https://orcid.org/0000-0002-1387-4642
Figures
Abstract
Affordable health care is often a result of increased government spending on the health sector. Out-of-pocket expenses remain the primary health care funding source in many South Asian nations. Lack of adequate public funding for health services, difficulty in obtaining health insurance, and high out-of-pocket costs can result in indebtedness, reductions in actual consumption, and decreased access to health care services. The study examines life expectancy and health care spending in South Asian countries. The life expectancy of South Asian countries is studied as a health outcome in relation to health care spending, gross national income per capita, and expected years of schooling. This study is based on secondary data from World Bank statistics that covers eight South Asian countries from 2000 to 2021, for a total of 176 pooled time series and cross-sectional observations. The data were analysed using econometric models, including the cross sectional dependency test, panel unit root test, panel co-integration test, vector error correction model, pair-wise Granger causality test, and Wald test statistics. The vector error correction model results indicate that health care spending, anticipated years of schooling, and gross national income per capita have a long-run association with health outcomes. Health care spending, per capita gross national income, and expected years of education have all greatly enhanced life expectancy in South Asian countries. An effective health strategy is necessary to increase people’s healthy life expectancy and well-being. To accomplish this, government may need to promote school enrolment to help people become more health literate and aware of their health outcomes. As a result, persons with more years of schooling have better health, higher levels of well-being, healthier habits, and ultimately, a longer life expectancy. This study will provide an important insight to policymakers in improving health outcomes through targeted and sustained health care spending in South Asia.
Citation: Dhungana BR, Singh JK, Dhungana S (2024) Life expectancy and health care spending in South Asia: An econometric analysis. PLoS ONE 19(12): e0310153. https://doi.org/10.1371/journal.pone.0310153
Editor: Andrea Nigri, University of Foggia, ITALY
Received: March 16, 2024; Accepted: August 25, 2024; Published: December 23, 2024
Copyright: © 2024 Dhungana et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Data can be available from the following website: https://databank.worldbank.org/reports.aspx?source=2&series=SH.XPD.CHEX.GD.ZS&country=#.
Funding: The author(s) received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Globally, the government increasingly prioritizes ensuring good health and well-being of the people [1,2]. However, in low income nations, investing in the health sector often have a major financing constraint and limited impact on health outcomes. The health spending capacity of a nation significantly influences health outcomes [3]. Several evidence shows that there is a nexus between health care spending and health outcomes and it acts as a critical measure of healthcare policies effectiveness [4–6]. Yet, the question of improving health outcomes is a significant health policy issue in low income nations. Realizing the significance of good health policy for better health outcomes, researchers conducted this study to explore the nexus between life expectancy and health care spending in South Asia.
South Asia is a home of almost a quarter of the world population struggling for in accessing quality health care and outcomes [7]. This region includes eight nations—Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka. Health care spending remains low, relying heavily on out-of-pocket costs and limited public financing [8]. This region is facing economic resource constraints, gaps in quality of health care, dominant of costly private health delivering institutions, and inadequate health professionals [9]. A critical challenge of poverty, quality education and health services complicate the efforts of government to improve health outcomes [10]. Addressing these issues requires increased investment in health care, effective policy reforms, and the use of new technologies to ensure equitable access to quality health services.
Life expectancy is widely used measurement indicator of health outcome. A population health indicator that considers both length and quality of life is healthy life expectancy (HLE). It is all part of highlighting health disparities, focusing resources on health promotion, assessing the results of health policies, and planning for health, social, and fiscal policy [11]. Health is a crucial factor for productivity and economic growth. Due to its effects on population, participation, and production, good health can over time result in increased GDP (gross domestic product) (GDP) per capita [12]. Good health care system ensures to achieve good health and healthy life expectancy [6]. A major health policy issue is whether improvements in quality of life are consistent with increases in life expectancy.
The Grossman health capital model focuses on the issues related to health, health care demand, occupational choice, health preventions, and socio-economic inequalities in health [13]. This theory suggests that investing in health care spending ensures to improve life expectancy and health outcomes [14]. Adam Wagstaff argues that health care spending can improve health outcomes, but how this improvement will happen depends on the efficiency and adequacy of the health care system [15]. The theory developed by Samuel Preston explains that health care spending and income per capita significantly affect life expectancy [16]. Likewise, human capital theory developed by Gary Becker explains that investment in health and education improves the productivity and economic value of the people. The investment in health care improves human capital that facilitates to increase life expectancy and health outcomes [17]. These insights are important for policymakers aiming to improve health outcomes through targeted and sustained health care spending in South Asia.
Several studies found a positive relationship between health care expenditure and health outcomes [18,19]. The primary influence of health care spending determines a country’s level of wealth. Health care expenditure is mainly influenced by income [20,21]. However, a high health care expenditure sometimes could signify poor health because of epidemics, natural disasters, aging populations, greater demand for medical interventions, and inadequate preventive measures [22]. Effective policymaking at the national and regional levels requires understanding overall health care expenditures as a share of the total gross domestic product (GDP), including public and private health care expenditures [23]. Poor health outcomes and ineffective use of health care resources are linked to low health literacy [24]. Health literacy has become increasingly concern for improving health outcomes in many developing countries [25].
The prominence of societal issues today has led to a rising understanding that achieving inclusive growth and sustainable development depends critically on the health and well-being of a population [26]. In this sense, the correlation between life expectancy and health care spending has drawn much attention as a crucial determinant of a population’s health results [27]. Infrastructure, accessibility, and quality issues in health care present special concerns for South Asia [28,29]. Increased health care spending can significantly improve life expectancy by expanding health services and preventive measures. However, differences in health expenditure and resource allocation often result in differential improvements in health outcomes [4]. Thus, understanding the dynamics of health expenditure and life expectancy in South Asia is critical for effective health care policy development.
Based on the above study, it is found that health care expenditure matters for the health outcomes. However, there is a limited study using econometric analysis to examine the relationship between health care expenditures and life expectancy in South Asia. This study aims to fill the research gap often overlooked the variables gross national income per capita and expected years of schooling in many studies. Moreover, theoretical contributions from Grossman’s health capital model, Adam Wagstaff’s efficiency model, Samuel Preston’s income-health nexus, and Gary Baker’s human capital theory, all of which are rarely used in research. Therefore, this study aims to examine health outcomes in terms of life expectancy with wide range of health care spending indicators—current health expenditures, domestic health expenditures, out-of-pocket payments, and private health expenditures including gross national income per capita, and expected years of schooling. It is anticipated that evidence-based programmes and efforts can be established by policymakers, healthcare providers, and relevant authorities to improve the general well-being and life expectancy of the people in South Asia.
Materials and methods
The study intends to investigate South Asian nations’ health care spending and life expectancy, hence all South Asian nations were included. South Asia comprises eight countries: Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka. This study is based on secondary sources of data that were acquired from World Bank Statistics and Global Health Expenditure Database [30,31]. The variables used in the study were life expectancy, health care expenditure (current health expenditures, domestic health expenditures, out-of-pocket payments, and private health expenditures), anticipated years of schooling, and gross national income per capita. It includes 22 years of data from 2000 to 2021, with a total of 176 pooled time series and cross-section observations from South Asian countries.
The econometric approach was used to investigate the life expectancy and health-care spending in South Asian countries. The panel data were analysed using econometric models, including cross-sectional dependency test, panel unit root test, panel cointegration test, vector error correction model, pair-wise Granger causality test, and Wald test statistics. First, we used the Pasaran CD test to investigate cross-sectional dependence. Then we performed a panel unit root test to ensure that the first and second differences were stationary. Again, we used the panel cointegration test to figure out if there is a long-term relationship between the panel data. After determining the variables’ cointegration, we employed the VECM technique to estimate the long-run adjustment process. We also utilised the Granger causality test and Wald test statistics to assess the causal relationships and short-run cumulative effects of the independent factors on the dependent variable. Finally, we ran a diagnostic residual test to validate the model. We utilised life expectancy at birth to assess population health outcomes [32]. Table 1 provides the name, abbreviation, and description for each variable.
Results and discussion
Cross sectional dependency
Panel-based unit root tests are powerful as compared to the unit root tests based on individual time series. Cross-sectional dependency in panel data refers to correlations or relationships among different entities (units) observed simultaneously across multiple time periods. It is, therefore, crucial first to ensure unbiased parameter estimates and valid inferences in panel data analysis [33]. Testing for cross-sectional dependence or contemporaneous correlation assesses interdependencies among units (individuals, firms, regions) simultaneously, crucial for accurate panel data modelling and interpretation [34]. By understanding these dependencies, researchers and policymakers can make more informed decisions based on robust econometric analyses. In this study, we employed the Pasaran CD test to examine cross-sectional dependence.
The test yielded a Pesaran’s test statistic of -0.675 (Pr = 0.4996) and an average absolute value of the off-diagonal elements of 0.232, indicating no significant cross-sectional dependence in the dataset (Table 2). Consequently, under the assumption of independent units suggested by the Pasaran CD test results, first-generation unit root tests such as the Augmented Dickey Fuller (ADF) or Phillips-Perron (PP) test can be appropriately employed. Thus, ADF test is applied to find the unit root test.
Panel unit root test
Stationarity is crucial for time series and panel data [35]. A time series and panel data are stationary if its mean and variance are time-invariant. Time series econometrics often employs a stationary, linear, or nonlinear time series model after removing a trend from or taking a (log-) difference of a non-stationary economic time series [36]. Data from non-stationary time series should be differentiated into stationary data [37]. The panel unit root test summary has been presented in Table 3.
Table 3 shows that all the variables have unit roots or are non-stationary at a level I (0), where the P-value is inconsequential. Additionally, the table shows that the variable is stationary or has no unit root at the first and second differences, as shown by I (1) and I (2), respectively, as the P-value is significant at the first and second differences. The common unit root process includes Levin, Lin & Chu t*, and the individual unit root process includes Im, Pesaran, and Shin W-stat, ADF—Fisher Chi-square, and PP—Fisher Chi-square.
Panel cointegration test
A cointegration test is utilized to determine whether there is a long-term association between different time series and panel data. Several non-stationary time series and panel data are tested for cointegrating relationships using the Johansen test [38]. The results of cointegration tests reveal situations in which two or more non-stationary time series and panel data are combined so that they cannot depart from equilibrium over the long term. Consider the generic equational model below:
The Panel Cointegration test among the health care spending variables and other variables is presented in Table 4. In this study, sample taken from 2000 to 2021with 176 observations including linear deterministic trend assumption with automatic lag length selection based on SIC with a max lag of 2.
From Table 4, Kao and Pedroni Residual Cointegration test suggest that there is cointegration among life expectancy, out-of-pocket payments, private health expenditure, domestic health expenditure, gross national income per capita, and expected years of schooling at a 5% level. The Johnson Fisher Panel Cointegration test has been presented in Table 5. Here, sample taken from 2000 to 2021with 176 observations including linear deterministic trend assumption.
Table 5 shows five cointegrating equations among the research variables at the 5% level based on trace statistics and the maximum Eigenvalue statistic. We proceed with the VECM approach to estimate the error correction coefficients since the test implies that cointegrated panel data contain an error-correction representation that reflects the long-run adjustment process.
Vector error correction model (VECM)
This model is used to examine the long-term relationships between the variables. When the variables exhibit cointegration and are ordered one integrator, the VECM is employed. Since there is cointegration among all the variables, the VECM model (also called restricted VAR model) has been applied to find out whether there is a long-run association among the variables. The following equation has been developed to estimate VECM:
Equation: D(DDLE) = C(1)* (DDLE(-1) + 0.0233*DDOM(-1) + 0.0239*DOOPS(-1) - 0.0303* DPVTD(-1) - 0.0002*DGNIPC(-1) + 0.0172*DEYS(-1) + 0.0458) + C(2)*D (DDLE(-1)) + C(3)*D(DDLE(-2)) + C(4) *D(DDOM(-1)) + C(5)*D(DDOM(-2)) + C(6)*D(DOOPS(-1)) + C(7) *D(DOOPS(-2)) + C(8)*D(DPVTD(-1)) + C(9)*D(DPVTD(-2)) + C(10) *D(DGNIPC(-1)) + C(11)*D(DGNIPC(-2)) + C(12)*D(DEYS(-1)) + C(13)*D(DEYS(-2)) + C(14) As the coefficient is negative and the p-value is significant for the above model (shown in supporting information), it suggests a long-run association between DDLE to DDOM, DOOPS, DPVTD, DGNIPC, and DEYS. The result of vector error correction model shows that health care spending (current health expenditures, domestic health expenditures, out-of-pocket payments, and private health expenditures), anticipated years of schooling, and gross national income per capita have a long-run association with health outcomes i.e. life expectancy in South Asia.
Granger causality test
Granger created a technique to examine the causal relationship between variables systematically [39]. In the Granger method, the causation from y1,t to y2,t, we examine if the values of y1,t in the past can contribute to an understanding of y2,t in the present. We need to consider four instances for the bivariate model.
Case 1: y1,ty2,t but y2,t
y1,t
Case 2: y2,ty1,t but y1,t
y2,t
Case 3: y1,ty2,t but y2,t
y1,t
Case 4: y1,ty2,t but y2,t
y1,t
Granger causality tests, which Granger first proposed in 1969, look at the pair-wise causal relationship between variables in a model that could result in a one-way, two-way, or no interaction [39]. The following equations can be used to represent the model when used to examine the causality between two variables (X and Y) at time t in time series:
The relationship between various health care spending variables, anticipated years of education, gross national income per capita, and life expectancy in South Asian nations have been examined in this study. The pair-wise Granger causality test has been presented in Table 6 where, sample taken from 2000 to 2021with lag of 1.
Table 6 shows no short-run causality between health care spending and other variables. However, there is unidirectional causality from DDOM to DPVTD and bidirectional causality between DEYS and DPVTD. The pair-wise Granger causality test indicates that health care spending (current health expenditures, domestic health expenditures, out-of-pocket payments, and private health expenditures), anticipated years of schooling, and gross national income per capita do not have a short-run association with health outcomes i.e. life expectancy in South Asia.
The Wald test statistics
Furthermore, Wald test statistics were used to calculate the short-run combined effects of the independent variables on the dependent variable. Accepting the null hypothesis implies that there is no short-run causality exists between the dependent and independent variables [38]. The following null hypothesis has been proposed for a short-term relationship.
Null Hypothesis: C(4) = C(5) = C(6) = C(7) = C(8) = C(9) = C(10) = C(11) = C(12) = C(13)
Table 7 presents the results of the Wald test statistics.
Table 7 suggests that there is no short-run causality between DDLE and other independent variables, as the null hypothesis is rejected. The Wald test statistics do not find short-run causality between life expectancy and health care spending indicators including gross national income per capita, and anticipated years of schooling in South Asia.
Diagnostic test of residuals
For further validation of the model, the residual should be normally distributed and have homoscedasticity and no serial correlation. This assumption of the regression model has been presented below.
Normality test
Normality test was performed to identify whether or not the model’s residual was normally distributed. If the residuals are not normally distributed, the model’s results violate the assumption of the central limit theorem, rendering regression results invalid. The Jarque-Bera test was performed to identify the normality of the model [40]. The Jarque-Bera statistics are 3.4803, with a p-value of 0.1755, indicating that we failed to reject the null hypothesis. It implies that residuals are normally distributed, which validates the model’s assumption (Fig 1).
Breush Pegan test was performed to validate the assumption of homoscedasticy. To improve the model’s validity, the residual should be homoscedastic. If the probability value exceeds 5%, we have strong evidence that the residuals are homoscedastic [41]. The p-value in Table 8 is greater than 5%, so we can accept the null hypothesis. It suggests that the residuals are homoscedastic, which validates the model’s assumption.
The study examines the life expectancy and health care spending, anticipated years of schooling, and gross national income per capita of South Asian countries. The econometric model results indicate that health care spending, anticipated years of schooling, and gross national income per capita all have a long-run relationship with health outcomes i.e. life expectancy. However, pair-wise Granger causality and Wald test statistics reveal no short-run causality between life expectancy and health care spending indicators including gross national income per capita, and anticipated years of schooling in South Asia.
Previous study has demonstrated that total health spending has considerable impact on average life expectancy [42,43]. Improvements in infant mortality correlate with increased health care spending, but they are also linked to life expectancy [44]. Public and private health care spending improves the health outcomes significantly [45–47]. The average number of school years and access to sanitary facilities are both strong predictors of life expectancy [48]. The findings of this study highlight the importance greater government spending on health care in enhancing access to and quality of health services. However, health coverage in many South Asian countries is primarily based on out-of-pocket costs, posing significant challenges for individuals and families. High out-of-pocket costs, as well as insufficient public funds and limited access to health insurance, frequently leads to budget constraints, and reduces essential health services [49].
The causal relationship between education and health is significant. The outcome of this study is consistent with the other researchers who emphasized the importance of income and education in improving health outcomes [50,51]. Health and its components, such as harmful environments, risky behaviours, and the use of preventative services, are strongly correlated with education [52,53]. Individuals with a high level of education are more likely to adopt healthy behaviours and make informed health decisions [54]. Average life expectancy and enrolment in education are directly correlated [55]. People with more years of schooling tend to have better health, more well-being, and healthier habits [56–58]. This is especially significantin the South Asian context, where disparities in educational attainment and health outcomes are evident.
There is a long-term relationship between health outcomes and per capita gross national income and health care spending [59,60]. The growth in health spending and GDP across income levels have a different causal relationship When cross-sectional dependence in the panel is taken into account, the causal link between increase in health spending and GDP across income level changes, and the results demonstrate no long-term cointegration [61].
The growing rate of per capita income has greatly increased life expectancy [4]. When compared to private health spending, public health expenditure considerably extends life expectancy [62]. Spending on health care can enhance life quality and health outcomes while lowering financial obstacles to accessing care [63].
Conclusions
This study finds a long-run correlation between health care expenditure and health outcomes in the South Asian region. Life expectancy is strongly impacted byhealth care spending, gross national income per capita, and anticipated years of schooling. It is vital to consider an efficacious health strategy that may improve and promote individuals’ healthy life expectancy and overall welfare. Enhancing education and increasing student enrolment need to be essential components of health care.
The government may enhance school enrolment to increase health literacy and health outcomes in South Asia. As a result, persons with more years of schooling have better health, higher levels of well-being, healthier habits, and, ultimately, a longer life expectancy. This study will provide an important insight to policymakers on improving health outcomes through targeted and sustained health care spending in South Asia.
This study is confined to assess health care spending (current health expenditures, domestic health expenditures, out-of-pocket payments, and private health expenditures), GNIPC, and EYS with health outcomes as a life expectancy in South Asian nations using econometric analysis. This study only has access to secondary data from World Bank Statistics from 2000 to 2021, but in some situations data are available up to 2019. There are no biases in the selection of data and variables for this study. Further study can be conducted by comparing with East Asia and other similar settings.
Supporting information
S1 File. Links and process of data extraction.
https://doi.org/10.1371/journal.pone.0310153.s002
(DOCX)
Acknowledgments
We would like to express our sincere gratitude to the Editorial Board and anonymous referees for their thoughtful recommendations and input, which helped us improve the paper’s content. We are also grateful to Dr. Niranjan Devkota, Mr. Niraj Paija, and Mr. Ramkrishna Chapagain for their valuable support in the methodology part.
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