Investigating the role of foreign aid, FDI, and remittance on the public health of selected South Asian countries

Hasan, Md. Atik; Suborna, Shabikunnahar; Urbee, Afrida Jinnurain Article Investigating the role of foreign aid, FDI, and remittance on the public health of selected South Asian countries Research in Globalization Provided in Cooperation with: Elsevier Suggested Citation: Hasan, Md. Atik; Suborna, Shabikunnahar; Urbee, Afrida Jinnurain (2025) : Investigating the role of foreign aid, FDI, and remittance on the public health of selected South Asian countries, Research in Globalization, ISSN 2590-051X, Elsevier, Amsterdam, Vol. 10, pp. 1-12, https://doi.org/10.1016/j.resglo.2025.100268 This Version is available at: https://hdl.handle.net/10419/331191 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Investigating the role of foreign aid, FDI, and remittance on the public health of selected South Asian countries Md. Atik Hasan a,1,* , Shabikunnahar Suborna b , Afrida Jinnurain Urbee a a Faculty of Department of Economics, Noakhali Science and Technology University, Noakhali 3814, Bangladesh b Department of Economics, Noakhali Science and Technology University, Noakhali 3814, Bangladesh ARTICLE INFO Keywords: Health quality index Health expenditures Foreign aid Remittances Foreign direct investment Globalization South Asia ABSTRACT Foreign aid, remittance, and foreign direct investment have a significant role in shaping and promoting globalization and these factors also play a vital role in determining health quality in developing countries. Developing countries, especially South Asian countries still need research and policies to efficiently utilize the contributions of these external capital sources in their health sector. For this reason, the present study examined the effects of different globalization-related factors (remittances, foreign direct investment, foreign aid) and health spending from 2000 to 2020 on the quality of healthcare in six South Asian countries: Bangladesh, India, Nepal, Pakistan, Maldives, and Sri Lanka. Moreover, this investigation introduces an unprecedented facet to the realm of health sector research by introducing a novel health quality index that incorporates life expectancy, newborn mortality rate, maternal mortality rate, and illness prevalence (specifically tuberculosis). This study used Augment Mean Group (AMG) estimation for data analysis. To ensure the precision and dependability of the findings, this research utilizes sophisticated statistical methodologies, including the Common Correlated Effect of Mean Group (CCEMG), Driscoll-Kraay Robust Standard Error approaches, and Dumitrescu and Hurlin (D-H) causality test, thereby establishing their dependability. The findings of the study demonstrate that foreign aid and health spending have a significant beneficial impact on the health quality of South Asia. In contrast, remittances tend to harm health quality. Furthermore, the influence of FDI on the quality of health in South Asia is equivocal. South Asian countries must allocate more of their budget to the health sector and ensure that foreign aid is properly utilized for its development. On the other hand, these countries are required to take policy and create an environment that will help to improve health quality through effective use of remittance and FDI. 1. Introduction A nation’s healthcare system is crucial for its economic growth (Islam et al., 2020) and social welfare. A healthy population can actively participate in the economy and drive growth, while poor health reduces productivity and strains public resources. Even health spending itself is positively associated with economic growth (Islam &Alhamad, 2022), but In South Asia, the health sector is one of the least essential sectors to look after. Average spending on the health sector in South Asia is lower than 3 % of the country’s budget (Bidin, 2017). This low health spending can be covered by wise use of the capital inflow from external factors. That is why the relationship between economic factors like foreign aid, FDI, remittance, health expenditure, and health outcomes has become a crucial area of study in developing nations, particularly in South Asia. Researchers have been paying attention to the impact of foreign aid, remittance, and FDI on human development metrics, such as health quality and healthcare service accessibility. This issue has garnered attention from policymakers and academics alike in recent times. Although there have been efforts to improve public health in low and middle-income countries through foreign aid, FDI, and remittances from migrant workers, the impact of these economic factors has been contested in existing literature. It remains unclear whether these factors alone can be credited for societal gains in public health or whether other determinants of health also play a significant role. Further research is needed to understand better the specific impact of these economic elements on advancing health systems in these countries. Foreign direct investment (FDI) and remittances are two external financial flows, which are important in mitigating economic growth and * Corresponding author at: Faculty of Department of Economics, Noakhali Science and Technology University, Noakhali 3814, Bangladesh. E-mail addresses: [email protected] (Md.A. Hasan), [email protected] (S. Suborna), [email protected] (A.J. Urbee). 1 0009-0000-3919-6993. Contents lists available at ScienceDirect Research in Globalization journal homepage: www.sciencedirect.com/journal/research-in-globalization https://doi.org/10.1016/j.resglo.2025.100268 Received 28 September 2024; Received in revised form 30 December 2024; Accepted 2 January 2025 Research in Globalization 10 (2025) 100268 2 health outcomes’importance, particularly in developing nations (Islam, 2024). But like any other thing, human capital development is essential for economic progress (Islam &Alam, 2023; Islam &Muneer, 2018; Islam &Shindaini, 2022), and personal remittances frequently enhance family income, increasing household access to healthcare services (Islam, 2022, 2020b, 2024; Islam &Alam, 2023). Furthermore, research shows that remittances help narrow the gap in receiving health care and education by reducing inequality and helping to positively impact income distribution (Islam &Azad, 2024; Islam &Keramat, 2012). A study of remittance-sending countries shows that effective governance helps maximize the remittance benefits by directing them toward productive sectors such as healthcare and sustainable energy (Hasan et al., 2019; Islam, 2020a; Pradhan &Khan, 2015). FDI can facilitate technology transfer and infrastructure development –which include health facilities. FDI has a role in economic growth, income redistribution, and improving healthcare services (Nagel et al., 2015). FDI and remittances help recipient nations participate in the global market and the capital flows into the country (Shahid et al., 2021). Improving health outcomes is still a significant development concern in South Asia due to the region’s ongoing health system inadequacies and uneven progress toward essential health goals. To make informed and evidence-based reforms, it is crucial to comprehend the primary determinants that impact the functioning of the healthcare system. South Asia’s inadequate health systems and variable progress toward SDG target attainment require priority attention to improve health outcomes. Therefore, policymakers are working towards understanding and enhancing the health outcomes of the population in South Asia. Significant health issues, notably high rates of infectious illnesses, malnutrition, and maternal and infant mortality, exist in this region. Bloom et al. (2004) analyze how important economic factors impact the quality of health across six major economies in South Asia. Rahman et al. (2013) examine the factors that contribute to high healthcare costs and financial disasters associated with healthcare in Bangladesh and make urgent recommendations for lowering the burden of out-of-pocket expenditures to lower levels of catastrophic health spending. Progress across the region has been uneven despite improvements in life expectancy and other health indicators (Burns et al., 2017). In addition, healthcare systems are still facing financial insufficiencies and difficulties in keeping up with the increasing demand for their services (Prinja et al., 2017). South Asian governments, as lower-middle-income economies, have limited fiscal capacity to invest in healthcare. In the context of the health challenges faced by South Asia, it is crucial to use all financing sources effectively to improve the performance and quality of healthcare systems. External financing plays an important role. A health quality index is a metric that evaluates and quantifies the effectiveness of healthcare systems or the state of health in a community. That evaluates multiple factors, such as population health outcomes, access to care, and the efficiency of the health system. An effective healthcare system can be measured by a higher score on the health index, which represents better life expectancy, lower frequency of illnesses, and fewer preventable deaths. This demonstrates that the population’s health status improvements are partly due to broader socioeconomic progress. HQI is a better measurement of the health status of a country than several other single variables like mortality rate, infant mortality, life expectancy, etc., because this variable shows a single dimension of a country’s health sector. In contrast, HQI is the metrics that merge all these dimensions. Fig. 1 shows the improvements in the health quality index in South Asia. Healthcare expenditures provide the necessary funding to enhance the accessibility and quality of healthcare services (Bhalotra, 2007; Bein et al., 2017; Mohapatra, 2022; Rahman et al., 2018). Enhancing health results is a significant policy concern, particularly in environments with limited resources. It is thought that health spending affects how well the health system functions. Empirical evidence, however, has produced conflicting findings about the connection between health outcomes and medical costs. One reason is that corruption and inefficiency may hinder the performance of further investments. Second, poor diet, hygiene, education, and economic development can hinder the progress of health. Lastly, gains in marginal spending may be offset by the significant disease burden from HIV/AIDS, malaria, and other infectious diseases. Despite this, there is still a lot of debate around these fundamental causes. As there is a bidirectional relationship between economic growth and health spending (Islam, 2020a,b,c), lower GDP growth in South Asian countries means lower health spending. This is why external economic factors like foreign aid, FDI, and remittance are essential to improve the health quality of this region. The link between foreign direct investment (FDI) inflows and health quality has important implications for economic growth and development. Research on how globalization affects health outcomes, particularly increased foreign investment flows, has been vital. In both industrialized and developing nations, Regarding the connection between inward FDI stocks and population health quality, there are still Fig. 1. HQI of South Asia for the selected countries (). Source: calculated by authors Md.A. Hasan et al. Research in Globalization 10 (2025) 100268 3 contradictory findings. FDI growth can affect health in several ways. Public health expenditures and access to high-quality treatment may rise as a result of FDI-related development and higher earnings. Also, the availability and delivery infrastructure of medical technology are significantly improved by foreign direct investment (FDI) (Burns et al., 2017). However, foreign direct investment (FDI) may also contribute to the spread of illness or have unfavorable environmental effects that could worsen medical issues (Baker et al., 2014; Barlow et al., 2017; Labont´ e, 2019a).Therefore, the overall consequences probably rely on the socioeconomic circumstances unique to each nation. Remittances can increase an individual’s purchasing power for healthcare expenses (Rahman et al., 2013). Remittances from migrants to their home countries, particularly developing nations, have been shown in several studies to boost local populations’health results. Studies have indicated that these remittances, particularly in lowand middle-income nations, can considerably lower newborn mortality rates and enhance the health of children. Improving health outcomes and their quality in developing regions is a significant consequence of foreign aid that is allocated toward health programs. Remittance can affect health not only in a direct way but also in indirect ways. For example, remittance inflow helps to raise GDP (Islam &Shindaini, 2022), and higher GDP may lead to improved health quality. The impact of foreign aid on the health of developing nations has been an area of global debate over the past several decades. The question is whether additional funding and health-related initiatives will result in better healthcare quality and access. Aid can provide a boost to domestic health spending and resources, which can lead to higher allocations to health systems and improved access to high-quality care (Botting et al., 2010; Johri et al., 2012). Assistance can also enable the development of enhanced health infrastructure, transfer of medical technology, improved surveillance programs, and a more capable health workforce. Also, foreign aid allows governments to fund public health programs they cannot afford otherwise, and recipient governments are unable to afford it (Farag et al., 2013). But despite receiving a lot of aid, there hasn’t been any noticeable improvement in terms of health and other health-related indicators for many developing countries, especially from Africa and South Asia. Adhikari et al. (2018) suggest that rather than depending on middlemen, foreign funding should be directed at bolstering and assisting the domestic healthcare system. Many individuals think that foreign aid may help developing nations with their health issues. On the other hand, there is disagreement over the contribution of aid to economic growth and its ability to improve health outcomes. Fig. 2 shows the link between economic and globalization factors with good health. This research will enhance the discourse on strengthening health systems and population health in South Asia. The previous study by Mohanty and Behera (2020) investigated the correlation between healthcare costs and patient outcomes. However, this study included more economic variables in its analysis. It’s essential to understand the connection between external finances and health, as this can help with deciding which areas need funding the most, prioritizing global development partnerships, and improving investment promotion strategies. South Asia faces significant health challenges, so it’s crucial to make the most of all funding sources to improve the performance and quality of healthcare systems. This study primarily utilizes the Health Quality Index (HQI) as the dependent variable, reflecting a holistic measure of health system effectiveness over time. Using a composite metric like HQI, which consolidates multifaceted health domains into a standardized score, helps evaluate overall performance. This study makes use of panel data from the years 2000 to 2020, concentrating on the six largest South Asian nations: Bangladesh, India, Pakistan, Sri Lanka, the Maldives, and Nepal. These nations were chosen because they account for more than 95 % of South Asia’s population and GDP, as well as data availability. The main objective of this study is to provide helpful information for health policy decisions in South Asia. About the South Asian economies, the research will specifically examine how health spending, and globalization-related factors (foreign direct investment, remittance inflows, and development aid for health) have affected the health quality index between 2000 and 2020. By examining the relationship between these economic factors and health performance measures, the study aims to prioritize funding areas that have had the most significant impact in the past. The development of a novel health quality index to fully capture the actual contribution of dependent variables on health quality is the second purpose of this study. Because only life expectancy or mortality is inefficient in capturing the actual health quality of a region. The inclusion of policies regarding the development of regional and national health finance strategies is the third goal. The study intends to support policymakers in improving resource allocation to optimize future health benefits by quantifying the historical correlations between observed health quality and external health spending. This study is expected to facilitate health policy and governance dialogues in South Asia by providing a clear understanding of the context of development financing. The Novelty of this study is embedded in that, it introduces a unique health quality index to capture the health quality of South Asian nations. Along with this, this study used robust approaches that effectively capture the complex dynamics and heterogeneity of health quality with health spending, foreign direct investment, and remittance inflows. We use the augmented mean group (AMG) estimator, the common correlated effect of mean group (CCEMG), and the Discroll and Karry standard error approach. All of these methods can handle the cross-sectional dependency and slope homogeneity problems of the data. With the help of this methodology, we can investigate the underlying mechanisms as well as the statistical relationships between the chosen variables. Our goals are to give policymakers helpful information and a thorough understanding of the ways that foreign aid affects health outcomes. The remaining part of this study is organized as follows: Section 2 briefly discusses past literature related to this study whereas Section 3 represents the estimation strategies of this study. Results from data analysis are represented in Section 4. Section 5 states a brief discussion of estimated results. A causality analysis is also illustrated in section 6. The conclusion and policy recommendation based on this study’s findings are discussed in section 7. Finally, Section 8 demonstrates the limitations of this study and future research guidelines on this topic. 2. Literature review Several studies have been conducted to analyze how different economic factors, such as health spending, foreign direct investment (FDI), remittances, and foreign aid, affect the standard of health in developing nations. This section will summarize those previous discussions. Fig. 2. Link between the selected variables. Md.A. Hasan et al. Research in Globalization 10 (2025) 100268 4 2.1. Health quality and health expenditure Nexus A study by Mohapatra (2022) found that health expenditure positively affects health outcomes in the SAARC region. In the SAARCASEAN area, overall health spending, state health spending, and private health spending all greatly lowered newborn mortality rates (Rahman et al., 2018). According to the findings of Akbar et al. (2021), public health expenditure helps to minimize the infant mortality rate. Alziyani and Bein (2021) and Bein et al. (2017) discovered that public health initiatives are essential, and more health spending is a significant contributor to longer life expectancies. A review by Jutkowitz (2009) discovered mixed findings, with some research demonstrating a negative correlation between Medicare spending and care quality and others finding a positive correlation between total healthcare costs and health quality. Barber et al. (2017) review previous research that used amenable mortality as a proxy for access to and quality of healthcare. Increased health spending should lead to improved access to highquality care, but the empirical evidence shows conflicting results. According to this review paper, previous cross-national research in the OECD found only weak correlations between health spending and amenable mortality. In the meantime, research by Akinkugbe and Mohanoe (2009) Dickson et al. (2021); Dieleman et al. (2020) demonstrated that increased public health investment resulted in lower infant mortality and longer life expectancies. However, no significant causal correlations were discovered between various health spending variables and health outcomes in Africa by other analyses. Among these are research works by (Baldacci et al., 2003; Gyimah-Brempong &Wilson, 2004; Novignon et al., 2012). All of the above studies point to the need for careful management and balancing health spending with other issues, even though it can contribute to better health. 2.2. Health quality and FDI Nexus Many studies have examined how foreign direct investment (FDI) and health are related, utilizing indicators like life expectancy and newborn mortality rates. Research on developing nations revealed that foreign direct investment (FDI) increased health spending and results, but some studies also found that adverse environmental effects offset these advantages. Immurana (2020) find out foreign direct investment (FDI) has a favorable effect on health outcomes, including life expectancy and death rates. They propose that to enhance health outcomes, such as life expectancy and mortality rates, countries should concentrate on luring more foreign direct investment (FDI). Both Immurana (2020) and Shahid et al. (2019) also discovered a favorable correlation between foreign direct investment (FDI) and health since FDI raises life expectancy and lowers the death rate. However, a conflicting result was also found regarding the impact of increasing FDI on health quality indicators and its outcomes when this link was examined in developing countries, especially those in South Asia (Shahid, 2021). Research from developed countries indicates that because of employment uncertainty, more significant FDI may be harmful to public health, economic inequality, and psychosocial stress (Chiappini et al., 2022a). FDI appears to have a favorable impact on health at lower income levels but a detrimental impact at higher income levels, according to Nagel et al. (2015), who indicates that this relationship is nonlinear. Emphasizing the role of population health in luring foreign direct investment (FDI) and the correlation between improved health and FDI inflows into lowand middle-income nations (Maiti &Bidinger, 1981). The connection between population health and foreign direct investment (FDI) in lowand middle-income countries (LMICs) has sparked a lot of interest in the literature and conversation. Despite the widespread perception that economic expansion has positive long-term effects on health, the impact of short-term macroeconomic adjustments, particularly those brought on by foreign direct investment (FDI), on health is not widely recognized (Burns et al., 2017). Zhang et al., (2023) examine the association between foreign direct investment (FDI) inflow and the quality of population health in China. According to the findings, FDI helps to improve health quality when they are invested in carbonminimizing projects. 2.3. Health quality and foreign aid Nexus According to Bendavid and Bhattacharya (2014), each 1 % increase in health aid was associated with improvements in life expectancy and under-5 mortality. And the association between health aid and health improvements strengthened over time. Health assistance has improved life expectancy and child mortality rates, according to cross-country assessments (Akinbode et al., 2021; Asiama &Quartey, 2009; Rashed et al., 2024). Health results are significantly and favorably impacted by foreign aid, especially in nations with solid institutional quality (Zulaikha, 2016). It has been discovered that assistance lowers the productivity cost of illness, especially in regions adjacent to assistance initiatives (Odokonyero et al., 2018). Investments in essential medical technology, disease surveillance, health worker training, and infrastructure can enable developing countries to shore up access to highquality care (Burns et al., 2017; Lim et al., 2023). Mishra and Newhouse (2009) looks at the connection between health outcomes in developing countries and foreign help, specifically health aid. Foreign aid had little to no effect on population health overall since 2000, with only a minimal improvement in life expectancy observed. 1 % increase in foreign aid, life expectancy goes up by 0.004 %. Findings showed that although there isn’t enough evidence to draw a definite connection between help and economic growth, the study does show that foreign aid can improve health outcomes (Toseef et al., 2019). Nonetheless, there is a complicated relationship between globalization, health, and aid, with a negative correlation between the three at high levels of globalization overall (Welander et al., 2012). Institutional risks also affect the effectiveness of aid in the health sector (Maruta et al., 2020). Supporters of foreign assistance assert that it may improve the delivery of health care and save lives through programs like immunization campaigns. At the same time, opponents contend that it may have unfavorable effects like encouraging reliance or being dispersed inefficiently. 2.4. Health quality and remittance Nexus Recognizing the vital contribution that personal remittances make to bettering health outcomes is essential, especially in countries facing economic hardship. Shafiq and Gillani (2020) assessed how remittances affected child health and concluded that personal remittances have a beneficial impact on child health in developing nations. Terrelonge, (2014) shows that the rise in remittances might be partly responsible for the decrease in baby and child mortality rates. Between 1995 and 2009, remittances and public health spending in poor nations increased more than two-fold. However, child and newborn mortality in these nations decreased by 33.5 % and 30.9 % respectively during the same period (Terrelonge, 2014). It has been discovered that remittances have a favorable effect on health outcomes, such as in the case of babies and under-5 mortality rates (Zulaikha, 2016). They also contribute to lowering newborn mortality, raising life expectancy, and raising achievement in elementary and secondary (Zhunio et al., 2012). Remittances have been connected to higher health costs in Ecuador, as well as the cost of prescription drugs during illness and preventive treatments like immunizations and deworming (Ponce et al., 2011). Remittances boost health outcomes in developing countries, with governance and maternal education identified as crucial routes, according to a recent study utilizing a panel vector autoregressive model (Djeunankan &Tekam, 2022). Lindstrom and Ramírez (2010) discovered that remittance-receiving households might put consumption ahead of investments in health and education, which could have a detrimental impact on health, especially for children. A recent study conducted in Bangladesh by Pradhan and Khan (2015) investigated the Md.A. Hasan et al. Research in Globalization 10 (2025) 100268 5 correlation between remittance earnings and health quality. The study’s conclusions demonstrated a long-term causal link between remittances and the Human Development Index (HDI). This implies that remittances eventually result in better living conditions. It is worth noting that remittances are a reliable predictor of healthy functioning and demonstrate a strong and nonlinear relationship with healthy functioning (P˘ aunic˘ a et al., 2019). Table 1 represents the summary of the literature review. 2.6. Literature gap Numerous studies have already been conducted on the health sector outcome in South Asia. However, none of those researchers try to create a health quality index. Somewhat different, the literature tries to calculate health outcomes by considering different proxy variables like child mortality, maternal mortality, and life expectancy. However, this study, for the first time, tries to create a unique health quality index by using child mortality, maternal mortality, life expectancy, and disease prevalence. The existing literature fails to adequately explain the link between external capital inflow-related variables and health outcomes in South Asian countries. The effects of government health spending, FDI, foreign aid, and remittances on health outcomes have been examined in the past, but separately. To better understand how external economic factors, affect public health in South Asian countries, it is crucial to investigate how household-level remittance inflows, industrylevel FDI inflow, and international aid interact with government policies and healthcare investments. By doing so, we can gain a more comprehensive understanding of the mechanisms that impact population health in this region. 3. Methodology 3.1. Data and sources 3.1.1. Development of health quality index Several previous studies tried to model health quality with economic aspects. Still, one of the significant gaps in the earlier studies was the determinant of health quality that they used, which was a poor representation of a country’s health sector quality. Most of the previous studies use dependent variables like life expectancy, maternal mortality rate, or disease prevalence. However, one single variable can’t express the real health scenario of a country. That’s why this study makes an index by combining all of this. To do this study, I followed the following procedure: Step 1: After collecting data on life expectancy, infant mortality, maternal mortality, and tuberculosis prevalence, we take the inverse of these to transfer them into positive attributes. Now, the variables become life expectancy, maternal survival rate, infant survival rate, and tuberculosisfree population. All of them show positive attributes. (continued on next column) (continued) Step 2: As the ranges of all these variables were different, this study applied a widely used standardization method to convert all the variables within the same range. Step 3: After standardization, this study applied the principal component analysis (PCA) method to create a unique health quality index. 3.1.2. Data definition Our study’s econometric estimate was predicated on secondary data, namely the World Bank’s WDI and Our World in Data sources. We examined vital factors such as FDI inflow, personal remittances received, foreign aid, and health spending as independent variables to find out what affects health outcomes. We confined our study and examined data for South Asian nations (Bangladesh, India, Maldives, Nepal, Pakistan, and Sri Lanka) from 2000 to 2020, as data for Afghanistan and Bhutan were not available. The information is yearly panel data—a more thorough analysis of the data for South Asian economies from 2000 to 2020. Four primary variables were selected to measure the inputs of the healthcare system, which are independent of each other. These include Health expenditure (HE), which is the amount spent on medical care by the public and private sectors. Foreign aid (FA) represents the net official development assistance for health, which is external financing. Remittances are defined as personal payments made by foreign workers back to their home nations in South Asia. This serves as an additional source of health financing. Foreign direct investment (FDI) inflows are capital investments from abroad that are linked to healthcare capacity. A list of the variables and their definition are given in Table 2. 3.1.3. Summary statistics The variable’s descriptive statistics from 2000 to 2020 are shown in Table 3. Standard deviations, max, min, skewness, kurtosis, sum, sum, Table 1 Summary of the literature. Literature Country Period Method Variable Findings (Immurana, 2020) Africa 1997–2017 IVFE and GMM FDI and Health Outcome positive (Zhang et al., 2023) China 1980–2020 VECM FDI and Health Outcome Positive (Shahid et al., 2019) South Asia 1990–2016 Fixed Effect (FE) FDI and Health Outcome Negative (Chiappini et al., 2022a) 143 countries 1990–2016 Instrumental Variable (IV) FDI and Health Outcome Positive (Shafiq &Gillani, 2020) 132 countries 1980–2015 System GMM Remittance and Child Health Positive (Terrelonge, 2014) 138 developing countries 1995–2009 OLS and 2SLS Remittance and Health Outcome Positive (Pradhan &Khan, 2015) Bangladesh 1981–2011 VECM Remittance and Health Outcome Positive (Asiama &Quartey, 2009) Sub-Saharan Africa OLS and GMM Aid and Health Outcome Positive (Mishra &Newhouse, 2009) 118 countries 1973–2004 OLS and GMM Aid and Health Outcome Negative (Bendavid &Bhattacharya, 2014) 140 countries 1974–2010 OLS Foreign Aid and Health Outcome Positive (Anyanwu &Erhijakpor, 2007) Africa 1999–2004 ROLS and R2SLS Health Expenditure and Child Mortality Positive (Akinkugbe &Mohanoe, 2009) Lesotho 1975–2007 VAR Health Expenditure and Life Expectancy Positive (Mohapatra, 2022) SAARC 1993–2012 GLS Health Expenditure and Health Outcome Positive Table 2 List of all variables. Variable Indicator Measurement Source HQI Health Quality Index This index includes four variables: Life expectancy at birth, Mortality rate (infant), Maternal mortality ratio, and incidence of tuberculosis. World Bank, (2023) HE Health Expenditure Current health expenditure per capita (current US$) World Bank, (2023) FDI Foreign Direct Investment Foreign direct investment, net (Bop current US$) World Bank, (2023) FA Foreign Aid Foreign aid (current US$) Our World in Data, (2023) PRR Personal remittance received Personal remittances received (current US$) World Bank, (2023) Md.A. Hasan et al. Research in Globalization 10 (2025) 100268 6 sq., and means are among them. For every series, developments and observations are available. Outlines some of the variables’most significant features and gives the results of the descriptive statistics. The components have varying average values; remittances have the lowest mean value, while health expenses have the greatest mean value. The skewness of all the variables is positive. While most variables have few outliers and display platykurtosis or negative kurtosis, FDI displays leptokurtosis or positive excess kurtosis. 3.2. Theoretical model This study explores the impact of government health spending on health outcomes in South Asian countries, excluding Afghanistan and Bhutan, using Grossman’s health production model. According to Grossman (2017), the cost of healthcare and other consumer goods can affect people’s satisfaction with producing and consuming health. A lot of other research also uses Grossman’s health production function as their theoretical model, for example (Bala et al., 2022; Bolin &Caputo, 2017; Hwang &Sakong, 2019; Labont´ e, 2019b; Liljas, 1998; Mhlanga, 2021; Novignon et al., 2012), etc. The Grossman model provides a formula for measuring health production function. H=f(x)(1) The equation below shows how each person’s health output is measured. The letter H represents a measure of the health quality index (HQI). The letter X represents a collection of different inputs to the health production function. These inputs in our study include government health expenditures (HE), foreign aid (FA), inflows of foreign direct investment (FDI), and personal remittances received (PRR) in this study as the main focus of this study is to examine the relationship between external economic factors and health quality. In previous studies like Immurana (2020), Shafiq and Gillani (2020), Asiama and Quartey (2009), and Anyanwu and Erhijakpor (2007), the effect of these variables was calculated separately on health outcomes. The H vector represents health outcome or quality, and it may include any factor that can show the scenario of health status. In our study, we include four components in the H vector to represent health quality: incidence of tuberculosis, life expectancy, infant mortality rate, and maternal mortality ratio. However, in previous studies, such as (Chiappini et al., 2022b; Novignon et al., 2012; Pradhan and Khan (2015)), health outcomes were measured by only one or two of these variables. It is possible to define the empirical links between health inputs and health outcomes as follows. HQIit =β0+β1HEit +β2FAit +β3FDIit +β4PRRit + ε it (2) Equation (3) HQI is used to measure the health quality index in this study. This index is determined by several factors such as the incidence of tuberculosis, maternal mortality ratio, infant mortality rate, and life expectancy at birth. Furthermore, foreign aid (FA), foreign direct investment inflows (FDI), health expenditure (HE), and personal remittance received (PRR) are considered independent variables. Here β0is the intercept and β1to β4are slope coefficients for different independent variables and ε it is the error term to measure deviations. 3.3. Econometric estimation 3.3.1. Augmented mean group test To ensure that cross-sectional dependence and parameter heterogeneity are taken into consideration (Sencer Atasoy, 2017), the AMG proposed by (Eberhardt et al., 2010; Eberhardt &Bond, 2009) is used. When analyzing panel data, the Augmented Mean Group (AMG) estimator has several benefits. First off, it efficiently captures both crosssectional variation and time-series dynamics by permitting heterogeneous parameter estimates across individual units while also pooling information across units (Eberhardt &Teal, 2010). Second, by including lagged variables and other control variables, the AMG estimator mitigates possible endogeneity problems and improves the robustness of the estimation results (Othman et al., 2018). Thirdly, the AMG estimator minimizes bias and increases efficiency in predicting long-run coefficients by adding lagged variables to the group mean estimator. This is especially useful when there is a short time-series dimension compared to the cross-sectional dimension (Pesaran &Smith, 1995). How these estimators estimate the unobserved standard components is the primary distinction. The AMG estimator permits cross-sectional dependence by estimating the unobserved joint dynamic impact while accounting for the standard dynamic effect parameter. After adding time dummies to the formula, the first difference OLS is used to estimate. Δyit = α 1i +βiΔXit +φift+∑ T t=2 τ tDUMMYt+ ε it (3) We assign a unit coefficient to every group member to build a regression model that is unique to that group. The AMG estimator is subtracted from the dependent variable to achieve this. The intercept captures the time-invariant fixed effects in each regression. We apply the mean group estimate for AMG. AMG =N−1∑ N i=1 βi(4) Here  βiis the coefficient estimator. 3.3.2. Robustness check This study utilizes three recent panel data estimators to address the cross-sectional dependency and slope heterogeneity problems of the dataset. To assess the consequences of AMG regression, we included the Driscoll-Kraay standard error (D-K SE) method as a robustness test, proposed by (Driscoll &Kraay, 1998). Unlike other regression procedures, this technique yields reliable and immaculate findings even when dealing with CSD. The method is capable of addressing missing values as well (Danish et al., 2019). The D-K standard error is also a useful method for dealing with heteroscedasticity or longitudinal and serial dependence within the paradigm of fixed effects (Danish et al., 2019). Moreover, it applies to both balanced and imbalanced panel data sets, allowing for a more extended period and more flexibility due to its use of Table 3 Summary statistics. HQI HE FDI FA PRR Mean −7.94E-10 135.0841 5.41E +09 8.61E +08 2.76E +08 Median −0.526273 42.23648 7.12E +08 5.79E +08 7.593543 Maximum 3.538248 993.4720 6.44410 3.47709 3.066009 Minimum −3.16603 8.338082 −6647984 5170000. 0.077343 Std.Dev 1.671682 229.4762 1.250010 8.1000008 7.020008 Skewness 0.52974 2.339349 2.765608 1.158262 2.535668 Kurtosis 2.426585 7.257731 9.774297 3.614196 8.281825 Sum −1.00E-07 17020.60 6.81E +11 1.08E +11 3.48E +10 Sum Sq.Dev 349.3152 6582418. 1.95E +22 8.20E +19 6.17E +19 Observations 126 126 126 126 126 Md.A. Hasan et al. Research in Globalization 10 (2025) 100268 7 a non-parametric method. Equation (6) representing the regression of Driscoll Kraay’s standard error is as follows: V( α ) = (XʹX)−1 ST(XX)−1(6) When slope heterogeneity and cross-sectional dependence are key issues to address, the CCEMG estimator is a robust choice in panel data models. Even in the face of those econometric challenges, it delivers unbiased and consistent estimates. To determine the mean group estimator for the CCE, compute the average of every coefficient across all individual regression as described below: CCEMG =N−1∑ N i=1 βi(7) where  βirepresents the coefficient estimations. Finally, this study applied the Generalized Method of Moment (GMM) approach to check whether there is any lagged effect of the dependent variable exists or not. Several notable advantages of the GMM method are available. Firstly, first, it does not need the assumption of normality. Second, the applied model is robust to heteroscedasticity within the model. Third, GMM can provide estimation of parameters even when the model cannot be solved fully analytically, and with some flexibility in how to choose the set of instrumental variables. GMM then, is particularly good at dealing with endogeneity in the dependent variable, the independent variable, or the error term. This study applied the following GMM proposed by (Arellano &Bond, 1991), HQIit =β0+β1HQIit−1+β2HEit +β3FAit +β4FDIit +β5PRRit + ε it (8) 3.3.3. Causality test Granger (1969) created a test to determine whether the variables are causally related. However, it has several shortcomings, such as the test’s inaccuracy when cross-sectional dependency (CSD) is present. For this reason, this study employed a more advanced version of the D-H causality assessment, developed by (Dumitrescu &Hurlin, 2012). As a result, the D-H assessment is applied, which is better at taking CSD into account than the panel Granger causality analysis. One approach to express the D-H panel causality would be as follows: Zin =δi+∑ k k−1 Yk iZi(n−k)+∑k k−1θk iXi(n−k)+ ε in (9) where X and z represent the observables, Yk irepresents an autoregressive parameter, and θk idenotes the estimations of the regression coefficient. Fig. 3 represents the data analysis process and technique of this study. 4. Results and discussion 4.1. Correlation metrics The results of the correlation analysis will show if the variables have a positive or negative connection. Table 4 shows that HE and HQI have a positive connection (0.2340), whereas FA and HE have the greatest and most significant negative correlation (−0.4305). Higher remittances are typically linked to worse health quality, as shown by the statistically significant negative association between remittance received and the health quality index (−0.2540). It is significant to note that the correlation coefficients for all variables are less than 0.70, except for FDI and remittance received. This suggests the absence of multicollinearity. 4.2. Multi-collinearity test The chosen variables show no multi-collinearity, as supported by a variance inflation factor of less than 10. The correlation results also indicate no multi-collinearity, except for foreign aid, which correlates with other independent variables greater than 0.5. The results of the multicollinearity test are stated in Table 5. 4.3. Cross-Sectional dependency test Table 6 represents the cross-sectional dependency test suggested by Pesaran. There is strong evidence that contradicts the null hypothesis of cross-sectional independence for all variables, as indicated by the CDtest statistics and corresponding p-values. The average joint T value is the same (21.0) across all variables. The mean ρ (rho) values, which represent the average pair-wise correlation coefficients between crosssectional units, vary from 0.12 (PRR) to 0.94 (HE). Similarly, the mean abs( ρ ) values, which represent the average absolute pair-wise correlations, vary from 0.37 (PRR) to 0.95 (HQI). All of these findings suggest that there is a significant cross-sectional dependency in the data, with the variables PRR and HQI showing the least cross-sectional dependence among those considered and HE and HQI showing the most. 4.4. Unit root test Due to having CSD in the dataset, this study employs two 2nd generation unit root tests, CADF and CIPS. The 2nd generation unit root test results for the variables PRR, FDI, FA, HE, and HQI are displayed in Table 7. CADF test shows that all the variables except HQI are nonstationary at the level, but they become stationary after taking the first difference. But HQI is stationary at level. CIPS test shows that two variables (PRR and HQI) are stationary at the level, and the remaining variables are stationary at the difference. 4.5. Slope homogeneity test The results of the slope homogeneity test are shown in Table 8. There are significant differences in the slopes of the data between groups or over time, as indicated by the tests for slope homogeneity. the  Δand  Δ adj tests reject the null hypothesis of slope homogeneity, as their pvalues are very low (0.000). It is crucial to consider these variations Fig. 3. Data analysis techniques. Table 4 Correlation test. HQI HE FDI FA PRR HQI 1.0000     HE 0.2340*** 1.0000    FDI 0.1059 −0.1524* 1.0000   FA 0.0175 −0.4305*** 0.5891*** 1.0000  PRR −0.2510** −0.1800** −0.1037 0.3149*** 1.0000 ***p <0.1, **p <0.5, *p <0.10. Md.A. Hasan et al. Research in Globalization 10 (2025) 100268 8 while analyzing and interpreting the data since they imply different correlations between the variables across groups or periods. 4.6. Augmented mean group (AMG) estimator test The AMG estimator results display the estimated coefficients, standard errors, and z-values for the independent variables HE, FDI, FA, and PRR along with the constant component (_cons) in Table 9. The coefficient for HE is −0.0004316 with a z-value of −1.85, and it is statistically insignificant at a 5 % level as the p-value is 0.064. It refers that a rise in health expenditure will causes a small deterioration in heal quality. This may occur due to corruption and mismanagement in health sector in South Asia. Similarly, the coefficient for PRR is −0.0078775 with a zvalue of −2.21, which is statistically significant at 5 % level. This implies that a 1 % increase in PRR is associated with a 0.78775 % reduction in the dependent variable, ceteris paribus. According to the AMG finding remittance contribute positively to rise health standard of South Asian countries although it is very small. In south Asian countries remittance usually not being directed for the improvement of heal sector as heal is the least priority sector in these countries. On the other hand, the coefficient for FDI is 3.94e-12 with a z-value of 1.80, which is statistically significant at a 10 % level with a p-value of 0.071. It indicates that FDI have a small positive role on health quality of South Asia. FDI helps to rise income level and living standard of the people which contribute positively to rise heal quality in South Asian countries. The FA coefficient is 8.83e-11, and the z-value of 3.03 indicates that it is statistically significant at a 1 % level. This means that a 1 % increase in FA is linked to a 0.0000000883 % increase in the dependent variable, given that all other variables remain constant. It implies that foreign aid has small but significant positive role in developing health quality of South Asian countries. Because foreign aid often comes to these countries as a form of medical assistance. Furthermore, the constant term (_cons) has a coefficient of −1.965414, which is statistically significant at a 1 % level with a z-value of −3.22. When all independent variables are zero, this constant term represents the value of the dependent variable. In conclusion, the results indicate that while HE and FDI are not strong predictors in this model, FA and PRR have a significant impact on the dependent variable. 4.7. Robustness test Table 10 presents three distinct techniques for estimating the coefficients of the given variables −GMM, Driscoll-Kraay, and CCEMG. The variables HE, PRR, and FA show statistical significance in all techniques, with PRR having a negative coefficient but HE and FA having a positive coefficient. The variables HE, FA, and PRR are significant at the 1 % levels in both Driscoll-Kraay and CCEMG approaches but the significance varies in the GMM approach. All approaches indicate that the constant term (_cons) is statistically significant. However, it is negative in both Driscoll Kraay and CCEMG tests but positive according to the GMM method. 5. Discussion This study investigates the impact of foreign aid, FDI inflows, remittances, and health expenditures on the health quality index of South Table 5 Variance Inflation Factor (VIF) for Inquiring Multi-collinearity. Variable VIF 1/VIF HE 1.25 0.798973 FDI 1.82 0.550867 FA 2.30 0.434493 PRR 1.29 0.772760 Mean VIF 1.67  Table 6 Cross-Section Dependency Test. Variable CD-test p-value average joint T mean ρ mean abs( ρ ) HQI 6.597*** 0.000 21.00 0.37 0.95 HE 16.613*** 0.000 21.00 0.94 0.94 FDI 8.913*** 0.000 21.00 0.50 0.51 FA 3.713*** 0.000 21.00 0.21 0.38 PRR 2.098*** 0.036 21.00 0.12 0.37 ***p <0.1, **p <0.5, *p <0.10. Table 7 CADF and CIPS unit root test. Variables CADF CIPS Level At 1st Difference Stationarity Level At 1st Difference Stationarity HQI −0.610** I (0) −2.865*** I (0) HE 0.722 −2.142*** I (1) −1.383 −3.966*** I (1) FDI −0.446 −2.937*** I (1) −2.326 −4.488*** I (1) FA 1.114 −1.293* I (1) −1.233 −3.892*** I (1) PRR 1.144 −1.362* I (1) −1.481* I (0) ***p <0.1, **p <0.5, *p <0.10. Table 8 Slope Homogeneity Tests. Δp-value  Δtest 10.951*** 0.000  Δadj test 12.957*** 0.000 ***p <0.1, **p <0.5, *p <0.10. Table 9 AMG estimator. Variable Coefficient Standard error Z-value P value HE −0.0004316* 0.0028407 −1.85 0.064 FDI 3.94e-12* 2.36e-11 1.80 0.071 FA 8.83e-11*** 2.92e-11 3.03 0.002 PRR −0.0078775** 0.003566 −2.21 0.027 _cons −1.965414*** 0.6112609 −3.22 0.001 Wald chi2(2) =4.90 Prob >chi2 =0.0863 ***p <0.1, **p <0.5, *p <0.10. Table 10 GMM, Driscoll Kraay, and CCEMG Test. Variables GMM CCEMG Driscoll-Kraay HE 0.0013832** 0.0047595*** 0.0020681*** FDI −1.47e-12 −8.15e-12 −2.96e-12 FA 1.82e-11* 2.02e-11*** 4.98e-10*** PRR −1.11e-12** −0.0217629*** −6.69e-10*** _cons 0.1024126*** −0.1109265* −0.5076288* ***p <0.1, **p <0.5, *p <0.10. Md.A. Hasan et al.