024/04/17 | Accepted: 2024/09/1 | Published: 2025/04/4
Received: 2024/04/17 | Accepted: 2024/09/1 | Published: 2025/04/4
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