Skip to main content

Food insecurity, environment, institutional quality, and health outcomes: evidence from South Asia



Food insecurity and environmental degradation pose significant threats to health outcomes in South Asia, necessitating effective policy interventions. Therefore, this study aims to examine the impact of food insecurity and environmental degradation on health outcome indicators amidst global inflationary shocks and institutional quality arrangements. Additionally, it aims to explore the intricate moderating role of institutional quality on the relationship between food insecurity, endogenous variables, and external shocks.


In alignment with the study’s objectives, a set of panel data spanning from 2000 to 2021 is compiled for South Asia. The study introduces a novel variable representing inflationary shock, crafted through the integration of inflation datapoints and the application of the generalized autoregressive conditional heteroskedasticity model. Additionally, a distinctive aggregate institutional quality index is formulated, drawing from six key measures of the Worldwide Governance indicators. To scrutinize the effects of food insecurity, environmental degradation, and other explanatory variables, the study employs the two-step system generalized method of moment technique, offering a robust analytical approach to uncover complex relationships and dynamics in the region.


The results indicate that the prevalence of undernourishment, inequality in per capita calorie intake, and CO2 emissions significantly reduce life expectancy and increase mortality rates. Additionally, it shows that per capita kilocalorie supply, per capita GDP, per capita health expenditures, and urbanization are statistically significant for increasing life expectancy and decreasing mortality rates. The findings reveal that inflationary shocks severely affect food insecurity and environmental factors, exerting further pressure on contemporary life expectancy and mortality rates. In rebuttal, the institutional quality index is found to have significant effects on increasing and decreasing life expectancy and mortality rates, respectively. Furthermore, the institutional quality index is effective in moderating the nexus between food insecurity, environmental degradation, and health outcomes while also neutralizing the negative impact of inflationary shocks on the subject.


The results verify triple health constraints such as food insecurity, environmental factors, and economic vulnerability to global shocks, which impose severe effects on life expectancy and mortality rates. Furthermore, poor institutional quality is identified as a hindrance to health outcomes in South Asia. The findings suggest specific policy implications that are explicitly discussed.


Food insecurity (FI) remains an enduring global challenge, affecting over 9.3% of the world’s population. Nearly 29.6% of individuals currently face severe or moderate FI [1]. The World Bank [2] reports a substantial increase, with over 42% of the global population experiencing unaffordability of healthy food and basic nutritional needs in 2021, compared to 2019. FI is characterized by the inability to physically and economically afford and safe, nutritious food that meets dietary requirements for an active and healthy life [3]. Despite being one of the most fundamental human needs, having sufficient food to eat is a challenge in the real world. Figure 1 illustrates the prevalence of undernourishment, a key proxy for measuring food security (energy intake) worldwide. It highlights three highly undernourished regions—Sub-Saharan Africa, South Asia, Latin America and the Caribbean—compared to the global scenario. According to Fig. 1, Sub-Saharan Africa has the highest hunger rate at 20.9% in 2020, followed by South Asia (15.9%), and Latin America and the Caribbean (7.8%). Notably, South Asia, with its 15.9% hunger rate ranks second globally, exceeding the worldwide rate of 9.3%. As one of the most populous regions, South Asia is home to over 25% of the world’s population, a figure expected to rise by 40% in the next three decades [4].

Fig. 1
figure 1

Prevalence of undernourishment (% of population)

Source: Roser and Ritchie [5] in Our World in Data. The plot has been created by authors

At present, South Asia confronts a multitude of challenges, including draught, escalating food prices, environmental degradation, poverty, internal displacement of people, rapid population growth, high income inequality, and an alarming high prevalence of undernourishment [6]. Figure 2 illustrates that among South Asian countries, Afghanistan bears the highest rate of hunger, followed by Pakistan. Considerably, the rate of hunger has shown a narrowing trend in Nepal, the Maldives, and Bhutan in recent years. However, India and Bangladesh are anticipated to grapple with persistently high hunger rates in the years to come.

Fig. 2
figure 2

Prevalence of undernourishment; South Asia

Notes: AFN: Afghanistan, BGD: Bangladesh, BHT: Bhutan, IND: India, SRL: Sri Lanka, MLD: Maldives, NPL: Nepal, PAK: Pakistan

Source: Roser and Ritchie [5] in Our World in Data. The plot has been created by authors

Furthermore, the region is home to over 40% of the world’s poorest inhabitants, with a headcount poverty ratio of less than $1.25 per day. While the global economy has been slowly recovering from the pandemics, recent political tensions have sparked a higher inflationary episode. Consequently, food prices have risen, and the supply of essential items such as wheat, barley, and sunflower oil has decreased. This surge in poverty stressors has limited access to food items, disproportionately affecting people globally and particularly in South Asia [7]. Evidence demonstrate the adverse effects of FI on human lives, including heightened exposure to chronic diseases, increased mortality rates, diminished mental stability, reduced human reproduction, and an elevated rate of miscarriage [8, 9]. Therefore, aside from managing the direct impact of FI on people’s well-being—with life expectancy and mortality rates being particularly noteworthy [10, 11]—governments and policymakers must address the broader challenge of mitigating the impact of global economic and inflation uncertainties on contemporary FI. This necessitates a comprehensive assessment of the influence of both endogenous FI indicators and the external shocks to pinpoint specific areas where precise policy tensions exist. Numerous studies [12,13,14,15,16,17,18,19] have explored the effects of FI on various health aspects, including life expectancy, mortality rates, chronic health diseases, and women’s pregnancy, across diverse geographical locations. While these studies and many others have predominantly focused on how endogenous predictors explain the subject, there has been a tendency to overlook the externalities that impose spillovers on health outcomes. For instance, Beyene [13] delved into the impact of FI on infant mortality rates and life expectancy in Sub-Saharan Africa. The study expanded on subject-endogenous predictors, including the prevalence of undernourishment, dietary energy supply, personal disposable income, and average schooling years. While these findings are noteworthy, their policy implications for precise policy reorientations may be limited. Therefore, the primary objective of our study is to explore the effects of FI and environmental degradation on health outcomes in South Asia, addressing a domain with an empirical dearth in the existing literature. While filling this gap is substantive for this investigation, the present study further aims to delve into specific areas of policy tension. Particularly, the study formulates five research questions of the present time: First, what is the impact of FI and environmental degradation on health outcomes in South Asia? Second, how do external (global) inflationary shocks impact health outcomes in the region? Third, does institutional quality impart meaningful direct and spillover effects on heath outcome indicators? Fourth, does institutional quality effectively moderate the relationships between FI and subject-endogenous variables? Fifth, does institutional quality modulate the negative effects of inflationary shocks on health outcome predictors in South Asia? Providing evidence-based answers to these questions is not only integral to achieving the study’s primary objectives but also crucial for identifying specific areas that necessitate targeted policy interventions.

The methodology and scope of the study make it a novel contribution to the existing literature. The distinctiveness of the present study’s contributions can be outlined as follows: Firstly, while a substantial body of literature has explored similar topics, South Asia has not been extensively examined in scholarly research, particularly, in the context of a precise and policy-oriented study. This study fills the gap by providing a focused exploration of the region. Secondly, a unique aspect of this study is the development of a novel inflation uncertainty predictor. This tool captures the impact of external inflationary episodes resulting from global political and trade tensions on health outcomes. This innovative strategy not only helps gauge the size and magnitude of the effects of sudden global price inflation but also provides guidance on policy interventions to absorb them. Thirdly, the study distinctively develops an institutional quality index using a distance-based technique. This index measures the influence of institutional quality on health outcomes, emphasizing the variability of exogenous forces that may affect contemporary health outcomes in South Asia. This approach is instrumental in understanding how existing governance responds to catastrophic food security and informs potential policy measures. Fourthly, in case where the existing governance structure does not directly respond to altering the subject, the study extends its analysis to investigate whether institutional quality plays a moderating role in improving the relationships between FI and health outcomes. This examination of the moderating role of institutional quality verifies the variability of macroeconomic, demographic, environmental, and FI predictors, thereby influencing health outcomes in South Asia. In sum, the conclusions drawn from the outcome of the study will enhance the current state of knowledge and help relevant policymakers in South Asian countries.

The subsequent sections of the study are structured as follows: Sect. 2 endeavors to conceptualize the paper and reviews pertinent empirical studies. Section 3 introduces the data, variables, and sources of data compilation. In Sect. 4, a foundational estimation method is established in alignment with the study’s objectives to test the competing hypotheses. Section 5 then presents the results and discusses the empirical findings. Finally, Sect. 6 concludes the article and offers specific policy implications.

Literature review

Conceptual framework

Prior literature has predominantly focused on the health production function (HPF), establishing a conceptual framework that emphasizes endogenous factors such as health expenditures, per capita income, employment, environmental quality, lifestyle, education, and genetics [10, 20,21,]– [22]. This approach traces back to the seminal work of Auster et al. [23], who explored the impact of environmental and healthcare indicators on mortality rates. However, despite the subsequent adoption of a similar pattern by substantial body of literature, most studies have overlooked exogenous factors. These factors include external social and economic shocks as well as institutions, which can either directly or indirectly influence health outcomes measured by mortality rate or life expectancy. In conceptualizing our study, we build upon this foundation, addressing the gap in literature. Figure 3 outlines the conceptual framework (extended HPF) designed for the present inquiry. Line (1) represents the conventional approach, as seen in studies like Onyimadu et al. [24] and Salgado et al. [25], offering for an extensive systematic review of studies examining how endogenous factors influence the subject. Line (2) illustrates how external shocks from global inflationary episodes, causing fluctuations in the general price level of food and non-food items, impact health outcomes. Line (3) outlines the direct effects of institutional quality on health outcomes, while Line (4) emphasizes the moderating effects of institutional quality on endogenous health factors.

Fig. 3
figure 3

Study’s conceptual framework

Source: Authors’ creation

Institutional quality

Institutional quality represents the overall efficiency, reliability, and effectiveness of institutions in an economy [26]. Fundamentally, institutions encompasses rules, policies, and practices that form and instruct the behavior of individuals and organizations in a society [27,28,29]. Institutional quality is a multifaceted concept that gauges that state’s power to govern its resources for the benefit of the nation. According to the World Health Organization [30], states are responsible for designing a country’s health system based on two key pillars: resource production and efficient service provision. This design aims to facilitate the achievement of three objectives, including institutions’ responsiveness, health system efficacy, and the availability of sufficient and just financial and physical resources [31, 32]. To assess the efficacy and quality of institutions, Kaufmann and Kraay [33] developed six indicators, measuring corruption control, the rule of law, government effectiveness, political stability, regulatory quality, and voice and accountability. In essence, the higher these measures, the higher the institutional quality, signifying that a country has a robust and transparent mechanism in place to ensure the fair and efficient utilization of its resources, resulting to positive outcomes for the nations [34].

Review of empirical studies

Our study aligns with prior empirical literature on several fronts, including the health consequences of economic growth, environmental degradation, demography, social factors, and institutional quality. For instance, researchers such as Dadgar and Norström [35], Gautam [36], Niu et al. [37], Spiteri and von Brockdorff [38], Erdoğan et al. [39], Salahuddin et al. [40], Knapp and Wang [41], and Mohapatra [42] have extensively examined the effects of economic growth, utilizing either per capita GDP or GDP growth rate, on health outcome indicators across diverse nations. They employed different statistical methods both at regional and country levels. The collective findings unanimously confirmed that economic growth plays a crucially positive role in influencing health outcomes. This positive influence operates through enhancement of individuals’ economic capacity, enabling them to afford better living conditions, accessing healthcare, and improve their living standards.

Furthermore, the review of literature reveals that numerous studies have delved into the relationship between environmental degradation and health outcome indicators, establishing a general concensus on the negative consequences of increased environmental degradation on population health. Noteworthy among these studies are Gasimli et al. [43], Mumtaz et al. [44], Omri et al. [45], Taghizadeh-Hesary et al. [46], Alimi et al. [47], Clark et al. [48], Li et al. [49], Emodi et al. [50], Zeeshan et al. [51], Murthy et al. [52], and Das and Debanth [53], which specifically explored the health consequences of CO2 emissions, ecological footprint, non-renewable energy consomption, and climate change predictors on mortality rates, life expectancy, and mental health of populations across various countries. Their collective findings consistently suggest that environmental degradation is detrimental to public health. Comparatively, FI, a sensitive topic of policy discussions worldwide, has not recived extensive scrutiny in the exisitng literature. The available studies, conducted by Beyene [13], Benzekri et al. [11], Johnson et al. [54], Dean et al. [55], Seligman et al. [56], Militao et al. [57], Pengpid and Peltzer [58], Nagata et al. [59], and Nwosu et al. [60] have explored the effects of FI on different health outcome indicators, including mental health, life expectancy, infant mortality rates, and per capita health expenditures. Using diverse data sources, these empirical investigations span different countries and regions, excluding South Asia. Despite this diversity, their unanemous findings support the assertion that FI poses an early-stage threat to human well-being, acting as a harbinger for various diseases over time.

While demography, often measured by population growth and urbanization, is considered a health-endogenous factor, recent emprical studies yield mixed responses. For instance, Jemiluyi [61], Tripathi and Maiti [62], Perrott and Holland [63], and de Meijer et al. [64] concluded that growing population rate and rapid urbanization have negative impacts on public health. Within a given per capita income, these factors increase contemporary health expenditures and suppress the overall health outcomes. Conversely, studies conducted by Huang et al. [65] and Shao et al. [66] argue that urbanization is an effective means of increasing life expectancy and reducting infant mortality rates by facilitating people with swift access to better healthcare facilities. Additionally, the review of existing empirical literature reveals that studies conducted by Liao et al. [67], Gumus and Yurumez [68], Raghupathi and Raghupathi [69], and Gottfried and Sublett [70] explored the health effects of social factors, primarily proxied by school enrollment rate across various countries, using diverse statistical methods for their analysis. In consensus, their findings emphasize that the level of education and literacy have a positive impact on life expectancy and negative effects on mortality rates. Finally, the study delved into existing literature and discovered that recent works conducted by Socoliuc et al. [71], Rahman and Alam [72], Vian [73], Van De Bovenkamp et al. [74], De Luca [75], Onofrei et al. [76], Glynn [77], Farag et al. [78], Rosenberg [79], Koller et al. [80], and Hadipour et al. [81], mostly employing the rule of law or control of corruption as proxies for institutional quality, affirm that institutional quality is crucially in promoting positive health outcomes. Essentially, they highlight the importance of governance structure and anti-corruption acts in contributing to the efficacy of healthcare systems.

Research gaps

While recent empirical studies contribute significantly to existing literature, an added dimension would involve examining how contemporary health outcomes relate to externalities. Notably, there is a dearth of studies on the comprehensive effects of institutional quality on health outcomes, covering all aspects of the institutions both as a direct and moderating predictor. This gap is more tangible in the case of South Asian countries. Another gap is the absence, to our knowledge, of studies addressing global economic shocks, particularly global inflationary periods, which could significantly raise food prices, intensifying vulnerability to food security in South Asia. Moreover, apart from Gasimli et al. [43], who investigated the impact of environmental degradation on health outcomes, no other studies were found focusing on this aspect in South Asian countries. To address these gaps and align with our conceptual framework, we propose four key hypotheses: H1: FI and environmental pollution have severe effects on health outcomes. H2: Inflation uncertainty, as one of the key drivers of food price volatility, negatively impacts health outcomes. H3: Institutional quality has a direct link with health outcome indicators. H4: The interaction of institutional quality with inflation uncertainty increases or decreases the effects of endogenous health variables.

Data and variables

Our study covers the period from 2000 to 2021, incorporating the latest available data. The empirical investigation centers on South Asian countries, including Afghanistan, Bnagladesh, Bhutan, India, the Maldives, Nepal, Sri Lanka, and Pakistan. The selection of South Asia as the context of our study is guided by two compelling reasons. Firstly, despite the abundance of studies on the health implications of FI and environmental degradation, the bloc has not received extensive attention in the existing literature. Secondly, the region is at a precarious equilibrium characterized by low staple productivity, minimal returns to formers, supply shortages, highly volatile food prices, area diversification, and low per capita income. These factors collectively contribute to escalating threats of FI on health outcome indicators, yet there is insufficient number of studies to guide contemporary policy directions for South Asia. Therefore, addressing these challenges necessitates a comprehensive analysis of the current situation to inform effective policies and resource reallocation in South Asia.

Selection of variables

Dependent variables

The selection of the variables aligns with study’s objectives and is consistent with prior empirical literature [82,83,84,85,86]. We employ life expectancy at birth (LE), representing the number of years a newborn kid would survive if the prevailing mortality patterns at the time of its birth persist throughout its life. Additionally, we incorporate infant mortality rates (MR), indicating the number of kids who die before reaching one year of age per 1,000 live births per year. In this study, LE and MR are used as dependent variables. While LE represents a broader overview of a nation’s health outcomes, MR is considered as a micro-predictor. It is essential to examine the response of both macro- and micro-health outcome predictors to the explanatory variables.

Explanatory variables

In addition to two innovatively constructed variables for inflationary shocks and the institutional quality index (details in the next section), the study builds upon previous studies [13, 87,88,89,90,91,]– [92] and employs three indicators, namely, prevalence of undernourishment (PN), per capita kilocalorie supply (KS), and inequality in per capita calorie intake (CI), as explanatory variables to measure FI in South Asia. PN is expressed as the percentage of people with insufficient regular food intake to maintain a typical, active life; a data value of 2.5 indicates a malnutrition rate lower than 2.5%. Moreover, KS represents the amount of all types of daily food supplies, measuring the available quantity of food for consumption. CI is expressed as the coefficient of variation of energy intakes, with a higher coefficient indicating greater inequality. These indicators are widely used in literature and are considered as best-fit proxies for measuring FI.

Control variables

To control for the effects of various social, economic, demographic, and environmental factors, the study gauges social factors through the school enrollment rate (SE), expressed as the gross percentage of enrollment in primary schooling to the total enrollment [83, 93]. SE is employed to capture the effects of education and literacy on the subject. Moreover, to account for macro-level economic variations and their effects on the dependent variables, per capita GDP growth (PG, annual %) is employed as a control variable [94, 95]. Per capita health expenditure (HE, constant 2015 US$) is utilized, following [96] and [97], to control for their effects on LE and MR. In this context, HE enables the assessment of the effects of out-of-pocket spending on the subject. Additionally, per capita CO2 emissions (CO2e), resulting from the use of fossil fuels and industry, serve as an environmental degradation variable [53]. Lastly, the study incorporates urbanization (UR, % of population) as a control variable for its effects on LE and MR [98, 99].

Construction of new variables

This part addresses the construction of the inflationary shock variable and institutional quality index. The persistent growth in the general price level of food items, especially when it is unpredictable in the future, cannot be overruled, Considering the previous period of inflationary episodes in South Asia that hindered general food prices, we innovatively construct a predictor of inflation uncertainty (InF). This allows for a more precise evaluation of the effects of economic variability on both LE and MR. In doing so, we use the datapoints of the annual inflation rate and the generalized autoregressive conditional heteroskedasticity (GARCH) model of Bollerslev [100] as follows:

$$VAR\left( {{\varepsilon _{INF,t}}} \right)=\sigma _{t}^{2}+{\vartheta _0}+{\vartheta _1}\varepsilon _{{INF,t - 1}}^{2}+\zeta \sigma _{{t - 1}}^{2}$$

In Eq. (1), \( VAR\left({\epsilon }_{INF,t}\right)\) is the conditional variance of error term of the annual inflation rate, \( {\vartheta }_{0}\) and \( {\vartheta }_{1}\) are the intercept and autoregressive conditional heteroskedasticity parameter, respectively, and \( \zeta {\sigma }_{t-1}^{2}\) represents the GARCH parameter. Additionally, since the 1980s, political instability, ineffective government, and, most importantly, corruption have been serious issues in South Asian economies that have brought local and international concerns to the fore [101]. This has been an alarming concern to most of the financial aid to uplift poverty, basic healthcare services, and FI. However, South Asian governments adopted programs of anti-corruption, they only remained as populist mottos. Based on the Worldwide Governance Indicators (see Fig. 4), though all institutional indicators are comparatively lower than other regions, political stability stands at 26.53 percentile rank, followed by regulatory quality at 30.65 percentile rank, voice and accountability at 33.29 percentile rank, rule of law at 37 percentile rank, and government effectiveness at 38.91 percentile rank. Values below the 50-percentile rank are alarming signs of poor institutional quality.

Fig. 4
figure 4

Institutional quality indicators

Notes: VoC: Voice and accountability, PoS: Political stability, GeF: Government effectiveness, ReQ: Regulatory quality, RoL: Rule of law, CoC: Control of corruption. Data sourced from Worldwide Governance Indicators. Values are presented in percentile ranks from 1 to 100 (perfect)

Thus, to account for both the direct and moderating effects of institutional quality on the subject, we innovatively construct a comprehensive institutional quality index (InQ) following the distance-based approach proposed by Sarma [102]. This technique has recently gained prominence in the literature and has several advantages over common methods [103,104,105,106]. Figure 5 displays the constructed institutional quality index (InQ).

Fig. 5
figure 5

Cross-country institutional quality index (InQ).

Notes: AFN: Afghanistan, BGD: Bangladesh, BHT: Bhutan, IND: India, MLD: Maldives, NPL: Nepal, SRL: Sri Lanka, PAK: Pakistan. InQ is expressed as numbers ranging from 0 to 1 (perfect)

Sources of data compilation

Initially, the study compiled relevant data at the country level and subsequently constructed a comprehensive panel for South Asia, encompassing 8 countries. The datasets for LE, MR, SE, PG, HE, UR, and annual CPI-based inflation rate were sourced from the World Development Indicators [107]. Additionally, the datasets for PN, KS, and CI were obtained from UN-FAO [108]. The data for per capital CO2e was sourced from the Global Carbon Budget, available in [109]. Finally, datasets for constructing the InQ have been compiled from Worldwide Governance Indicators [110] sources.

Estimation methods

Our primary objectives are to investigate how both endogenous and exogenous predictors influence health outcomes in South Asia. To that end, we modify the existing health production function using the lines of direction shown in our conceptual framework. First, to test the effects of FI, environmental factors, and other economic and social indicators in the presence of InQ and InF on health outcomes, we specify the following multivariate long-run equation:

$$\begin{gathered} H{O_{it}}=\delta +{\eta _1}P{N_{it}}+{\eta _2}K{S_{it}}+{\eta _3}C{I_{it}}+{\eta _4}S{E_{it}}+{\eta _5}P{G_{it}}+{\eta _6}H{E_{it}} \hfill \\ \,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,+{\eta _7}C{O_2}{e_{it}}+{\eta _8}U{R_{it}}+{\eta _9}In{Q_{it}}+{\eta _{10}}In{F_{it}}+{\wp _t}+{\varepsilon _{it}} \hfill \\ \end{gathered} $$

where all variables are defined before, HO refers to health outcome proxied by LE and MR, \( \delta \) is the intercept, and \( {\eta }_{1}\) to \( {\eta }_{10}\) are the long-run coefficients. Subscripts \( i\) represents the countries and \( t\) denotes time dimension. Equation (1) and the subsequent regressions account for country-specific unobserved fixed effects represented by \( \wp \). Finally, \( {\epsilon }_{it}\) presents the error term. To examine the moderating effects of InF and InQ on the relationship between health outcomes and the endogenous variables, we specify the following equation:

$$H{O_{it}}=\delta +\sum\limits_{{j=1}}^{{10}} {{\eta _j}{X_{it}}+} \,\theta \left( {{Z_{it}} \times {X_{it}}} \right)+\,{\wp _t}+{\varepsilon _{it}}$$

where \( {\eta }_{j}\) refers to the long-run coefficients of the explanatory variables \( {X}_{it}\) and \( \theta \) represents the long-run coefficient of the interaction term of the \( {Z}_{it}\) (InF or InQ) with the explanatory variables, using separate regressions for each interaction models. In order to incorporate the interaction terms into Eq. (3), we follow the same methodology as proposed by Abaidoo and Agyapong [111] and Dada and Ajide [112]. In doing so, we differentiate the health outcome indicators (LE and MR) with respect the explanatory variables as follows:

$$\frac{{\partial H{O_{it}}}}{{\partial {X_{it}}}}={\eta _j}+\theta {Z_{it}}$$

where the sign of \( \theta \) is a priori-indeterminant due to the expected effects (positive or negative) of the explanatory variables on HO. For example, we expect \( \theta \) to be positive in the relationship between LE and KS and negative in reducing the effects of CO2e on MR. To estimates Eqs. 2 and 3, we first consider the fixed effects (FE) model, where \( \wp \) is considered as the country-specific effects. The estimation of FE model is based on the assumption that \( {\epsilon }_{it}\) is correlated with \( {X}_{it}\) and uncorrelated with \( \wp \) [113]. Nonetheless, random effects (RE) model is an alternative to FE technique. It assumes that \( \wp \) is a random variable and uncorrelated with \( {X}_{it}\). If this assumption holds, then RE estimators would be more reliable than FE model [114]. This hypothesis can be tested using Hausman’s [115] specification approach. Additionally, the instrumental variables approach (IV) is another empirical competitor, which considers that there might be some exogenous variables, such as InQ, InF, and CO2e, as in our case, correlated with \( {\epsilon }_{it}\). It offers a mechanism to still estimate accurate and consistent coefficients. The IV regression takes the following form:

$$\begin{gathered} {Y_{it}}=\mu +{\vartheta _1}X_{{it}}^{1}+{\vartheta _1}X_{{it}}^{2}+\varepsilon _{{it}}^{1} \hfill \\ X_{{it}}^{1}=\mu +{\theta _2}X_{{it}}^{1}+{\theta _3}X_{{it}}^{2}+{\theta _4}X_{{it}}^{3}+\varepsilon _{{it}}^{2} \hfill \\ \end{gathered} $$

where \( {X}_{it}^{1}\), \( {X}_{it}^{2}\) and \( {X}_{it}^{3}\) refer to the endogenous variables, exogenous variables, and instrumental variables, respectively, \( \theta \) refers to the vector of reduced from coefficients, and \( {\epsilon }_{it}^{1}\) and \( {\epsilon }_{it}^{2}\) present the normal multivariate variance-covariance matrix. If the homoscedastic assumption holds true, then IV regression would be a good substitution. Nevertheless, in the presence of autocorrelation, cross-sectional dependence, heteroskedasticity, and endogeneity issues, neither of the above-cited models would be reliable. Therefore, to account for these issues, we estimate the IV-generalized method of moment (IV-GMM) model of Blundell and Bond [116]. It provides unbiased and consistent coefficients and has gained statistical prominence in prior literature. The IV-GMM model is also suitable for small samples like ours (t = 176), whether balanced or unbalanced [117]. Furthermore, unlike pooled OLS, FE, and RE techniques, the GMM model does not require the sample to hold normality assumptions [118]. For brevity, the moment conditions (MMs) of the GMM model, which were conducive to its development, take the following form:

$$E\left[ {{Z_{it}}{\varepsilon _{it}}\left( \vartheta \right)} \right]=E\left[ {{Z_{it}}\left( {{Y_{it}} - \left( \vartheta \right)X^{\prime}_{{it}}} \right)} \right]=0$$

where \( {X}_{i,t-1}\), \( {X}_{i,t-2},\) and \( {X}_{i,t-i}\) are the instruments used. The model can be estimated using system- or difference-GMM. The system-GMM model simultaneously includes two MMs for differenced and level equations, and it is evidently more accurate than difference-GMM [119, 120]. The difference-GMM, however, removes the fixed effects by differencing the employed data [121]. Further, the system-GMM is estimated using one-step or two-step estimators. Based on conventional asymptotics theory, however, estimators would be asymptotically normal in both approaches, but the two-step system-GMM estimator yields a comparatively smaller variance [122]. Therefore, the present study adopts the two-step system-GMM model. For estimation of the system-GMM, we used STATA/BE-17, in which the “xtabond2” command comes with a built-in diagnostic check for the first- and second-order autocorrelation, testing the well-being of the instruments used in the model under the presence of the first- and absence of the second-order autocorrelation [123]. Nevertheless, this inquiry does not aim to discuss the preference and technicality of the GMM model; the above-cited studies can be found highly informative.

Results and discussions

Summary statistics

This section presents summary statistics (see Table 1) for the variables utilized in the study. LE in South Asia averages 68.24 years, which is comparatively lower than East Asia (76 years) and Western Asia (72 years), yet similar to Central Asia (69 years). MR shows an average of 39.4 per 1,000 infants, with a maximum of 90.6 and a minimum of 22.3 in South Asia. This rate is higher than in other Asian regions; for instance, the average MR in East Asia has consistently remained between 12 and 13 per 1,000 infants throughout the recent decades. Worryingly, PN in South Asia is notably high at 17.37%, indicating a state of catastrophic FI. Afghanistan, Pakistan, Bangladesh, and India are at the top among the countries included in the study [5]. While for brevity, one may peruse other statistics, it is crucial to highlight the InF that averages 63.28% with a striking range from a minimum of 7.59% to a maximum of 589.7% over the years from 2000 to 2021. Again, Afghanistan, Sri Lanka, and Pakistan emerge as countries experiencing the highest levels of such uncertainties during 2010. Additionally, Fig. 6 illustrates the cross-country InF over the period under review. Finally, InQ reveals an average of 0.335, indicating a relatively low score across all institutional aspects. In practice, an average below 0.50 suggests catastrophic governance in an economy.

Table 1 Summary statistics
Fig. 6
figure 6

Cross-country InF, 2000–2021

Source: Authors’ depiction

In addition, the study conducted a correlation analysis between the variables used, with the results presented in Table 2. The main purpose was to examine the potential presence of multicollinearity among the variables in the recipient panel. Two common approaches were applied for this purpose: Firstly, following Elith et al. [124], a threshold level of above 0.85 was suggested for detecting multicollinearity among the variables. Our results indicate that the correlation between all variables is below this threshold level. Secondly, the variance inflation factor (VIF) was calculated to further assess multicollinearity. The VIF, computed as a post-estimation of the pooled OLS model, reveals that all variables exhibit values less than 10, with a mean value of 4.10, below a threshold level of 5. Both methods unanimously confirm that the variables do not suffer from multicollinearity problems.

Table 2 Correlation matrix

Insights into FI and LE nexus

Tables 3A and 3B report the results of 2Sys-GMM estimations. In Table 3A, column (1) reports the effects of FI on LE. Columns (2) to (9) further showcase the moderating effects of InF on FI and other explanatory variables. Table 3B, on the other hand, outlines the results of the moderating effects of InQ on the relationships between FI, InF, and other explanatory variables influencing LE.

The results reveal that a 1% increase in PN leads to a decrease in LE by 0.085 years in South Asia. This finding aligns with the observations of Beyene [13], who noted a decrease of 0.00348 years in LE with an increase in PN in Sub-Saharan Africa. Nutrition’s crucial role for the human body is emphasized, as sustained undernourishment can have significantly negative consequences on health [125]. Furthermore, the findings indicate that external shocks from InF, causing a surge in food prices, amplify the effects of PN on LE. The interaction of InF with PN reveals that a 1% increase in inflation forces PN to decrease LE by 0.091 years—an additional 0.006% point higher than the contemporary effects of PN. This resonates with the findings of Kidane and Woldemichael [126], who observed that a higher inflation rate diminishes people’s capacity to afford necessary food items, leading to long-term adverse consequences. Additionally, for every percent increase, InQ significantly contributes to increasing LE by 0.068 years. In Table 3B, column (1), the results underscore the highly effective moderating role of InQ in mitigating the negative impact of both PN and InF on LE. The interaction of InQ with PN demonstrates a reduction in the effects of PN on LE by 0.012 years. Consistent with our findings, Nugroho et al. [127] noticed that corrupted institutions serve as a concealed force contributing to the vulnerability of FI. They found that reducing corruption, meaning that people do not have to pay bribes, leads to improvements in undernourishment.

Additionally, the results highlight that KS significantly and positively contributes to an increase in LE by 1.749 years. However, the interaction of InF substantively diminishes the effects of KS on LE by 0.203 years. This underscores the highly negative impact of InF on the supply of necessary energy and food items in South Asia. Moreover, the interaction of InQ with KS reveals its effectiveness in enhancing the effects of KS on LE by 1.866 years. Importantly, it neutralizes the negative effects of InF (0.00054) on the subject. This suggests that while a higher inflation rate imposes elevated costs on agricultural inputs [128], leading to an overall increase in food prices, institutional quality may effectively mitigate or eliminate these inflationary stressors.

Table 3A Effects of FI on LE
Table 3B Effects of FI on LE

Consequently, this improves the relationship between KS and LE. These findings align with the work of Soko et al. [129], who similarly found that institutional quality has an effective mediating impact on the relationship between agricultural inputs and food security. The findings also reveal that CI has negative effects on LE. A 1% increase in CI reduces LE by 1.95 years. Again, the results highlight that when InF interacts with CI, the negative impact of CI intensifies, leading to a reduction in LE by 2.031 years. Conversely, the interaction of InQ with CI mitigates the negative impact of CI by 0.98 years. Notably, the findings do not support the significance of SE on LE. However, PG is identified as a positive factor affecting the dependent variable. The shock from InF reduces the positive effects of PG on LE, showing that InF reduces the purchase power of the people buying food items. In this context, InQ emerges as a significant moderating factor, countering the negative shock of InF on the nexus between PG and LE. These results support the findings of Salahodjaev and Chepel [130], Khan and Hanif [101], and Cicen [131], who observed that institutional quality modulates the negative impact of inflation rate on GDP. Furthermore, HE is found to improve LE. However, InF reduces the effectiveness of HE, while InQ improves the relationship between HE and life expectancy. Recent studies by Opeloyeru et al. [132] and Sharma et al. [133] also noticed that institutional quality improves the outcomes of health expenditures.

With respect to the environmental degradation effects, the results indicate that CO2e reduces LE in the recipient panel. Consistently, Azam et al. [82], Rahman et al. [134], and Majeed and Ozturk [135] support these findings on the negative impact of environmental degradation on LE. For instance, Azam et al. [82] delved into the effects of CO2e on LE in Pakistan and found that CO2e play a significantly negative role in reducing LE. Furthermore, the results show that the interaction of InF with CO2e increases its negative impact on LE from 0.371 to 0.484 years. The interaction of InQ with CO2e decreases its negative impact by 0.196 years. Ahmad et al. [136] investigated the effects of inflation instability on environmental degradation in Asian countries, and they similarly found that it hinders environmental quality and thus affects health outcomes. On the other hand, Jahanger et al. [137] provide support for the significance of institutional quality in improving environmental quality. Finally, the results offer statistical support for the positive effects of UR on LE. This association is grounded in the reality that individuals residing in remote areas often face constraints in accessing food, sanitation, and healthcare services, compared to their urban counterparts [98]. The challenges of poverty, unemployment, and illiteracy may further impede the quality of life for people in remote areas.

Insights into FI and MR nexus

Tables 4A and 4B report the results of 2Sys-GMM estimations. In Table 4A, column (1) details the effects of FI on MR, while columns (2) to (9) report the moderating effects of InF on MR and other explanatory variables. Table 4B illustrates the moderating effects of InQ on the relationships between MR, InF, and other explanatory variables. The results reveal that PN significantly increases MR. Specifically, a 1% increase in PN leads to an increase in MR by 0.0845 per 1,000 infants. Contrastingly, KS is identified as a significant factor in reducing MR, with a decrease of 0.22 per 1,000 infants. However, CI is associated with an increase in MR by 0.74 per 1,000 infants. Notably, Banerjee et al. [85] studied the effects of FI on MR and found similar results. Moreover, Beyene [13] found that a 1% increase in PN statistically increases MR by 0.0119 per 1,000 infants in Sub-Saharan Africa. Our results show that compared to Sub-Saharan African countries, South Asia is more vulnerable to FI and experiences more deaths each year. Furthermore, the findings reveal that PG and HE are significant factors in reducing MR. It shows that a one US$ increase in PG causes MR to reduce by 0.012 per 1,000 infants. These results are consistent with those of Pérez-Moreno et al. [138], Salahuddin et al. [40], Kammerlander and Schulze [139], and Fotio et al. [140], who also found that economic growth is essential to reducing the contemporary MR. On the other hand, a one US$ increase in HE reduces MR by 0.260 per 1,000 infants. The results demonstrate that, compared to PG, HE is more effective in reducing MR in South Asia. Recent studies by Houeninvo [141], Schneider et al. [142], and Nketiah-Amponsah [143], have also observed that HE is significant in reducing MR. The interaction of InF shows that it highly reduces the impact of both PG and HE on MR, while InQ is found to effectively moderate the relationship between them. Similarly, Farag et al. [78] and Ahmad and Hasan [144] found that institutional quality plays an important moderating role in improving the nexus between HE and MR.

Table 4A Effects of FI on MR
Table 4B Effects of FI on MR

Additionally, the results indicate that CO2e have positive impacts on MR, showing that an increase in environmental degradation increases MR. InF is found to increase the effects of CO2e, while InQ is observed to have a substantive moderating role in neutralizing the negative impact of InF and CO2e on MR. It is obvious in most of the South Asian countries. For example, due to financial incapacity, people burn plastic, tyers, and rubbers for heating purposes during winter. Our results are consistent with those of Avik [145], Rasoulinezhad et al. [146], Ogungbenle and Rufus [147], and Adeleye et al. [148], who have also discovered that environmental degradation has positive effects on MR. Furthermore, Uzar [149] offers specific support to our findings on the significant moderating role of InQ in reducing the impact of environmental degradation on the subject. However, UR is substantive to reduce MR by 0.302 per 1,000 infants; inflation instability is found to neutralize the impact of UR on MR. This might be due to the reduction of purchase power and increase in general prices that slow down the process of UR, affecting the MR. Finally, we found that, in addition to their moderating roles, both InF and InQ have direct effects on MR. While InF increases MR, InQ has a significant effect on reducing MR in South Asia.

Robustness checks

The results obtained from the 2Sys-GMM model, as reported in Tables 3A4B, demonstrate robustness. Each table includes diagnostic checks to ensure the reliability of the estimations. In particular, the results of Arellano-Bond (1) are significant at the 1% level, leading to rejection of the null hypothesis of no first-order autocorrelation. Conversely, the statistical results for Arellano-Bond (2) are insignificant to reject the null hypothesis of no second-order autocorrelation across all estimated models [150]. Additionally, we conducted tests for examining the overidentifying constraints through the Sargan-Hansen model. The results indicate that the combined null hypothesis of instrumental validity—meaning that the instruments are uncorrelated with the error term—and the appropriateness of excluding omitted instruments from the estimated equations holds [151].


Economic disparities persist in our world, where stark contrasts exist between those succumbing to extreme hunger and deprivation of basic human rights and others facing health risks due to overeating. South Asia, as the second-poorest region globally, grapples with high rates of poverty, hunger-related deaths, and limited access to essential healthcare facilities. This study seeks to explore the health consequences of FI across eight South Asian countries: Afghanistan, Bangladesh, Bhutan, India, the Maldives, Nepal, Sri Lanka, and Pakistan. Spanning the years from 2000 to 2021, the study employs datasets compiled from various reliable sources. Notably, the study introduces innovative variables, including an inflationary shock variable (InF) and a composite institutional quality index (InQ), using the generalized autoregressive conditional heteroskedasticity and the distance-based approach index construction of Sarma [102], respectively. InF aims to capture the impact of global inflationary shocks on health outcomes, while InQ aims to assess how effective institutions can counteract and control InF alongside other local factors. To analyze the data, the study employs the 2Sys-GMM model as the primary functional equation.

The study’s results unveil compelling insights into the dynamics of life expectancy and infant mortality rates in South Asia. In particular, the prevalence of undernourishment (PN), inequality in per capita calorie intake (CI), CO2e, and InF emerge as influential factors with negative impacts on life expectancy but positive effects on infant mortality rates. These variables are found to play a significant role in reducing life expectancy and increasing infant mortality rates in the region. Conversely, per capita kilocalorie supply (KS), per capita GDP growth (PG), per capita health expenditures (HE), and urbanization (UR) are identified as significantly effective contributors to increasing life expectancy and decreasing infant mortality rates. The findings emphasize the negative moderating role of InF, accentuating the adverse effects of PN, CI, and CO2e on life expectancy, while amplifying their positive impacts on infant mortality rates. This underscores the direct and severe consequences of inflation instability on FI and environmental factors, leading to higher infant mortality rates and diminished contemporary life expectancy in South Asia. In rebuttal, the study employs InQ to examine its direct unconditional and moderating effects. InQ is revealed to have both positive and negative impacts on life expectancy and infant mortality rates, respectively. Moreover, InQ emerges as a significant moderator, effectively tempering the positive effects of KS, PG, HE, and UR on life expectancy, while mitigating the negative effects of PN, CI, and CO2e on the subject. The role of InQ extends to moderating the relationships between the variables and infant mortality rates, further highlighting its crucial influence in shaping health outcomes in South Asia.

Policy implications

From the findings, two policy implications emerge as follows:

Triple constraints

The results identify three critical constraints—FI, environmental factors, and economic vulnerability to global shocks—that significantly impact both life expectancy and infant mortality rates in South Asia. Major FI constraints arise from insufficient food products to meet daily requirements. Given the region’s heavy reliance on agriculture, governments need to support farmers. Implementing specific financial and non-financial incentive schemes is essential to enhance contemporary agricultural capacity. This proactive approach is imperative in effectively combating extreme hunger and improving overall food security in South Asia. Environmental degradation poses alarming threats to life expectancy and infant mortality rates. Contributing factors include individual poverty, low-quality production machinery leading to industrial pollution, and inadequate governmental support at both public and private sector levels. A comprehensive strategy addressing these factors concurrently is necessary to improve environmental quality. Governments must focus on mitigating individual and industrial impacts, alongside providing robust support to environmental initiatives on a national scale. Inflationary shocks emerge as a significant factor affecting both life expectancy and mortality rates. While eliminating global shocks is challenging, the region can explore strategies such as fostering free trade and establishing visa-free zones. Promoting higher integration, increased trade, and enhanced capital exchange can help mitigate the adverse effects of inflationary shocks on health outcomes. By opening new avenues for cooperation and economic resilience, South Asian nations can better navigate and counteract the impacts of global economic fluctuations on public health.

Institutional constraint

The findings indicate that poor institutional quality hampers health outcomes, while its improvement proves effective in enhancing both the efficiency and scope of health outcomes. Significantly, institutional quality serves as a remedy to mitigate the negative effects of external shocks on contemporary endogenous FI and environmental indicators. The governments of South Asia urgently need to prioritize the promotion, advancement, and institutionalization quality factors within public organizations. This necessitates a focused effort to combat corruption, improve government efficacy, and advance other elements of good governance.

Study’s limitations

The present study examined the effects of food insecurity and environmental degradation on life expectancy and mortality rates in South Asia, pinpointing specific policy areas that demand attention. However, two major limitations are acknowledged throughout writing this piece of investigation: firstly, the unavailability of health-sector-specific disaggregated datasets. Aggregate data provides a general overview of the current state; however, if disaggregated datasets were available, cross-sector specific results could help deeper insights into existing policies measures. Future studies, equipped with such datasets, may address these empirical shortcomings. Secondly, the overspecification issue. Due to this constraint, the present study could not incorporate additional explanatory variables such as health diplomacy, foreign direct investment, and domestic credit to private health sector, which could influence health outcome indicators. Subsequent studies could benefit from broader observations, integrating these predictors into their analysis for a more comprehensive understanding of the complex dynamics influencing health outcomes in South Asia.

Data availability

Datasets relevant to LE, MR, SE, PG, HE, and UR have been compiled from the World Development Indicators (WDI). Datasets for PN, KS, and CI were collected from UN-FAO. The data for CO2e has been collected from the Global Carbon Budget (2022). Finally, datasets for constructing InQ have been compiled from Worldwide Governance Indicators (WGI) sources.


  1. Santos MP, Brewer JD, Lopez MA, Paz-Soldan VA, Chaparro MP. Determinants of food insecurity among households with children in Villa El Salvador, Lima, Peru: the role of gender and employment, a cross-sectional study. BMC Public Health. 2022;22(1).

  2. Bank W, Food Security Update| World Bank Response to Rising Food Insecurity. 2023, [Online]. Available:

  3. FAO., In Brief to The State of Food Security and Nutrition in the World 2021. 2021.

  4. Yan S, Alvi S. Food security in South Asia under climate change and economic policies. Int J Clim Chang Strateg Manag. 2022;14(3):237–51.

    Article  Google Scholar 

  5. Hannah MRoserRitchie. Hunger and under nourishment. Soc Dev. 2019;1(1):pp1–29. [Online]. Available:.

    MathSciNet  Google Scholar 

  6. Rasul G. Twin challenges of COVID-19 pandemic and climate change for agriculture and food security in South Asia. Environ Challenges. 2021;2.

  7. Bhowmick S. Ukraine-Russia conflict: Impact on South Asia, Observer research foundation. 2023, [Online]. Available:

  8. Wynn A, Wynn M. The effects of food shortage on human reproduction. Nutr Health. 1993;9(1):43–52.

    Article  CAS  PubMed  Google Scholar 

  9. Hood RB, Norris AH, Huber-Krum S, Garver S, Chapotera G, Turner AN. Food insecurity and adverse pregnancy outcomes among rural Malawian women. Int J Gynecol Obstet. 2022;156(2):309–15.

    Article  Google Scholar 

  10. Halicioglu F. Modeling life expectancy in Turkey. Econ Model. 2011;28(5):2075–82.

    Article  Google Scholar 

  11. Benzekri NA, et al. The impact of food insecurity on HIV outcomes in Senegal, West Africa: a prospective longitudinal study. BMC Public Health. 2021;21(1).

  12. Weiser SD, Palar K, Hatcher AM, Young S, Frongillo EA, Laraia B. Food insecurity and health: a conceptual framework. in Food Insecurity Public Health, 2015, pp. 23–50.

  13. Beyene SD. The impact of food insecurity on health outcomes: empirical evidence from sub-saharan African countries. BMC Public Health. 2023;23(1).

  14. Brucker DL. The association of food insecurity with health outcomes for adults with disabilities. Disabil Health J. 2017;10(2):286–93.

    Article  PubMed  Google Scholar 

  15. Heflin C, Darolia R, Kukla-Acevedo S. Exposure to Food Insecurity during Adolescence and Educational Attainment. Soc Probl. 2022;69(2):453–69.

    Article  Google Scholar 

  16. Saeed A, Naqvi M, Javed A. Effects of maternal food insecurity on Birth Weight of neonates: a prospective cohort. Ann King Edw Med Univ. 2018;23(4):524–30.

    Article  Google Scholar 

  17. Gundersen C, Tarasuk V, Cheng J, De Oliveira C, Kurdyak P. Food insecurity status and mortality among adults in Ontario, Canada. PLoS ONE. 2018;13(8).

  18. Tayal D. Gender inequality, reproductive rights and food insecurity in Sub-saharan Africa– a panel data study. Int J Dev Issues. 2019;18(2):191–208.

    Article  Google Scholar 

  19. Muluneh MG. Impact of climate change on biodiversity and food security: a global perspective—a review article. Agric Food Secur. 2021;10(1).

  20. Bayati M, Akbarian R, Kavosi Z. Determinants of life expectancy in eastern Mediterranean region: a health production function. Int J Heal Policy Manag. 2013;1(1):57–61.

    Article  Google Scholar 

  21. Fayissa B, Gutema P. Estimating a health production function for Sub-saharan Africa (SSA). Appl Econ. 2005;37(2):155–64.

    Article  Google Scholar 

  22. Grossman M. On the Concept of Health Capital and the demand for Health. J Polit Econ. 1972;80(2):223–55.

    Article  Google Scholar 

  23. Auster R, Leveson I, Sarachek D. The production of Health, an exploratory study. J Hum Resour. 1969;4(4):411.

    Article  Google Scholar 

  24. Onyimadu O, Violato M, Astbury NM, Jebb SA, Petrou S. Health Economic aspects of Childhood excess weight: a structured review. Children. 2022;9(4).

  25. Salgado M, Madureira J, Mendes AS, Torres A, Teixeira JP, Oliveira MD. Environmental determinants of population health in urban settings. A systematic review. BMC Public Health. 2020;20(1).

  26. Acemoglu D, Johnson S, Robinson JA. Chapter 6 institutions as a Fundamental cause of Long-Run Growth. Handb Econ Growth. 2005;1:386–472.

    Article  Google Scholar 

  27. Fayissa B, Nsiah C. The impact of Governance on Economic Growth in Africa. J Dev Areas. 2013;47(1):91–108.

    Article  Google Scholar 

  28. Al-Bassam B. The Relationship between Governance and Economic Growth during Times of Crisis. Eur J Sustain Dev. 2013;2(2):1–18.

    Article  Google Scholar 

  29. North DC, North DC. Institutions, J. Econ. Perspect, vol. 5, no. 1, pp. 97–112, 1991.

  30. WHO., World health statistics 2022 (Monitoring health of the SDGs), Monit. Heal. SDGs, pp. 1–131, 2022, [Online]. Available:

  31. Frenk J, Moon S. Governance challenges in Global Health. N Engl J Med. 2013;368(10):936–42.

    Article  CAS  PubMed  Google Scholar 

  32. McInnes C et al. Framing global health: The governance challenge, Glob. Public Health, vol. 7, no. SUPPL. 2, 2012,

  33. Kaufmann D, Kraay A, Mastruzzi M. Governance matters VII: Aggregate and individual governance indicators 1996–2007, in Non-State Actors as Standard Setters, 2009, pp. 146–188.

  34. Pradhan RP. Good governance and human development: evidence form Indian States. J Soc Dev Sci. 2011;1(1):1–8.

    Article  Google Scholar 

  35. Dadgar I, Norström T. Is there a link between all-cause mortality and economic fluctuations? Scand J Public Health. 2022;50(1):6–15.

    Article  PubMed  Google Scholar 

  36. Gautam N. Impact of of Government’s Human Capital Expenditure on Economic Growth of Nepal: an ARDL Approach. Quest J Manag Soc Sci. 2022;4(1):30–41.

    Article  Google Scholar 

  37. Niu XT, Yang YC, Wang YC. Does the Economic Growth Improve Public Health? A cross-regional heterogeneous study in China. Front Public Heal. 2021;9.

  38. Spiteri J, von Brockdorff P. Economic development and health outcomes: evidence from cardiovascular disease mortality in Europe. Soc Sci Med. 2019;224:37–44.

    Article  PubMed  Google Scholar 

  39. Erdoğan E, Ener M, Arıca F. The Strategic Role of Infant Mortality in the process of economic growth: an application for high income OECD Countries. Procedia - Soc Behav Sci. 2013;99:19–25.

    Article  Google Scholar 

  40. Salahuddin M, Vink N, Ralph N, Gow J. Effects of economic growth, foreign direct investment and internet use on child health outcomes: empirical evidence from South Africa. Dev Stud Res. 2020;7(1):1–17.

    Article  Google Scholar 

  41. Knapp M, Wong G. Economics and mental health: the current scenario. World Psychiatry. 2020;19(1):3–14.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Mohapatra S. Economic growth, public expenditure on health and IMR in India: an econometric investigation of causal linkages. Int J Soc Econ. 2017;44(12):2002–18.

    Article  Google Scholar 

  43. Gasimli O, ul Haq I, Gamage SKN, Prasanna R, Khattak ZZ, Abbas A. Energy, environmental degradation, and health status: evidence from South Asia. Environ Sci Pollut Res. 2023;30(5):13639–47.

    Article  Google Scholar 

  44. Mumtaz A, Rehman E, Rehman S, Hussain I. Impact of Environmental Degradation on Human Health: an Assessment using Multicriteria decision making. Front Public Heal. 2022;9.

  45. Omri A, Kahouli B, Afi H, Kahia M. Impact of Environmental Quality on Health Outcomes in Saudi Arabia: does Research and Development Matter? J Knowl Econ. 2022.

    Article  Google Scholar 

  46. Taghizadeh-Hesary F, Rasoulinezhad E, Yoshino N. Energy and Food Security: linkages through price volatility. Energy Policy. 2019;128:796–806.

    Article  Google Scholar 

  47. Alimi OY, Ajide KB, Isola WA. Environmental quality and health expenditure in ECOWAS. Environ Dev Sustain. 2020;22:5105–27.

    Article  Google Scholar 

  48. Clark MA, Springmann M, Hill J, Tilman D. Multiple health and environmental impacts of foods, Proc. Natl. Acad. Sci. U. S. A, vol. 116, no. 46, pp. 23357–23362, 2019,

  49. Li Z, Wei X, Al Shraah A, Khudoykulov K, Albasher G, Ortiz GGR. Role of green energy usage in reduction of environmental degradation: a comparative study of east Asian countries. Energy Econ. 2023;126.

  50. Emodi NV, Inekwe JN, Zakari A. Transport infrastructure, CO2 emissions, mortality, and life expectancy in the Global South. Transp Policy. 2022;128:243–53.

    Article  Google Scholar 

  51. Zeeshan M, Han J, Rehman A, Ullah I, Alam Afridi FE. Exploring asymmetric nexus between CO2 emissions, environmental pollution, and household health expenditure in China. Risk Manag Healthc Policy. 2021;14:527–39.

    Article  PubMed  PubMed Central  Google Scholar 

  52. MURTHY U, SHAARI MS, MARIADAS PA, ABIDIN NZ. The relationships between CO2 emissions, Economic Growth and Life Expectancy. J Asian Financ Econ Bus. 2021;8(2):801–8.

    Article  Google Scholar 

  53. Das S, Debanth A. Impact of CO2 emission on life expectancy in India: an autoregressive distributive lag (ARDL) bound test approach. Futur Bus J. 2023;9(1).

  54. Johnson KT, Palakshappa D, Basu S, Seligman H, Berkowitz SA. Examining the bidirectional relationship between food insecurity and healthcare spending. Health Serv Res. 2021;56(5):864–73.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Dean EB, French MT, Mortensen K. Food insecurity, health care utilization, and health care expenditures. Health Serv Res. 2020;55:883–93.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Seligman HK, Bindman AB, Vittinghoff E, Kanaya AM, Kushel MB. Food insecurity is associated with diabetes mellitus: results from the National Health Examination and Nutrition Examination Survey (NHANES) 1999–2002. J Gen Intern Med. 2007;22(7):1018–23.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Militao EMA, Salvador EM, Uthman OA, Vinberg S, Macassa G. Food Insecurity and Health Outcomes Other than Malnutrition in Southern Africa: a descriptive systematic review. Int J Environ Res Public Health. 2022;19(9).

  58. Pengpid S, Peltzer K. Food insecurity and health outcomes among community-dwelling middle-aged and older adults in India. Sci Rep. 2023;13(1).

  59. Nagata JM, et al. Food Insecurity Is Associated with Poorer Mental Health and Sleep outcomes in Young adults. J Adolesc Heal. 2019;65:805–11.

    Article  Google Scholar 

  60. Nwosu CO, Kollamparambil U, Oyenubi A. Food insecurity and health outcomes during the coronavirus pandemic in South Africa: a longitudinal study. Health Econ Rev. 2022;12(1).

  61. Jemiluyi OO. Urbanization and Child Health outcomes in Nigeria. J Popul Soc Stud. 2021;29:586–603.

    Article  Google Scholar 

  62. Tripathi S, Maiti M. Does urbanization improve health outcomes: a cross country level analysis. Asia-Pacific J Reg Sci. 2023;7(1):277–316.

    Article  Google Scholar 

  63. Perrott GSJ, Holland DF. Population trends and problems of public health. Milbank Q. 2005;83(4):569–608.

    Article  PubMed  PubMed Central  Google Scholar 

  64. de Meijer C, Wouterse B, Polder J, Koopmanschap M. The effect of population aging on health expenditure growth: a critical review. Eur J Ageing. 2013;10:353–61. no. 4.

    Article  PubMed  PubMed Central  Google Scholar 

  65. Huang WH, Lin YJ, Lee HF. Impact of population and workforce aging on economic growth: case study of Taiwan. Sustain. 2019;11(22).

  66. Shao Q, Tao R, Luca MM. The effect of urbanization on Health Care expenditure: evidence from China. Front Public Heal. 2022;10.

  67. Liao J, et al. The effect of school-entry age on health is understudied in low- and middle-income countries: a scoping review and future directions for research. SSM - Popul Health. 2023;22.

  68. Gumus YY, Yurumez E. The effect of lowering school entry age on attention deficit hyperactivity disorder diagnosis. J Exp Clin Med. 2021;38(2):176–81.

    Article  Google Scholar 

  69. Raghupathi V, Raghupathi W. The influence of education on health: an empirical assessment of OECD countries for the period 1995–2015. Arch Public Heal. 2020;78(1).

  70. Gottfried MA, Sublett C. Older versus younger: the role of entry age for students who begin kindergarten with disabilities. Teach Coll Rec. 2019;121(3).

  71. Socoliuc OR, Sîrghi N, Jemna DV, David M. Corruption and Population Health in the European Union Countries—An institutionalist perspective. Int J Environ Res Public Health. 2022;19(9).

  72. Rahman MM, Alam K. Life expectancy in the ANZUS-BENELUX countries: the role of renewable energy, environmental pollution, economic growth and good governance. Renew Energy. 2022;190:251–60.

    Article  Google Scholar 

  73. Vian T. Anti-corruption, transparency and accountability in health: concepts, frameworks, and approaches. Glob Health Action. 2020;13(sup1).

  74. Van De Bovenkamp HM, De Mul M, Quartz JGU, Weggelaar-Jansen AMJWM, Bal R. Institutional layering in governing healthcare quality. Public Adm. 2014;92(1):208–23.

    Article  Google Scholar 

  75. De Luca G, Lisi D, Martorana M, Siciliani L. Does higher institutional quality improve the appropriateness of Healthcare Provision? J Public Econ. 2021;194:104356.

    Article  Google Scholar 

  76. Onofrei M, Vatamanu AF, Vintilă G, Cigu E. Government health expenditure and public health outcomes: a comparative study among Eu developing countries. Int J Environ Res Public Health. 2021;18(20).

  77. Glynn EH. Corruption in the health sector: a problem in need of a systems-thinking approach. Front Public Heal. 2022;10.

  78. Farag M, Nandakumar AK, Wallack S, Hodgkin D, Gaumer G, Erbil C. Health expenditures, health outcomes and the role of good governance. Int J Health Care Finance Econ. 2013;13(1):33–52.

    Article  PubMed  Google Scholar 

  79. Rosenberg DY. Political Economy of Infant Mortality Rate: Role of Democracy Versus Good Governance. Int J Heal Serv. 2018;48(3):435–60.

    Article  Google Scholar 

  80. Koller T, Clarke D, Vian T. Promoting anti-corruption, transparency and accountability to achieve universal health coverage. Glob Health Action. 2020;13(sup1).

  81. Hadipour A, Delavari S, Bayati M. What is the role of institutional quality in health outcomes? A panel data analysis on 158 countries from 2001–2020. Heliyon. 2023;9(9):e20251.

    Article  PubMed  PubMed Central  Google Scholar 

  82. Azam M, Uddin I, Saqib N. The determinants of life expectancy and environmental degradation in Pakistan: evidence from ARDL bounds test approach. Environ Sci Pollut Res. 2023;30(1):2233–46.

    Article  Google Scholar 

  83. Ali A, Ahmad K. The Impact of Socio-Economic Factors on Life Expectancy in Sultanate of Oman: An Empirical Analysis, Middle-East J. Sci. Res, vol. 22, no. 2, pp. 218–224, 2014,

  84. Banks AR, Bell BA, Ngendahimana D, Embaye M, Freedman DA, Chisolm DJ. Identification of factors related to food insecurity and the implications for social determinants of health screenings. BMC Public Health. 2021;21(1).

  85. Banerjee S, Radak T, Khubchandani J, Dunn P. Food insecurity and mortality in American adults: results from the NHANES-Linked Mortality Study. Health Promot Pract. 2021;22(2):204–14.

    Article  PubMed  Google Scholar 

  86. Men F, Gundersen C, Urquia ML, Tarasuk V. Association between household food insecurity and mortality in Canada: a population-based retrospective cohort study. CMAJ. 2020;192(3):E53–E60.

    Article  PubMed  PubMed Central  Google Scholar 

  87. Hassan FA, Minato N, Ishida S, Nor NM. Social Environment Determinants of Life Expectancy in developing countries: a Panel Data Analysis. Glob J Health Sci. 2016;9(5):105.

    Article  Google Scholar 

  88. McCarthy A, et al. Prevalence of malnutrition in pediatric hospitals in developed and in-transition countries: the impact of hospital practices. Nutrients. 2019;11(2).

  89. Pawellek I, Dokoupil K, Koletzko B. Prevalence of malnutrition in paediatric hospital patients. Clin Nutr. 2008;27(1):72–6.

    Article  PubMed  Google Scholar 

  90. Franco M, et al. Impact of energy intake, physical activity, and population-wide weight loss on cardiovascular disease and diabetes mortality in Cuba, 1980–2005. Am J Epidemiol. 2007;166(12):1374–80.

    Article  PubMed  Google Scholar 

  91. Hebert JR. Social and environmental factors and life expectancy, infant mortality, and maternal mortality rates: results of a cross-national comparison., Social science & medicine (1982), vol. 45, no. 12. p. 1899, 1997,

  92. Kabir M. Determinants of life expectancy in developing countries. J Dev Areas. 2008;41(2):185–204.

    Article  Google Scholar 

  93. Nwani SE. Air pollution trajectories and life expectancy in Nigeria. Int J Soc Econ. 2022;49(7):1049–70.

    Article  Google Scholar 

  94. He L, Li N. The linkages between life expectancy and economic growth: some new evidence. Empir Econ. 2020;58(5):2381–402.

    Article  Google Scholar 

  95. Shahbaz M, Shafiullah M, Mahalik MK. The dynamics of financial development, globalisation, economic growth and life expectancy in sub-saharan Africa. Aust Econ Pap. 2019;58(4):444–79.

    Article  Google Scholar 

  96. Shahraki M. Public and private health expenditure and life expectancy in Iran., Payesh Heal. Monit, vol. 18, no. 3, pp. 221–230, 2019, [Online]. Available:

  97. Bunyaminu A, Mohammed I, Yakubu IN, Shani B, Abukari AL. The effect of health expenditure on average life expectancy: does government effectiveness play a moderating role? Int J Heal Gov. 2022;27(4):365–77.

    Article  Google Scholar 

  98. Torres C, Canudas-Romo V, Oeppen J. The contribution of urbanization to changes in life expectancy in Scotland, 1861–1910. Popul Stud (NY). 2019;73(3):387–404.

    Article  Google Scholar 

  99. Shahbaz M, Loganathan N, Mujahid N, Ali A, Nawaz A. Determinants of Life Expectancy and its prospects under the role of economic misery: a case of Pakistan. Soc Indic Res. 2016;126(3):1299–316.

    Article  Google Scholar 

  100. Bollerslev T. Generalized Autoregressive conditional heteroskedasticity. J Econom. 1986;31(1):307–27.

    Article  MathSciNet  Google Scholar 

  101. Khan M, Hanif W. Institutional quality and the relationship between inflation and economic growth. Empir Econ. 2020;58(2):627–49.

    Article  Google Scholar 

  102. Sarma M. Index of financial inclusion– A measure of financial sector inclusiveness, Berlin Work. Pap. Money, Financ. Trade Dev, vol. 24, no. 8, pp. 472–476, 2012, Accessed: Jun. 12, 2022. [Online]. Available:

  103. Sarma M. Index of Financial Inclusion, Indian Council for Research on International Economic Relations Working Paper No 215, 2008.

  104. Park C-Y, Mercado RV. Does financial inclusion reduce poverty and income inequality in developing Asia?, in Financial Inclusion Asia, 2016, pp. 61–92.

  105. Omar MA, Inaba K. Does financial inclusion reduce poverty and income inequality in developing countries? A panel data analysis. J Econ Struct. 2020;9:1–25.

    Article  Google Scholar 

  106. Park C, Mercado RV, Inclusion F. Poverty, and Income Inequality in Developing Asia. ADB Economics Working pepers series., ADB Economics Working Paper Series 426. Manila, Philippines, 2015.

  107. Bank W. World development indicators| data, World Development Indicators, 2023. (accessed Mar. 23, 2020).

  108. UN-FAO., Share of the population that is undernourished. p. 1, 2023, [Online]. Available:

  109. Budget GC. Per capita CO2 emissions. p. 1, 2023, [Online]. Available:

  110. WGI., World Governance Indicators, 2023. [Online]. Available:

  111. Abaidoo R, Agyapong EK. Environmental sustainability risk, institutional effectiveness and urbanization. Energy Environ. 2022.

    Article  Google Scholar 

  112. Dada JT, Ajide FM. The moderating role of institutional quality in shadow economy–pollution nexus in Nigeria. Manag Environ Qual Int J. 2021;32(3):506–23.

    Article  Google Scholar 

  113. Zulfikar R. Estimation model and selection method of panel data regression: An overview of common effect, fixed effect, and random effect model, JEMA J. Ilm. Bid. Akunt, pp. 1–10, 2018, [Online]. Available:

  114. Bourbonnais R. Économétrie. 2018.

  115. Hausman JA. Specification tests in econometrics. Econometrica. 1978;38(2):112–34.

    MathSciNet  Google Scholar 

  116. Blundell R, Bond S. Initial conditions and moment restrictions in dynamic panel data models. J Econom. 1998;87(1):115–43.

    Article  Google Scholar 

  117. Kiviet JF, Pleus M, Poldermans RW. Accuracy and efficiency of various GMM inference techniques in dynamic micro panel data models. Econometrics. 2017;5(1).

  118. Verbeek M. A guide to modern econometrics. Appl Econometrics. 2007;8(4):125–32.

    Google Scholar 

  119. Roodman D. How to do xtabond2: An introduction to difference and system GMM in Stata, Stata J, vol. 9, no. 1, pp. 86–136, 2009,

  120. Roodman D. XTABOND2: Stata module to extend xtabond dynamic panel data estimator. Stat Softw Compon. 2015.

  121. Jung H, Kwon HU, Jeon G. An alternative system GMM estimation in dynamic panel models. J Econ Theory Econom. 2015;26(2):57–78.

    Google Scholar 

  122. Hwang J, Sun Y. Should we go one step further? An accurate comparison of one-step and two-step procedures in a generalized method of moments framework. J Econom. 2018;207(2):381–405.

    Article  MathSciNet  Google Scholar 

  123. Imbens GW. Generalized method of moments and empirical likelihood. J Bus Econ Stat. 2002;20(4):493–506.

    Article  MathSciNet  Google Scholar 

  124. Elith J, et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography (Cop). 2006;29(2):129–51.

    Article  ADS  Google Scholar 

  125. Babu SC, Gajanan SN. Introduction to food security, in Food Security, Poverty and Nutrition Policy Analysis, 2022, pp. 3–26.

  126. Kidane D, Woldemichael A. Does inflation kill? Exposure to food inflation and child mortality. Food Policy. 2020;92.

  127. Nugroho AD, Cubillos JP, Bopushev TST, Bozsik N, Fehér I, Lakner Z. Effects of Corruption Control on the number of undernourished people in developing countries. Foods. 2022;11(7).

  128. Taghizadeh-Hesary F. The impacts of air pollution on health and economy in Southeast Asia. Energies. 2020;13(7).

  129. Soko NN, Kaitibie S, Ratna NN. Does institutional quality affect the impact of public agricultural spending on food security in Sub-saharan Africa and Asia? Glob Food Sect. 2023;36.

  130. Salahodjaev R, Chepel S. Institutional quality and inflation. Mod Econ. 2014;05(03):219–23.

    Article  Google Scholar 

  131. Cicen YB. The Nexus between Institutional Quality and inflation in emerging markets: a panel causality test with a fourier function. Ekonomika. 2023;102(1):140–52.

    Article  Google Scholar 

  132. Opeloyeru OS, Faronbi TO, Raifu IA. The role of Institutional Quality in Health expenditure-labor force participation Nexus in Africa. J Knowl Econ. 2023.

    Article  Google Scholar 

  133. Sharma T, Anand, Sharma, Vipin, Shekhar. Institutional quality and health outcomes: evidence from the EU countries. Econ Bus Lett. 2022;11(2):70–8.

    Article  Google Scholar 

  134. Rahman MM, Rana R, Khanam R. Determinants of life expectancy in most polluted countries: Exploring the effect of environmental degradation, PLoS One, vol. 17, no. 1 January, 2022,

  135. Majeed MT, Ozturk I. Environmental degradation and population health outcomes: a global panel data analysis. Environ Sci Pollut Res. 2020;27:15901–11.

    Article  Google Scholar 

  136. Ahmad W, Ullah S, Ozturk I, Majeed MT. Does inflation instability affect environmental pollution? Fresh evidence from Asian economies. Energy Environ. 2021;32(7):1275–91.

    Article  Google Scholar 

  137. Jahanger A, Usman M, Murshed M, Mahmood H, Balsalobre-Lorente D. The linkages between natural resources, human capital, globalization, economic growth, financial development, and ecological footprint: the moderating role of technological innovations. Resour Policy. 2022;76.

  138. Pérez-Moreno S, Blanco-Arana MC, Bárcena-Martín E. Economic cycles and child mortality: a cross-national study of the least developed countries. Econ Hum Biol. 2016;22:14–23.

    Article  PubMed  Google Scholar 

  139. Kammerlander A, Schulze GG. Local economic growth and infant mortality. J Health Econ. 2023;87.

  140. Fotio HK, Gouenet RM, Ngo Tedga P. Beyond the direct effect of economic growth on child mortality in Sub-saharan Africa: does environmental degradation matter? Sustain Dev. 2023.

    Article  Google Scholar 

  141. Houeninvo HG. Effects of health expenditures on infant and child mortality rates: a dynamic panel data analysis of 37 African countries. Afr Dev Rev. 2022.

    Article  Google Scholar 

  142. Schneider MT, et al. Trends and outcomes in primary health care expenditures in low-income and middle-income countries, 2000–2017. BMJ Global Health. 2021;6(8).

  143. Nketiah-Amponsah E. The impact of Health expenditures on Health outcomes in Sub-saharan Africa. J Dev Soc. 2019;35(1):134–52.

    Article  Google Scholar 

  144. Ahmad R, Hasan J. Public health expenditure, governance and health outcomes in Malaysia. J Ekon Malaysia. 2016;50(1):29–40.

    Article  Google Scholar 

  145. Sinha A. Carbon emissions and Mortality Rates: a causal analysis for India (1971–2010). Munich Pers RePEc Arch, pp. 1–7, 2014.

  146. Rasoulinezhad E, Taghizadeh-Hesary F, Taghizadeh-Hesary F. How is mortality affected by fossil fuel consumption, CO2 emissions and economic factors in CIS region? Energies. 2020;13(9).

  147. Ogungbenle S, Rufus OO. Carbon emissions, Mortality Rate and Economic Growth Nexus in Nigeria. Int J Humanit Soc Stud. 2022;10(3).

  148. Adeleye BN, Olohunlana AO, Ibukun CO, Soremi T, Suleiman B. Mortality rate, carbon emissions, renewable energy and per capita income nexus in Sub-Saharan Africa, PLoS One, vol. 17, no. 9 September, 2022,

  149. Uzar U. The relationship between institutional quality and ecological footprint: is there a connection? Nat Resour Forum. 2021;45(4):380–96.

    Article  Google Scholar 

  150. Labra Lillo R, Torrecillas C. Estimando datos de panel dinámicos. Un enfoque práctico para abordar paneles largos. Rev Colomb Estad. 2018;41(1):31–52.

    Article  Google Scholar 

  151. Baum CF, Schaffer ME, Stillman S. Instrumental variables and GMM: estimation and testing. Stata J Promot Commun Stat Stata. 2003;3(1):1–31.

    Article  Google Scholar 

Download references


Not applicable.


This research did not receive any funds from any profit or not-for-profit organizations.

Author information

Authors and Affiliations



Mohammad Naim Azimi: Data curation, formal analysis, investigation, methodology, resources, software, and visualization. Mohammad Mafizur Rahman: Conceptualization, project administration, supervision, and validation.

Corresponding author

Correspondence to Mohammad Naim Azimi.

Ethics declarations

Ethical approval

This study did not employ any animal or human as the process of its experimentation.

Consent for publication

Not applicable.

Consent for publication

All authors have read and approved the study for submission and publication.

Conflict of interest

The authors of the present study do not have any competing interest to declare.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Azimi, M.N., Rahman, M.M. Food insecurity, environment, institutional quality, and health outcomes: evidence from South Asia. Global Health 20, 21 (2024).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: