Investigating the Nexus between Government Health Expenditure and Growth Across SAARC Nations

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Existing literatures consistently highlights an opposite relationship between Infant Mortality Rate (IMR) and GDP per capita, underscoring the importance of health outcomes in shaping economic performance. In this context, the present paper explores the nexus between public health expenditure and economic growth across SAARC nations, using secondary data from the World Development Indicators. Employing panel regression techniques and various statistical tools, the analysis reveals that Domestic General Government Health Expenditure (DGGHE), IMR, and Life Expectancy at Birth (LEB) exert a statistically significant positive influence on GDP per capita in the region. Conversely, Labour Force (LF) was found to have no significant impact on GDP per capita, potentially reflecting underutilization of labour resources within SAARC economies. On the basis of the findings, it has been recommended to increase government investment in the health sector and strategic initiatives to enhance labour force participation and productivity. Health Expenditure Public Health SAARC Labour force GDP (pc) Economic Growth Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Among various forms of public investment, health stands out as a critical driver of economic productivity and capital formation [ 17 ]. Improved physical and mental health enhances labor efficiency, reduces absenteeism, and fosters long-term human development [ 2 ]. According to the World Bank (2024), disparities in economic growth between developed and developing nations are largely attributable to differences in population health status. Developed countries allocate approximately 9–16% of their GDP to health, while developing nations spend only 3–7%, reflecting a significant investment gap [ 16 ]. This imbalance contributes to a unidirectional relationship between healthcare spending and economic growth, where increased investment in health leads to economic gains [ 5 ]. However, in many developing countries, health expenditure remains low due to limited resources, low per capita income, and insufficient government prioritization [ 12 ]. Numerous studies have explored the linkage between health expenditure and economic growth. While some found weak correlations in developing contexts [ 12 , 15 ], others emphasized the need for greater attention to health investment [ 2 , 6 , 18 ]. In recent years, countries within the SAARC region have experienced rapid economic growth, which has gradually increased the proportion of government healthcare expenditure. This, in turn has contributed to the physical well-being of human capital and supported broader development goals [ 11 ] Following this, the second section presents the evidences on public health expenditure, i.e. the review of literatures. Third section discussed the methodology including study area, variables and data, diagrammatic representation and the model which was used in the paper. Fourth section has presented the empirical results and analyze them in detail. Section five concludes the paper with recommendations. 2. Theoretical Evidences A substantial body of research have explored the relationship between health expenditure and economic growth across various regions and time periods. [ 17 ] found a significant and positive correlation between healthcare spending and economic growth in Nigeria, suggesting that investment in health directly contributes to capital formation. In contrast, [ 3 ] examining Iran from 1970 to 2007, concluded that the impact of health expenditure on economic output was statistically insignificant, highlighting the role of contextual factors in shaping outcomes. [ 4 ], employed a panel co-integration approach to study 20 developing countries and discovered a bilateral long-run causality between GDP and health expenditure. Their findings imply that rising income levels can drive health investment, while improved health can, in turn, support economic growth. 5, analyzing data from Saudi Arabia (1981–2013), used Granger causality tests and found a unidirectional relationship—where economic growth influenced health expenditure, but not vice versa. These mixed results underscore the complexity of the health-growth nexus and suggest that the direction and strength of causality may vary depending on a country’s development stage, institutional capacity, and health system efficiency. Several empirical studies have examined the causal and co-integrative relationship between public health spending and economic growth across diverse contexts. 11, using per capita GDP as the outcome variable and public health spending (PHS) as the input, found a long-run equilibrium relationship between the two, suggesting that sustained investment in health contributes to economic development. [ 18 ], similarly reported a positive and statistically significant correlation between healthcare expenditure (HCE) and economic growth. Their findings indicate that increased HCE enhances labor productivity, which in turn drives economic output. [ 6 ], applied the Granger causality test on health expenditure and growth and found that income levels serve as a key determinant of healthcare expenditure across countries—implying that economic growth tends to raise the proportion of health spending. [ 7 ], in a study grounded in the Abuja Declaration (2001–2013), analyzed GDP per capita alongside variables such as per capita health expenditure, household consumption, life expectancy, labor force, and trade. Their results revealed a positive and significant effect of health expenditure on economic growth, reinforcing the role of health investment in broader development strategies. [ 13 ], explored this relationship in seven emerging economies using panel co-integration methods over the period 1996–2016. Their study found no long-run relationship between private health expenditure and economic growth, but did observe a positive correlation between public health expenditure and GDP growth highlighting the importance of government-led health investment. [ 8 ], examined both private and public health expenditure from 1980 to 2015 using ARDL and ADF techniques. The study concluded that GDP per capita and health expenditure are positively linked, and that health investment can enhance national productivity. The earlier discussion showed that health expenditure leads to capital formation in terms of human capital. Therefore, [ 12 ] examined the impact of human capital formation on GDP growth using annual panel data for the period 2000–2017 of some countries (such as- Bangladesh, India, Nepal, Pakistan and Sri Lanka). The results obtained from ARDL shows that there is no significant impact of health expenditure and education spending on the GDP growth rate. The reason behind is that, such expenditures have no immediate impact for growth, it takes time, so there is a positive and significant result in the long run only. The relationship between health expenditure and economic growth has also been explored through multivariate models incorporating demographic and macroeconomic variables. [ 14 ], using time series data from 1975 to 2018, applied co-integration analysis and found that both health expenditure and life expectancy at birth positively influence economic growth. Interestingly, while trade was identified as a key driver of economic growth, it had a negative impact on health outcomes. Foreign Direct Investment [FDI) also emerged as a significant variable, suggesting that it can amplify the growth effects of health expenditure. The study concluded that a long-term relationship exists between health spending and other macroeconomic indicators, reinforcing the multidimensional nature of growth. [ 18 ], examined GDP per capita as the dependent variable, with public health expenditure, Human Development Index (HDI), labor force, life expectancy, and infant mortality as independent variables. Using panel co-integration tests and regression analysis, they found that public health expenditure, HDI, labor force, and infant mortality had a positive and significant impact on GDP per capita. However, life expectancy showed a negative and statistically insignificant effect, suggesting that its influence may be context-dependent or mediated by other factors. A similar study by [ 15 ], based on time series data from 2000 to 2020 sourced from the World Bank, National Statistical Office, and Eurostat, also found a negative and insignificant relationship between infant mortality and economic growth. This aligns with the notion that while infant mortality is a critical health indicator, its direct impact on GDP may decrease [ 19 ]. Thus, all the literatures revealed that there is a close relationship between public health expenditure and GDP of any country. 3. Methodology The study covered the eight member countries such as Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan and Sri Lanka of SAARC region to examine the effect of health expenditure on economic growth. The SAARC countries account for 3.8% of global GDP, 3% of its geographical area, and 21% [1.7 billion) of its population [Islam and Korim, 2019). But the SAARC region have been facing a lot of problems such as poverty, illiteracy, low GDP per capita, natural disasters, lack of infrastructure, lack of advance technology, poor socio-economic condition, etc. (Study IQ). The contribution of GDP (pc) in SAARC countries is low in comparison to the other developed countries and world. Figure 1. shows that the GDP (pc) of South Asia is low in comparison to the other regions and the world. It was only $ 2287.4, while for East Asia & Pacific, OECD members and the world, the value of GDP (pc) are $12930.7, $43476.4 and $12687.7 respectively. Data sources and variables : Only secondary data collected from World bank for the period 2002-2020. The present study was chosen the secondary data because of the macroeconomic nature of the study and also the availability and organised nature. STATA 14 is used to analyse the data and for the empirical estimation in the study. The definition of the variables and data source is given in table 1: Table 1 : Key variables and their sources of data Variables Definition Source GDP [pc) GDP per capita World Bank DGGHE Domestic General Government Health Expenditure [% of GDP) World Bank LEB Life Expectancy at Birth World Bank IMR Infant Mortality Rate World Bank LF Labour Force, Total World Bank Source: Author’s compilation Tools of analysis Diagrammatic representation: The line graphs were used to compare the GDP (pc) and DGGHE (% of general government health expenditure) of different regions over the period 2002-2020 in SAARC region. The econometric model: Panel regression model have been used for this study as it is best method to analyse multiple countries in years [19]. The panel data analysis also has numerous advantages over the time series and cross-sectional data. The effects of public health expenditures on GDP per capita (proxy for economic growth) is estimated using the following equation 1: GDP [pc) it = α + β 1 DGGHE it + β 2 LE it + β 3 IMR it + β 4 LF it + ɛ it ………………….. [1) Where, GDP it denotes as economic growth measured per capita [current US$) in country i in period t . DGGHE represents the domestic government general health expenditure as % of GDP, LE indicates life expectancy at birth, IMR indicates the infant mortality rate and LF represents a labour force total. β 1 , β 2 , β 3 , and β 4 = partial slope coefficients. The model in equation 1 is estimated with pooled OLS estimator, fixed effect estimator (FEE) and the random effect estimator (REE). When there is no serial correlation between the error term and reminder error, and spatial serial dependence of error terms is absent then REE is more appropriate [19]. On the other hand, when the specific intercept term is correlated with one or more regressors then FEE is appropriate [1]. Hausman test has been conducted to know the relevance of FEE and REE. Moreover, Breusch and Pagan Lagrange Multiplier test conducted to select between pooled OLS estimator and REE. Sometimes, non-stationarity of data may lead to invalid prediction of results. Therefore, Levin-Lin-Chu [LLC) panel unit root test has been used for checking the stationarity. Results and Discussion Trend of health expenditure and economic growth across SAARC countries: Figure 2 presents the trend of GDP per capita measured at current US$ for SAARC countries. The graph shows that GDP per capita had been consistent for most of the SAARC countries, i.e., Afghanistan, Bangladesh, Bhutan, India, Nepal, and Pakistan. While it can identify that GDP per capita has been rapidly rising in Maldives for the period 2002-2020. Figure 3 presents the trend of domestic government general health expenditure in SAARC countries. It shows that health expenditure is almost consistent in most of the countries over the period. But it reveals that, in comparison to the other SAARC countries, Maldives government has the highest expenditure on health. While Afghanistan spent very less amount on health sector. Hence, we can say that performance of other SAARC countries in health indicators is very poor. And therefore, government of those countries should focus on increasing health expenditure. The trend of LEB in SAARC countries have been depicted in figure 4. It has seen from the figure that LEB of all the SAARC countries have been rising since 2002. Here we have observed that Maldives take notable position in LEB in comparison to the other countries of SAARC. But, LEB in Afghanistan is too low compared to other countries. Another figure (figure 5) on trend line has been drawn to show the trends in IMR in SAARC countries. The graph of trend line shows that IMR have been falling in all countries for the period 2002-2020. The lowest value of IMR was recorded for Maldives. Sri Lanka had almost consistency in IMR over the period. Thus, all the figures on various indicators of health shows that performance of SAARC countries is not well in all the indicators of health. Among the SAARC countries only Maldives performed very well as it spent more on health sector. Among the other SAARC countries Afghanistan’s performance is very poor as it spent less on health sector. Descriptive statistics: Table 2 presents the descriptive statistics such as mean, standard deviation, minimum and maximum values of the variables. Mean indicates the average value and standard deviation measures how dispersed the variable in relation to the mean. In Table 2, the mean of GDP (pc) is 2100.279 (range: from 182.17 to 11349.86) with standard deviation 2329.829, indicates the volatility of GDP (pc) in SAARC region for the period 2002 to 2020. For the same period, the mean of DGGHE (% of GDP) is 1.60 with standard deviation 1.57 indicating a low variation. Again, for the same period, the mean of LF is 68.53 and minimum and maximum are 56.45 and 80.12 with standard deviation 5.22, whereas the mean value of IMR is 38.29 (range: from 5.5 to 85.4) with standard deviation 21.48, indicating a high rate of fluctuation in IMR in SAARC region between 2002 to 2020. In addition, the mean value of LF is 7.64 with 3.97 as minimum and 5.21 as maximum value (Std. dev: 1.51) for the same period. It can be interpreted from the above results that DGGHE in SAARC countries is too low to maintain good health. Therefore, we have seen that IMR is very high in these countries. Moreover, the life expectancy also not efficient for these countries. Table 2 : Descriptive Statistics of the variables used for panel regression. Variables Mean Std. Dev. Min Max GDP[pc) 2100.279 2329.829 182.17 11349.86 DGGHE [% of GDP) 1.599868 1.565627 0.08 9.08 LE 68.53355 5.215852 56.45 80.12 IMR 38.28618 21.48136 5.5 85.4 LF 7.64007 1.51008 3.9739 5.21008 Countries [n) 8 Year [T) 19 Observations [N) 152 Abbreviations: GDP(pc), Gross Domestic Product per capita (current US $); DGGHE (% of GDP), Domestic General Government Health Expenditure (% of Gross Domestic Product); LE, Life Expectancy at Birth; IMR, Infant Mortality Rate 1000; LF, Labour Force, Total. Source: Author’s estimation based on data compiled from the World Development Indicators. Empirical Results Unit root test: As per the results of LLC presented in Table 3, shows that we can reject the null hypothesis of non-stationarity for the variables LE and IMR, and therefore we can use them in our regression model. However, rest of the variables i.e. GDP(pc), DGGHE (% of GDP) and Labour Force Total are shown non-stationary in their level; but after taking the second difference of the GDP(pc), DGGHE (% of GDP) and Labour Force are stationary. Thus, we use these variables in our regression model after taking second difference. Table 3 : Levin-Lin-Chu (LLC) unit root test statistics of the variables used in panel regression. Source: Author’s estimation based on data compiled from the World Development Indicators. Note: dd denotes the second difference of the stated variables. Abbreviations: GDP(pc), Gross Domestic Product per capita (current US $); DGGHE (% of GDP), Domestic Government General Health Expenditure (% of Gross Domestic Product); LE, Life Expectancy at Birth; IMR, Infant Mortality Rate 1000; LF, Labour Force, Total. *significant at 1%, **significant at 5% Effect of Health Expenditure on Economic Growth : The results on the effect of independent variables on GDP (pc) are presented in table 4. Table 4 : Coefficient Estimated, GDP (pc) as an outcome variable Independent Variables/constant/others Pooled OLS Estimator Fixed Effect Estimator Random Effect Estimator DGGHE (% of GDP) LE 715.125* (0.000) 411.7454* (0.000) 429.408*** (0.000) 670.6246* (0.003) 444.5203** (0.000) 635.6183** (0.000) IMR -50.96616** -83.67473* -8.15299** (0.000) (0.024) (0.000) LF 9075.555* 8025.65* 1000.435* (0.256) (0.288) (0.335) Constant -29173.96* -47391* -45170.12 [0.000) [0.004) [0.000) R 2 0.8866 0.7375 0.7360 F/Wald x 2 287.28* 409.67* 448.37* Source: Author’s own calculation on the basis of data compiled from the World Development Indicators. Note: Dependent Variable – GDP(pc) *significant at 1%, **significant at 5% and ***significant at 10% Hausman specification test: to H 0 : Difference in coefficients not systematic identify the appropriateness between Chi-square value: 8.43 fixed and random effect estimators p-value = 0.0379 Breusch and Pegan Lagrange H 0 : Var (u) = 0 multiplier test: to identify the Chibar 2 (01) value = 150.40 appropriateness between random and p-value = 0.000 pooled OLS estimators Abbreviations: GDP(pc)- Gross Domestic Product per capita (current US $); DGGHE (% of GDP)- Domestic Government General Health Expenditure (% of Gross Domestic Product); LE- Life Expectancy at Birth; IMR- Infant Mortality Rate 1000; LF- Labour Force, Total. The test such as Hausman specification and Breusch & Pagan Lagrange multiplier confirmed that random effect estimator (REE) is more appropriate than pooled OLS and again the fixed effect estimator (FEE) is more useful than random effect (REE) in this estimate. The REE is significant with an R-square value of 73.60%, and F-statistic value of 448.37. The R-square value indicates that 73.60% of the change in GDP (pc) (proxy for economic growth) is due to changes in the regressors or explanatory variables used in the study and the F test value indicate that the overall model is highly significant with 1% level of significance. Table 4 also shows that DGGHE (% of GDP) has positive and significant impact on GDP(pc). If the DGGHE increase by 1 percent then GDP (pc) increase by an average of 444.52% (significant at 5%). Life expectancy at birth shows a positive impact on standard of living and significant at the level of importance of 1% (p = 0.000). At the 1% importance level, it is estimated that if life expectancy increase by 1 year, GDP(pc) will increase by 6355.62 on average [significant at 5%). The effect of Infant mortality rate is significantly negative on GDP (pc). For every 1% decrease in IMR, estimate that GDP (pc) will increase by an average of 8.15% (significant at 5%). But the Labour Force has no significant impact on GDP(pc). It may be due to the fact that there was a lack of labour force or more labour but lack of productivity in SAARC countries which lead to the variation in result. Thus, the results of the present study looks similar to the findings of the studies of [2]; [6]; [12]. That is, low expenditure on health lead to low growth of labour force which in turn affect the GDP (pc) of the SAARC countries. Conclusion The present study investigated the cause-and-effect relationship between health expenditure on economic growth in the SAARC region using panel data from 2002 to 2020. Additionally, an effort has been made to investigate the relationship between the region's economic growth and the trends of health spending. The unit root test has been used to assess the stationarity of the data. It has been observed that majority of the countries of SAARC region have stable GDP per capita. The GDP per capita of Maldives' has been increasing quickly. Apart from that, it has been noted that the majority of SAARC nations, that is, Afghanistan, Bangladesh, Bhutan, India, Nepal, Pakistan, and Sri Lanka have stable domestic government general health expenditures (DGGHE) as a percentage of GDP. The DGGHE is extremely unstable only in the Maldives. Once again, all SAARC nations have shown increases in life expectancy at birth (LEB). Afghanistan had the lowest LEB and the Maldives the highest. For all SAARC nations, the infant mortality rate (IMR) has been falling and Maldives recorded lowest IMR value. The Panel regression revealed that the SAARC region's GDP (pc) is significantly boosted by DGGHE, IMR, and LEB. The study further concluded that LF has no discernible effect on GDP (pc). It could be because SAARC countries have poor labor utilization rates. The current study suggests that in order to boost economic growth, the governments of SAARC countries should enhance health expenditure. The government should simultaneously concentrate on allocating its health spending in an efficient manner. Moreover, the workforce is crucial to a nation's economic progress. For which, the government should place greater emphasis on labour by offering adequate training and effective techniques, which will ultimately boost overall development. Declarations Acknowledgements: Authors would like to thank the Department of Economics, Nagaon University, Nagaon, Assam for their facilities and support in conducting this project work. Funding: The author (s) did not receive support from any organization for the submitted work. Declarations: Ethics declaration: Not applicable. Competing interests: The authors declare no potential competing interests. Consent to participate: Not applicable. Ethics approval: Not applicable. Clinical trial number: Not applicable. Consent to Publish declaration: Not applicable. Author Contribution Declaration: The first author or the corresponding author designed the idea of the study and drawn the skeleton of the manuscript and finalise the paper after thorough review. The second author collected data, reviewed the literatures, analysed the data and written the draft of the paper. Data availability: The data used in this article will be made available upon reasonable request to the corresponding author. References Gujarati DN, Porter DC, Pal M. (2009). Basic Econometrics [Vol. 6). Mc Graw Hill. Bakare AS, Olubokun S. Health Care Expenditure and Economic Growth in Nigeria: An Empirical Study. J Emerg Trends Econ Manage Sci. 2011;2(2):83–7. Mehrara M, Musai M. Health Expenditure and Economic growth: An ARDL Approach for the Case of Iran. J Econ Behav Stud. 2011;3(4):249–56. Elmi ZM, Sadeghi S. 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Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 24 Feb, 2026 Reviews received at journal 14 Feb, 2026 Reviewers agreed at journal 14 Feb, 2026 Reviews received at journal 14 Feb, 2026 Reviewers agreed at journal 13 Feb, 2026 Reviewers agreed at journal 13 Feb, 2026 Reviewers invited by journal 13 Feb, 2026 Editor invited by journal 14 Jan, 2026 Editor assigned by journal 09 Jan, 2026 Submission checks completed at journal 09 Jan, 2026 First submitted to journal 05 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8523360","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":591437691,"identity":"24edf03d-60e2-48a8-971a-d548fd7aa459","order_by":0,"name":"Dimpal Dekaraja","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxElEQVRIiWNgGAWjYDCCwyCigJmBH0QnFBCtxYCZQbIBpMWAGC0HoFoMIAwidPAd50578MHAWs74/OrEDw8MGOT5xQ7g1yJ5mHe74QyDdGOzG283SwAdZjhzdgJ+LQaHebdJ8xgcTtx24+wGkJYEg9tEaqnfPOPs5h8kaUkw4O/dRpwtML8YzrjBu80iwUCCsF/4zp/d9uBDhbU8f//ZzTd/VNjI80sT0AIEbBBKAqxSgqByJC38B4hSPQpGwSgYBSMQAAAqBURKmC8Z2wAAAABJRU5ErkJggg==","orcid":"","institution":"Nagaon University","correspondingAuthor":true,"prefix":"","firstName":"Dimpal","middleName":"","lastName":"Dekaraja","suffix":""},{"id":591437693,"identity":"be6c121c-1fba-4926-90a3-a2150a25d46f","order_by":1,"name":"Moniram Hanse","email":"","orcid":"","institution":"Gauhati University","correspondingAuthor":false,"prefix":"","firstName":"Moniram","middleName":"","lastName":"Hanse","suffix":""}],"badges":[],"createdAt":"2026-01-05 16:23:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8523360/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8523360/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102964418,"identity":"52cd9d33-dd39-4a3a-81c2-7328ad55f5ad","added_by":"auto","created_at":"2026-02-19 04:22:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":17702,"visible":true,"origin":"","legend":"\u003cp\u003eStatus of GDP per capita across regions in the world.\u003c/p\u003e\n\u003cp\u003eSource: World Health statistics, 2024.\u003c/p\u003e\n\u003cp\u003eNote: GDP (pc), Gross Domestic Product [Per Capita) [current US$), the value are collected for 2020.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8523360/v1/e34e8c80a24bbf4effe4b786.png"},{"id":102941722,"identity":"a28dcf8a-9b50-49a7-8936-08025f5b4d74","added_by":"auto","created_at":"2026-02-18 17:20:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":20840,"visible":true,"origin":"","legend":"\u003cp\u003eTrend in GDP per capita for SAARC countries.\u003c/p\u003e\n\u003cp\u003eSource: World Development Indicators\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8523360/v1/bfaa0d9dbe771a9aea51318f.png"},{"id":102941720,"identity":"3be4af1e-bf08-4578-b4fa-53fdb650bd11","added_by":"auto","created_at":"2026-02-18 17:20:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":17617,"visible":true,"origin":"","legend":"\u003cp\u003eTrend in Domestic Government General Health Expenditure [DGGHE) for SAARC countries. Source: World Bank\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8523360/v1/baa016d9d41b2fbe0fdb42c0.png"},{"id":102964110,"identity":"224ee475-0590-447c-bfad-c88ed2506c72","added_by":"auto","created_at":"2026-02-19 04:21:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":19168,"visible":true,"origin":"","legend":"\u003cp\u003eTrend in Life Expectancy at Birth [LEB) for SAARC countries.\u003c/p\u003e\n\u003cp\u003eSource: World Development Indicators\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8523360/v1/5c680b4c3aedb44977d8a792.png"},{"id":102941723,"identity":"5e00c77e-3af7-4070-b470-56dfeeb38238","added_by":"auto","created_at":"2026-02-18 17:20:44","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":20197,"visible":true,"origin":"","legend":"\u003cp\u003eTrend in Infant Mortality Rate [(IMR) in SAARC countries.\u003c/p\u003e\n\u003cp\u003eSource: World Development Indicators.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8523360/v1/71ab3b5d53a2b923fb95627f.png"},{"id":102965699,"identity":"9c1a355f-35bd-40d4-b4fc-edb56fd4d957","added_by":"auto","created_at":"2026-02-19 04:32:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":728392,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8523360/v1/9763279b-e8bd-499e-ada5-4a8d38362de8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Investigating the Nexus between Government Health Expenditure and Growth Across SAARC Nations","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAmong various forms of public investment, health stands out as a critical driver of economic productivity and capital formation [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Improved physical and mental health enhances labor efficiency, reduces absenteeism, and fosters long-term human development [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. According to the World Bank (2024), disparities in economic growth between developed and developing nations are largely attributable to differences in population health status. Developed countries allocate approximately 9\u0026ndash;16% of their GDP to health, while developing nations spend only 3\u0026ndash;7%, reflecting a significant investment gap [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis imbalance contributes to a unidirectional relationship between healthcare spending and economic growth, where increased investment in health leads to economic gains [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, in many developing countries, health expenditure remains low due to limited resources, low per capita income, and insufficient government prioritization [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNumerous studies have explored the linkage between health expenditure and economic growth. While some found weak correlations in developing contexts [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], others emphasized the need for greater attention to health investment [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In recent years, countries within the SAARC region have experienced rapid economic growth, which has gradually increased the proportion of government healthcare expenditure. This, in turn has contributed to the physical well-being of human capital and supported broader development goals [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eFollowing this, the second section presents the evidences on public health expenditure, i.e. the review of literatures. Third section discussed the methodology including study area, variables and data, diagrammatic representation and the model which was used in the paper. Fourth section has presented the empirical results and analyze them in detail. Section five concludes the paper with recommendations.\u003c/p\u003e"},{"header":"2. Theoretical Evidences","content":"\u003cp\u003eA substantial body of research have explored the relationship between health expenditure and economic growth across various regions and time periods. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] found a significant and positive correlation between healthcare spending and economic growth in Nigeria, suggesting that investment in health directly contributes to capital formation. In contrast, [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] examining Iran from 1970 to 2007, concluded that the impact of health expenditure on economic output was statistically insignificant, highlighting the role of contextual factors in shaping outcomes.\u003c/p\u003e \u003cp\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], employed a panel co-integration approach to study 20 developing countries and discovered a bilateral long-run causality between GDP and health expenditure. Their findings imply that rising income levels can drive health investment, while improved health can, in turn, support economic growth. 5, analyzing data from Saudi Arabia (1981\u0026ndash;2013), used Granger causality tests and found a unidirectional relationship\u0026mdash;where economic growth influenced health expenditure, but not vice versa. These mixed results underscore the complexity of the health-growth nexus and suggest that the direction and strength of causality may vary depending on a country\u0026rsquo;s development stage, institutional capacity, and health system efficiency.\u003c/p\u003e \u003cp\u003eSeveral empirical studies have examined the causal and co-integrative relationship between public health spending and economic growth across diverse contexts. 11, using per capita GDP as the outcome variable and public health spending (PHS) as the input, found a long-run equilibrium relationship between the two, suggesting that sustained investment in health contributes to economic development.\u003c/p\u003e \u003cp\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], similarly reported a positive and statistically significant correlation between healthcare expenditure (HCE) and economic growth. Their findings indicate that increased HCE enhances labor productivity, which in turn drives economic output. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], applied the Granger causality test on health expenditure and growth and found that income levels serve as a key determinant of healthcare expenditure across countries\u0026mdash;implying that economic growth tends to raise the proportion of health spending. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], in a study grounded in the Abuja Declaration (2001\u0026ndash;2013), analyzed GDP per capita alongside variables such as per capita health expenditure, household consumption, life expectancy, labor force, and trade. Their results revealed a positive and significant effect of health expenditure on economic growth, reinforcing the role of health investment in broader development strategies.\u003c/p\u003e \u003cp\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], explored this relationship in seven emerging economies using panel co-integration methods over the period 1996\u0026ndash;2016. Their study found no long-run relationship between private health expenditure and economic growth, but did observe a positive correlation between public health expenditure and GDP growth highlighting the importance of government-led health investment. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], examined both private and public health expenditure from 1980 to 2015 using ARDL and ADF techniques. The study concluded that GDP per capita and health expenditure are positively linked, and that health investment can enhance national productivity.\u003c/p\u003e \u003cp\u003eThe earlier discussion showed that health expenditure leads to capital formation in terms of human capital. Therefore, [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] examined the impact of human capital formation on GDP growth using annual panel data for the period 2000\u0026ndash;2017 of some countries (such as- Bangladesh, India, Nepal, Pakistan and Sri Lanka). The results obtained from ARDL shows that there is no significant impact of health expenditure and education spending on the GDP growth rate. The reason behind is that, such expenditures have no immediate impact for growth, it takes time, so there is a positive and significant result in the long run only.\u003c/p\u003e \u003cp\u003eThe relationship between health expenditure and economic growth has also been explored through multivariate models incorporating demographic and macroeconomic variables. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], using time series data from 1975 to 2018, applied co-integration analysis and found that both health expenditure and life expectancy at birth positively influence economic growth. Interestingly, while trade was identified as a key driver of economic growth, it had a negative impact on health outcomes. Foreign Direct Investment [FDI) also emerged as a significant variable, suggesting that it can amplify the growth effects of health expenditure. The study concluded that a long-term relationship exists between health spending and other macroeconomic indicators, reinforcing the multidimensional nature of growth.\u003c/p\u003e \u003cp\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], examined GDP per capita as the dependent variable, with public health expenditure, Human Development Index (HDI), labor force, life expectancy, and infant mortality as independent variables. Using panel co-integration tests and regression analysis, they found that public health expenditure, HDI, labor force, and infant mortality had a positive and significant impact on GDP per capita. However, life expectancy showed a negative and statistically insignificant effect, suggesting that its influence may be context-dependent or mediated by other factors.\u003c/p\u003e \u003cp\u003eA similar study by [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], based on time series data from 2000 to 2020 sourced from the World Bank, National Statistical Office, and Eurostat, also found a negative and insignificant relationship between infant mortality and economic growth. This aligns with the notion that while infant mortality is a critical health indicator, its direct impact on GDP may decrease [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThus, all the literatures revealed that there is a close relationship between public health expenditure and GDP of any country.\u003c/p\u003e"},{"header":"3. Methodology","content":"\u003cp\u003eThe study covered the eight member countries such as Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan and Sri Lanka of SAARC region to examine the effect of health expenditure on economic growth. The SAARC countries account for 3.8% of global GDP, 3% of its geographical area, and 21% [1.7 billion) of its population [Islam and Korim, 2019). But the SAARC region have been facing a lot of problems such as poverty, illiteracy, low GDP per capita, natural disasters, lack of infrastructure, lack of advance technology, poor socio-economic condition, etc. (Study IQ).\u003c/p\u003e\n\u003cp\u003eThe contribution of GDP (pc) in SAARC countries is low in comparison to the other developed countries and world. Figure 1. shows that the GDP (pc) of South Asia is low in comparison to the other regions and the world. It was only $ 2287.4, while for East Asia \u0026amp; Pacific, OECD members and the world, the value of GDP (pc) are $12930.7, $43476.4 and $12687.7 respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData sources and variables\u003c/strong\u003e: Only secondary data collected from World bank for the period 2002-2020. The present study was chosen the secondary data because of the macroeconomic nature of the study and also the availability and organised nature. STATA 14 is used to analyse the data and for the empirical estimation in the study. The definition of the variables and data source is given in table 1:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e: Key variables and their sources of data\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDefinition\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGDP [pc)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eGDP per capita\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eWorld Bank\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDGGHE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eDomestic General Government Health Expenditure [% of GDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eWorld Bank\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLEB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eLife Expectancy at Birth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eWorld Bank\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIMR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eInfant Mortality Rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eWorld Bank\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eLabour Force, Total\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eWorld Bank\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: Author\u0026rsquo;s compilation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTools of analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDiagrammatic representation: The line graphs were used to compare the GDP (pc) and DGGHE (% of general government health expenditure) of different regions over the period 2002-2020 in SAARC region.\u003c/p\u003e\n\u003cp\u003eThe econometric model: Panel regression model have been used for this study as it is best method to analyse multiple countries in years [19]. The panel data analysis also has numerous advantages over the time series and cross-sectional data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe effects of public health expenditures on GDP per capita (proxy for economic growth) is estimated using the following equation 1:\u003c/p\u003e\n\u003cp\u003eGDP [pc)\u003cem\u003e\u003csub\u003eit\u003c/sub\u003e\u003c/em\u003e = \u0026alpha; + \u003cem\u003e\u0026beta;\u003csub\u003e1\u003c/sub\u003e\u003c/em\u003eDGGHE\u003cem\u003e\u003csub\u003eit\u003c/sub\u003e\u003c/em\u003e + \u0026beta;\u003csub\u003e2\u003c/sub\u003eLE\u003cem\u003e\u003csub\u003eit\u003c/sub\u003e\u003c/em\u003e + \u0026beta;\u003csub\u003e3\u003c/sub\u003eIMR\u003cem\u003e\u003csub\u003eit\u003c/sub\u003e\u003c/em\u003e + \u0026beta;\u003csub\u003e4\u003c/sub\u003eLF\u003cem\u003e\u003csub\u003eit\u003c/sub\u003e\u003c/em\u003e + ɛ\u003cem\u003e\u003csub\u003eit\u003c/sub\u003e\u003c/em\u003e \u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.. [1)\u003c/p\u003e\n\u003cp\u003eWhere, GDP\u003csub\u003eit\u003c/sub\u003e denotes as economic growth measured per capita [current US$) in country \u003cem\u003ei\u003c/em\u003e in period \u003cem\u003et\u003c/em\u003e. DGGHE represents the domestic government general health expenditure as % of GDP, LE indicates life expectancy at birth, IMR indicates the infant mortality rate and LF represents a labour force total. \u003cem\u003e\u0026beta;\u003csub\u003e1\u003c/sub\u003e, \u0026beta;\u003csub\u003e2\u003c/sub\u003e, \u0026beta;\u003csub\u003e3\u003c/sub\u003e, and \u0026beta;\u003csub\u003e4\u003c/sub\u003e\u0026nbsp;\u003c/em\u003e= partial slope coefficients.\u003c/p\u003e\n\u003cp\u003eThe model in equation 1 is estimated with pooled OLS estimator, fixed effect estimator (FEE) and the random effect estimator (REE). When there is no serial correlation between the error term and reminder error, and spatial serial dependence of error terms is absent then REE is more appropriate [19]. On the other hand, when the specific intercept term is correlated with one or more regressors then FEE is appropriate [1]. Hausman test has been conducted to know the relevance of FEE and REE. Moreover, Breusch and Pagan Lagrange Multiplier test conducted to select between pooled OLS estimator and REE. Sometimes, non-stationarity of data may lead to invalid prediction of results. Therefore, Levin-Lin-Chu [LLC) panel unit root test has been used for checking the stationarity.\u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cp\u003eTrend\u0026nbsp;of\u0026nbsp;health expenditure\u0026nbsp;and economic\u0026nbsp;growth across\u0026nbsp;SAARC countries:\u003c/p\u003e\n\u003cp\u003eFigure 2 presents the trend of GDP per capita measured at current US$ for SAARC countries. The graph shows that GDP per capita had been consistent for most of the SAARC countries, i.e., Afghanistan, Bangladesh, Bhutan, India, Nepal, and Pakistan. While it can identify that GDP per capita has been rapidly rising in Maldives for the period 2002-2020.\u003c/p\u003e\n\u003cp\u003eFigure 3 presents the trend of domestic government general health expenditure in SAARC countries. It shows that health expenditure is almost consistent in most of the countries over the period. But it reveals that, in comparison to the other SAARC countries, Maldives government has the highest expenditure on health. While Afghanistan spent very less amount on health sector. Hence, we can say that performance of other SAARC countries in health indicators is very poor. And therefore, government of those countries should focus on increasing health expenditure.\u003c/p\u003e\n\u003cp\u003eThe trend of LEB in SAARC countries have been depicted in figure 4. It has seen from the figure that LEB of all the SAARC countries have been rising since 2002. Here we have observed that Maldives take notable position in LEB in comparison to the other countries of SAARC. But, LEB in Afghanistan is too low compared to other countries.\u003c/p\u003e\n\u003cp\u003eAnother figure (figure 5) on trend line has been drawn to show the trends in IMR in SAARC countries. The graph of trend line shows that IMR have been falling in all countries for the period 2002-2020. The lowest value of IMR was recorded for Maldives. Sri Lanka had almost consistency in IMR over the period.\u003c/p\u003e\n\u003cp\u003eThus, all the figures on various indicators of health shows that performance of SAARC countries is not well in all the indicators of health. Among the SAARC countries only Maldives performed very well as it spent more on health sector. Among the other SAARC countries Afghanistan\u0026rsquo;s performance is very poor as it spent less on health sector.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDescriptive statistics:\u0026nbsp;\u003c/strong\u003eTable 2\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003epresents the descriptive statistics such as mean, standard deviation, minimum and maximum values of the variables. Mean indicates the average value and standard deviation measures how dispersed the variable in relation to the mean. In Table 2, the mean of GDP (pc) is 2100.279 (range: from 182.17 to 11349.86) with standard deviation 2329.829, indicates the volatility of GDP (pc) in SAARC region for the period 2002 to 2020. For the same period, the mean of DGGHE (% of GDP) is 1.60 with standard deviation 1.57 indicating a low variation. Again, for the same period, the mean of LF is 68.53 and minimum and maximum are 56.45 and 80.12 with standard deviation 5.22, whereas the mean value of IMR is 38.29 (range: from 5.5 to 85.4) with standard deviation 21.48, indicating a high rate of fluctuation in IMR in SAARC region between 2002 to 2020. In addition, the mean value of LF is 7.64 with 3.97 as minimum and 5.21 as maximum value (Std. dev: 1.51) for the same period. It can be interpreted from the above results that DGGHE in SAARC countries is too low to maintain good health. Therefore, we have seen that IMR is very high in these countries. Moreover, the life expectancy also not efficient for these countries.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e: Descriptive Statistics of the variables used for panel regression.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"587\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Dev.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMax\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003eGDP[pc)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e2100.279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e2329.829\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e182.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e11349.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003eDGGHE\u0026nbsp;[%\u0026nbsp;of GDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e1.599868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e1.565627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e9.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003eLE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e68.53355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e5.215852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e56.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e80.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003eIMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e38.28618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e21.48136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e5.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e85.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003eLF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e7.64007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e1.51008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e3.9739\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e5.21008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003eCountries [n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003eYear [T)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003eObservations [N)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: GDP(pc), Gross Domestic Product per capita (current US $); DGGHE (% of GDP), Domestic General Government Health Expenditure (% of Gross Domestic Product); LE, Life Expectancy at Birth; IMR, Infant Mortality Rate 1000; LF, Labour Force, Total.\u003c/p\u003e\n\u003cp\u003eSource: Author\u0026rsquo;s estimation based on data compiled from the World Development Indicators.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEmpirical\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnit root test: As per the results of LLC presented in Table 3, shows that we can reject the null hypothesis of non-stationarity for the variables LE and IMR, and therefore we can use them in our regression model. However, rest of the variables i.e. GDP(pc), DGGHE (% of GDP) and Labour Force Total are shown non-stationary in their level; but after taking the second difference of the GDP(pc), DGGHE (% of GDP) and Labour Force are stationary. Thus, we use these variables in our regression model after taking second difference.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e: Levin-Lin-Chu (LLC) unit root test statistics of the variables used in panel regression.\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003eSource: Author\u0026rsquo;s estimation based on data compiled from the World Development Indicators.\u003c/p\u003e\n\u003cp\u003eNote: dd denotes the second difference of the stated variables.\u003c/p\u003e\n\u003cp\u003eAbbreviations: GDP(pc), Gross Domestic Product per capita (current US $); DGGHE (% of GDP), Domestic Government General Health Expenditure (% of Gross Domestic Product); LE, Life Expectancy at Birth; IMR, Infant Mortality Rate 1000; LF, Labour Force, Total.\u003c/p\u003e\n\u003cp\u003e*significant at 1%, **significant at 5%\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEffect of Health Expenditure on Economic Growth\u003c/strong\u003e: The results on the effect of independent variables on GDP (pc) are presented in table 4.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e: Coefficient Estimated, GDP (pc) as an outcome variable\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndependent\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eVariables/constant/others\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePooled OLS\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eEstimator\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFixed Effect Estimator\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRandom Effect Estimator\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003eDGGHE\u0026nbsp;(%\u0026nbsp;of GDP)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eLE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e715.125* (0.000) 411.7454*\u003c/p\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e429.408*** (0.000) 670.6246*\u003c/p\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e444.5203** (0.000) 635.6183**\u003c/p\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"608\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003eIMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003e-50.96616**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e-83.67473*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e-8.15299**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e(0.024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003eLF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003e9075.555*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e8025.65*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e1000.435*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003e(0.256)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e(0.288)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e(0.335)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003e-29173.96*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e-47391*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e-45170.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003e[0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e[0.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e[0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003cem\u003e2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003e0.8866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.7375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e0.7360\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003eF/Wald\u003cem\u003ex\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003e287.28*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e409.67*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e448.37*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eSource: Author\u0026rsquo;s own calculation on the basis of data compiled from the World Development Indicators.\u003c/p\u003e\n\u003cp\u003eNote:\u0026nbsp;Dependent\u0026nbsp;Variable\u0026nbsp;\u0026ndash; GDP(pc)\u003c/p\u003e\n\u003cp\u003e*significant at 1%, **significant at 5% and ***significant at 10%\u003c/p\u003e\n\u003cp\u003eHausman specification test: to \u003cem\u003eH\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e:\u0026nbsp;Difference\u0026nbsp;in\u0026nbsp;coefficients\u0026nbsp;not\u0026nbsp;systematic\u0026nbsp;identify the appropriateness between Chi-square value: 8.43\u003c/p\u003e\n\u003cp\u003efixed and random effect estimators p-value = 0.0379\u003c/p\u003e\n\u003cp\u003eBreusch and Pegan Lagrange \u003cem\u003eH\u003csub\u003e0\u003c/sub\u003e\u003c/em\u003e:\u0026nbsp;Var\u0026nbsp;(u)\u0026nbsp;= 0\u003c/p\u003e\n\u003cp\u003emultiplier test: to identify the Chibar 2 (01) value = 150.40 appropriateness between random and p-value = 0.000 pooled OLS estimators\u003c/p\u003e\n\u003cp\u003eAbbreviations: GDP(pc)- Gross Domestic Product per capita (current US $); DGGHE (% of GDP)- Domestic Government General Health Expenditure (% of Gross Domestic Product); LE- Life Expectancy at Birth; IMR- Infant Mortality Rate 1000; LF- Labour Force, Total.\u003c/p\u003e\n\u003cp\u003eThe test such as Hausman specification and Breusch \u0026amp; Pagan Lagrange multiplier confirmed that random effect estimator (REE) is more appropriate than pooled OLS and again the fixed effect estimator (FEE) is more useful than random effect (REE) in this estimate. The REE is significant with an R-square value of 73.60%, and F-statistic value of 448.37. The R-square value indicates that 73.60% of the change in GDP (pc) (proxy for economic growth) is due to changes in the regressors or explanatory variables used in the study and the F test value indicate that the overall model is highly significant with 1% level of significance. Table 4 also shows that DGGHE (% of GDP) has positive and significant impact on GDP(pc). If the DGGHE increase by 1 percent then GDP (pc) increase by an average of 444.52% (significant at 5%). Life expectancy at birth shows a positive impact on standard of living and significant at the level of importance of 1% (p = 0.000). At the 1% importance level, it is estimated that if life expectancy increase by 1 year, GDP(pc) will increase by 6355.62 on average [significant at 5%). The effect of Infant mortality rate is significantly negative on GDP (pc). For every 1% decrease in IMR, estimate that GDP (pc) will increase by an average of 8.15% (significant at 5%). But the Labour Force has no significant impact on GDP(pc). It may be due to the fact that there was a lack of labour force or more labour but lack of productivity in SAARC countries which lead to the variation in result. Thus, the results of the present study looks similar to the findings of the studies of [2]; [6]; [12]. That is, low expenditure on health lead to low growth of labour force which in turn affect the GDP (pc) of the SAARC countries.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe present study investigated the cause-and-effect relationship between health expenditure on economic growth in the SAARC region using panel data from 2002 to 2020. Additionally, an effort has been made to investigate the relationship between the region's economic growth and the trends of health spending. The unit root test has been used to assess the stationarity of the data. It has been observed that majority of the countries of SAARC region have stable GDP per capita. The GDP per capita of Maldives' has been increasing quickly. Apart from that, it has been noted that the majority of SAARC nations, that is, Afghanistan, Bangladesh, Bhutan, India, Nepal, Pakistan, and Sri Lanka have stable domestic government general health expenditures (DGGHE) as a percentage of GDP. The DGGHE is extremely unstable only in the Maldives. Once again, all SAARC nations have shown increases in life expectancy at birth (LEB). Afghanistan had the lowest LEB and the Maldives the highest. For all SAARC nations, the infant mortality rate (IMR) has been falling and Maldives recorded lowest IMR value. The Panel regression revealed that the SAARC region's GDP (pc) is significantly boosted by DGGHE, IMR, and LEB. The study further concluded that LF has no discernible effect on GDP (pc). It could be because SAARC countries have poor labor utilization rates.\u003c/p\u003e\n\u003cp\u003eThe current study suggests that in order to boost economic growth, the governments of SAARC countries should enhance health expenditure. The government should simultaneously concentrate on allocating its health spending in an efficient manner. Moreover, the workforce is crucial to a nation's economic progress. For which, the government should place greater emphasis on labour by offering adequate training and effective techniques, which will ultimately boost overall development.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e Authors would like to thank the Department of Economics, Nagaon University, Nagaon, Assam for their facilities and support in conducting this project work.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThe author (s) did not receive support from any organization for the submitted work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declaration:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors declare no potential competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish declaration:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution Declaration:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe first author or the corresponding author designed the idea of the study and drawn the skeleton of the manuscript and finalise the paper after thorough review. The second author collected data, reviewed the literatures, analysed the data and written the draft of the paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u003c/strong\u003e The data used in this article will be made available upon reasonable request to the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGujarati DN, Porter DC, Pal M. (2009). Basic Econometrics [Vol. 6). Mc Graw Hill.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBakare AS, Olubokun S. Health Care Expenditure and Economic Growth in Nigeria: An Empirical Study. J Emerg Trends Econ Manage Sci. 2011;2(2):83\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMehrara M, Musai M. Health Expenditure and Economic growth: An ARDL Approach for the Case of Iran. J Econ Behav Stud. 2011;3(4):249\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElmi ZM, Sadeghi S. Health Care Expenditure and Economic Growth in Developing Countries: Panel Co-Integration and Causality. Middle-East J Sci Res. 2012;12(1):88\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlhowaish AK. Healthcare Spending and Economic Growth in Saudi Arabia: A Granger Causality Approach. Volume 5. International Journal of Scientific \u0026amp; Engineering Research; 2014.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBedir S. (2016). Healthcare Expenditure and Economic Growth in Developing Countries.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePiabuo S, M., Tieguhong JC. Health Expenditure and Economic Growth \u0026ndash; a review of the literature and an analysis between the economic community for central African states (CEMAC) and selected African countries. Health Economics Review; 2017.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eErcelik G. The Relationship between Health Expenditure and Economic Growth in Turkey from 1980\u0026ndash;2015. J Politics Econ Manage. 2018;I1:1\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIslam MT, Karim DE. (2019). UPDATE: A Research Guide on the South Asian Association for Regional Cooperation [SAARC).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIfa A, Guetat I. The short and long run causality relationship between public health spending and economic growth: Evidence from Tunisia and Morocco. J Economic Development. 2019;44:3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eModibbo HS, Saidu AM. (2020). Health Expenditure and Economic Growth Nexus: A Generalised Method of Moment Approach for the Case of Selected Africa Countries. Lapai Journal of Economics; Volume 4 (1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIslam MS. Human capital formation and economic growth in South Asia: Heterogeneous dynamic panel cointegration. Int J Educ Econ Dev. 2020;11:4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSethi S, Mohanty S, Das A, Sahoo M. Health Expenditure and Economic Growth Nexus: Empirical Evidence from South Asian Countries. Global Business Review; 2020.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOdhiambo NM. Health expenditure and economic growth in Sub-Saharan Africa: an empirical investigation. Development Studies Research; 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQehaja SS, Hoti A, Marovei E. The relationship government between health expenditure and economic growth: Evidence from western Balkan countries. Int J Appl Econ Finance Acc. 2023;15(1):10\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHooda SD. (2020). Trends of Healthcare Expenditure Indicators: A Study of Selected States in India. Palarch\u0026rsquo;s J Archaeol Egypt/Egyptology 1712).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJunhao Y. (2023). Analysis on Relationship Between Economic Growth, Public Health, and Medical Service Level in China Based on One-Dimension Regression Model Z. Zhan editors: EIMSS 2022, AHCS 7, pp.4\u0026ndash;13, 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhan B. Determinants of Public Health Expenditure In Some Selected States of India: A Panel Data Approach. J Posit School Psychol. 2022;6(10):1636\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDas T, Das D. Does the augmentation of monetary and non- monetary factors prerequisite for the improvement of health outcomes? Evidence from the Indian states. Int J Health Plann Manag. 2021;37[21131\u0026ndash;56. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/hpm.3397\u003c/span\u003e\u003cspan address=\"10.1002/hpm.3397\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Health Expenditure, Public Health, SAARC, Labour force, GDP (pc), Economic Growth","lastPublishedDoi":"10.21203/rs.3.rs-8523360/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8523360/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eExploring the nexus between government healthcare expenditure and economic growth remains a critical area of inquiry. Existing literatures consistently highlights an opposite relationship between Infant Mortality Rate (IMR) and GDP per capita, underscoring the importance of health outcomes in shaping economic performance. In this context, the present paper explores the nexus between public health expenditure and economic growth across SAARC nations, using secondary data from the World Development Indicators. Employing panel regression techniques and various statistical tools, the analysis reveals that Domestic General Government Health Expenditure (DGGHE), IMR, and Life Expectancy at Birth (LEB) exert a statistically significant positive influence on GDP per capita in the region. Conversely, Labour Force (LF) was found to have no significant impact on GDP per capita, potentially reflecting underutilization of labour resources within SAARC economies. On the basis of the findings, it has been recommended to increase government investment in the health sector and strategic initiatives to enhance labour force participation and productivity.\u003c/p\u003e","manuscriptTitle":"Investigating the Nexus between Government Health Expenditure and Growth Across SAARC Nations","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-18 17:20:40","doi":"10.21203/rs.3.rs-8523360/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-24T09:45:20+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-14T17:51:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"78869289266508614996163982462872190945","date":"2026-02-14T16:30:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-14T10:26:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"88532770852002916056681629372904980514","date":"2026-02-13T21:57:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"196212557997808562702021091989360166665","date":"2026-02-13T07:07:28+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-13T06:02:25+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-14T17:52:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-09T09:02:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-09T08:59:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Public Health","date":"2026-01-05T16:07:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5ab5ab16-dc48-4ee3-8100-f78d0ddc64a7","owner":[],"postedDate":"February 18th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-16T19:39:10+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-18 17:20:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8523360","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8523360","identity":"rs-8523360","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}