Does higher education matter in mitigating chronic disease Mortality? Evidence from MENA countries with consideration of Globalization, economic Growth, and environmental pollution

Zouine, Marouane; El Adnani, Mohamed Jallal; Salhi, Salah Eddine Article Does higher education matter in mitigating chronic disease Mortality? Evidence from MENA countries with consideration of Globalization, economic Growth, and environmental pollution Research in Globalization Provided in Cooperation with: Elsevier Suggested Citation: Zouine, Marouane; El Adnani, Mohamed Jallal; Salhi, Salah Eddine (2024) : Does higher education matter in mitigating chronic disease Mortality? Evidence from MENA countries with consideration of Globalization, economic Growth, and environmental pollution, Research in Globalization, ISSN 2590-051X, Elsevier, Amsterdam, Vol. 9, pp. 1-10, https://doi.org/10.1016/j.resglo.2024.100236 This Version is available at: https://hdl.handle.net/10419/331162 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-nc/4.0/ Research in Globalization 9 (2024) 100236 Available online 27 June 2024 2590-051X/© 2024 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/). Does higher education matter in mitigating chronic disease Mortality? evidence from MENA countries with consideration of Globalization, economic Growth, and environmental pollution Marouane Zouine * , Mohamed Jallal el adnani , Salah eddine salhi Faculty of Economics and Management. Sultan Moulay Slimane University: Beni-Mellal, Morocco ARTICLE INFO Keywords: Higher education MENA countries Globalization Carbon dioxide emissions (co2 emissions) Economic growth ABSTRACT This study investigates the relationship between higher education and mortality rates from chronic and fatal diseases in MENA countries from 2000 to 2020. Utilizing a robust panel econometric framework with Generalized Least Square, Fully Modified Ordinary Least Squares (FMOLS), and Dynamic Ordinary Least Squares (DOLS), we analyze empirical data while controlling for globalization, CO2 emissions, and GDP per capita. Our findings reveal a significant negative correlation between education and mortality rates, paralleled by similar trends for globalization and GDP per capita. Conversely, CO2 emissions are found to increase mortality rates, highlighting the detrimental impact of environmental degradation on public health. This underscores the pivotal role of education, globalization, and economic development in reducing mortality rates associated with chronic and fatal diseases. The study advocates for increased investments in education and healthcare infrastructure to address disparities and enhance public health outcomes, alongside promoting responsible globalization practices to further improve health metrics. Introduction Education plays a crucial role in driving societal progress, influencing not only economic growth, unemployment rates, and environmental sustainability but also broader aspects of public health. Research by Su et al. (2021), Li et al. (2021), and Zouine et al. (2024) highlights how education boosts productivity, fosters innovation, and ensures economic stability by equipping individuals with essential skills and knowledge for success in a competitive global landscape. Beyond its economic impacts, education significantly influences public health outcomes. Individuals with higher levels of education tend to adopt healthier lifestyles, access preventive healthcare services, and make informed health decisions, as noted by Zouine & El Adnani (2023). This educated population contributes to healthier communities, reduces healthcare expenditures, and enhances overall well-being. Thus, education plays a multifaceted role, not only in advancing economies but also in promoting better health outcomes and a higher quality of life for society at large. In the broader context of global health policymaking, the pivotal role of education in shaping health outcomes often remains underappreciated. Numerous studies have established that education serves as a potent health intervention, empowering individuals with knowledge and resources to proactively manage their well-being. Individuals with higher levels of education tend to exhibit greater awareness of health issues, leading to early preventive measures and timely access to healthcare services. This correlation is not merely anecdotal; empirical evidence demonstrates that higher educational attainment correlates with improved health outcomes and reduced mortality rates, particularly in the context of chronic and fatal diseases. In the Middle East and North Africa (MENA) region, chronic diseases such as cardiovascular diseases (CVDs) cancers, diabetes, and chronic respiratory problems are on the rise, posing significant challenges to public health. According to the World Bank (2019), these diseases have emerged as leading causes of morbidity and mortality in the MENA region, with approximately 70 % of deaths attributed to noncommunicable diseases (NCDs). Specifically, in 2020, the region witnessed approximately 461,000 new cancer cases and over 274,000 cancer-related deaths. Additionally, CVDs, including heart disease and stroke, accounted for over 810,000 deaths in 2019, while chronic respiratory diseases, such as chronic obstructive pulmonary disease (COPD), resulted in approximately 3.2 million disability-adjusted life years (DALYs) lost the same year. * Corresponding author. E-mail address: [email protected] (M. Zouine). Contents lists available at ScienceDirect Research in Globalization journal homepage: www.sciencedirect.com/journal/research-in-globalization https://doi.org/10.1016/j.resglo.2024.100236 Received 4 May 2024; Received in revised form 19 June 2024; Accepted 25 June 2024 Research in Globalization 9 (2024) 100236 2 Given the alarming prevalence and impact of these chronic diseases, investing in education emerges as a strategic imperative for public health in the MENA region. However, despite the escalating disease burden, the potential impact of higher education in mitigating the risks associated with these diseases remains relatively unexplored in the regional context. While education is widely recognized as a determinant of health outcomes globally, empirical research specific to the MENA region is limited. This underscores the need for comprehensive studies that examine the relationship between higher education and mortality rates related to chronic and fatal diseases in MENA countries. In addition to investigating the impact of higher education on health outcomes, our study will delve into the broader contextual factors that influence disease prevalence in MENA countries. Globalization, characterized by increased interconnectedness and the exchange of knowledge and goods, has been highlighted in recent research by Quasinowski and Liu (2020) and Jani et al. (2019) as a significant factor shaping the patterns of chronic diseases. Understanding how globalization affects disease transmission and prevalence in the MENA region is crucial for developing effective public health strategies. Furthermore, our study will explore the intricate relationship between economic development, as measured by GDP per capita, and chronic diseases. Extensive research, such as that by Bloom et al. (2020), demonstrates the complex interplay between economic growth and the burden of diseases like diabetes, CVD, and cancer. This underscores the importance of considering economic factors in designing and implementing health interventions tailored to local contexts. In addition to the detrimental health effects of CO2 emissions on chronic diseases, the preservation of the environment and the importance of reducing CO2 emissions have been highlighted in studies by Boubaker et al. (2024), Hasan (2024), and Shah et al. (2024). These studies emphasize that CO2 emissions contribute significantly to environmental pollution and climate change, amplifying health risks. Addressing these environmental health challenges requires comprehensive strategies that integrate public health policies with environmental sustainability goals. Furthermore, the detrimental health effects of CO2 emissions on chronic diseases have been well-documented in studies by Pontzer (2021) and Erdogan et al. (2020). These emissions contribute to environmental pollution and climate change, exacerbating health risks associated with respiratory diseases, cardiovascular disorders, and other chronic conditions. Addressing these environmental health challenges requires comprehensive strategies that integrate public health policies with environmental sustainability goals. The relationship between higher education and chronic disease mortality in the MENA region remains underexplored, despite its significance for public health and policy. Unlike more developed regions where extensive research has linked education levels to improved health outcomes, studies specific to MENA countries are limited. This gap is critical because the region faces unique challenges such as rapid urbanization, changing lifestyles, and varying healthcare access, which can significantly impact disease prevalence and mortality rates. Understanding how higher education influences mortality in MENA countries is therefore crucial for designing targeted interventions and policies that can effectively reduce disease burdens and improve population health. To comprehensively address these research gaps, our study will utilize rigorous econometric analysis grounded in theoretical frameworks such as Phelan and Bruce Link’s Fundamental Cause Theory and Gary Becker’s human capital theory (HCP). This theoretical foundation will guide our investigation into the complex interplay between education, environmental factors, and mortality rates associated with chronic and fatal diseases in the MENA region. Employing advanced panel data analysis techniques, including cross-dependency tests, unit root tests, Kao cointegration tests, generalized least squares (GLS), Fully Modified OLS (FMOLS), and dynamic OLS (DOLS) estimators, we aim to provide a nuanced understanding of these relationships. Our main contribution lies in empirically examining how higher education influences mortality rates related to chronic and fatal diseases within the MENA context by highlight education as a fundamental component of effective public health strategies. By leveraging robust econometric methods, our study aims to uncover the specific mechanisms through which education impacts health outcomes, thereby filling a critical gap in current literature. Moreover, we seek to extend the theoretical implications of Fundamental Cause Theory and Human Capital Theory by demonstrating their applicability in understanding health disparities and informing policy interventions. Throughout this paper, we will conduct an exhaustive review of the literature, synthesizing both theoretical frameworks and empirical studies to examine recent contributions on the impact of education, CO2 emissions, globalization, and economic growth on mortality rates associated with chronic and fatal diseases. In subsequent sections, we will explicate the methodology employed in this study, delineating the geographical context of our investigation (the MENA region) and the conceptual framework guiding our research model. Following this, we will rigorously analyze and interpret the findings derived from our empirical analysis. Lastly, the paper will conclude with a comprehensive synthesis of the results, accompanied by recommendations informed by our research outcomes. This systematic approach ensures a rigorous exploration of the research inquiry, offering valuable insights pertinent to scholarly discourse and policy formulation. 1. Literature review 1.1. Theoretical background The Fundamental Cause Theory posits that education acts as a fundamental cause of health disparities by influencing access to multiple resources critical for health. Education affects health outcomes through various interconnected mechanisms rooted in socioeconomic status (SES), health behaviors, and access to healthcare. Higher levels of education are associated with higher SES, which provides individuals with greater financial resources, better job opportunities, and improved access to healthcare services (Phelan et al., 2010). Socioeconomic advantages linked to education facilitate healthier living environments, including access to nutritious food, safe housing, and environments conducive to physical activity. These factors contribute significantly to reducing the risk factors for chronic diseases such as cardiovascular disease, cancer, diabetes, and chronic respiratory diseases. Additionally, education enhances health literacy, empowering individuals to understand and navigate health information, make informed decisions about preventive measures, and engage in healthier lifestyles (Zajacova & Lawrence, 2018). Furthermore, education influences health behaviors through its impact on cognitive abilities and social norms. Educated individuals are more likely to adopt health-promoting behaviors such as regular physical exercise, balanced diets, and avoiding harmful substances like tobacco and excessive alcohol consumption (Freese & Lutfey, 2011). They also tend to have better adherence to medical treatments and preventive healthcare practices, leading to early detection of diseases and improved health outcomes over the lifespan. Fundamental Cause Theory emphasizes the persistent nature of education’s impact on health disparities. It suggests that even as specific pathways linking education to health outcomes may evolve, the fundamental advantage of education in providing individuals with the skills and resources to manage health risks remains robust. This theory underscores the broader societal implications of education policies and interventions aimed at reducing health inequalities across different socioeconomic strata (Link & Phelan, 1995). In contrast, Human Capital Theory views education as an investment in enhancing human capital, which encompasses both productive skills and health capabilities. According to HCT, education enhances health outcomes by equipping individuals with knowledge and skills necessary for maintaining and improving health (Becker, 2009). M. Zouine et al. Research in Globalization 9 (2024) 100236 3 Educated individuals are more likely to engage in proactive health behaviors and preventive measures, contributing to better health outcomes and reduced healthcare costs. HCT argues that education enhances health through multiple pathways. Firstly, education increases health literacy, enabling individuals to understand health information, make informed decisions about their health, and advocate for their healthcare needs. Secondly, education fosters critical thinking and problem-solving skills, which are essential for navigating complex healthcare systems and managing chronic conditions effectively (Schultz, 1982; Bradley & Corwyn, 2002). Moreover, HCT highlights the economic benefits of investing in education for health. Educated individuals tend to have higher earning potentials and better job stability, which translates into improved access to healthcare services and resources. A healthier workforce contributes to higher productivity, reduced absenteeism, and overall economic growth, underscoring the broader societal benefits of education in promoting health and well-being (Feinstein et al., 2006). To conclude this subsection, both Fundamental Cause Theory and Human Capital Theory provide comprehensive frameworks for understanding the intricate relationship between education and health outcomes. FCT emphasizes education’s role in shaping health disparities through socioeconomic pathways and health behaviors, highlighting its enduring impact on health across different populations. In contrast, HCT underscores education’s role as an investment in enhancing human capital, promoting healthier behaviors, and improving individual and population health outcomes. Integrating these theoretical perspectives enhances our understanding of how education influences mortality rates related to chronic and fatal diseases, guiding effective policies and interventions aimed at improving public health and reducing health inequities. 1.2. Empirical perspective 1.2.1. Education and mortality The studies presented delve into the intricate nexus between education, health literacy, and chronic diseases, with a focus on CVD, cancer, diabetes, and chronic respiratory diseases. These investigations collectively underscore the profound influence of education and health literacy on disease awareness, prevention, management, and overall health outcomes. In the realm of cancer, Asuquo & Olajide (2015) highlight the pivotal role of health education in enhancing breast cancer awareness among female undergraduates, leading to improved understanding of symptoms and risk factors, as well as increased self-examination practices. Del Carmen et al. (2021) emphasize the effectiveness of educational interventions in breast cancer prevention and health promotion, advocating for comprehensive strategies targeting both men and women across all life stages. Additionally, Soni (2007) cites the integration of health education into curriculums as an effective approach to enhancing health literacy and raising awareness about breast cancer. Within the domain of cardiovascular disease, Boateng et al. (2017) underscore the importance of education and location in shaping understanding and awareness of CVD among sub-Saharan African populations, particularly among individuals with low education levels and those residing in rural areas. Safeer et al. (2006) emphasize the critical role of health literacy in promoting better communication, adherence to medical treatments, and overall health, with implications for preventing heart disease. Furthermore, Hawthorne et al. (1999) highlight the correlation between low literacy levels and tobacco use, a significant risk factor for heart disease. Regarding diabetes, Saeed et al. (2018) highlights the association between inadequate education, health literacy, and poor glycemic control, emphasizing the need for educational programs to enhance functional health literacy among diabetic patients. Gaffari-Fam et al. (2020) underscore the significant predictive value of health literacy and self-care behaviors in improving health-related quality of life among individuals with type 2 diabetes mellitus. Additionally, Reisi et al. (2016) stress the importance of self-efficacy theory in patient education programs for successful diabetes self-management. In the realm of chronic respiratory diseases, Mackey et al. (2017) reveal the correlation between limited knowledge, low literacy levels, and negative beliefs about respiratory diseases, underscoring the importance of health literacy in disease management and prevention. Puente-Maestu et al. (2016) shed light on the disparity in understanding and literacy levels among COPD patients, advocating for tailored interventions to improve health outcomes. Moreover, Taggart et al. (2012) demonstrate the impact of increasing health literacy levels on reducing risk behaviors associated with chronic respiratory diseases. These studies collectively underscore the pivotal role of education and health literacy in shaping disease understanding, prevention, and management across various chronic conditions, highlighting the need for comprehensive educational interventions and targeted health promotion strategies to address health disparities and improve population health. 1.2.2. CO2 emissions and mortality Jonathan (2007) delves into the intricate relationship between climate change and its health impacts, particularly focusing on chronic diseases. It emphasizes the disproportionate burden faced by vulnerable populations, such as children under 5 years old, and discusses the concept of “natural debt” to quantify the responsibility for current warming. The study advocates for equitable solutions to protect these vulnerable groups and mitigate the health impacts of climate changerelated chronic diseases. In line with this perspective, Bierwirth et al. (2018) and Jacobson et al. (2019) explore the health risks associated with elevated carbon dioxide (CO2) levels, specifically chronic diseases. They discuss the potential adverse effects of chronic exposure to high CO2 concentrations indoors, including inflammation, cognitive impairment, bone demineralization, and kidney calcification. These studies highlight the urgent need for further research to quantify these risks and develop effective mitigation strategies to protect vulnerable populations from chronic diseases linked to CO2 exposure. In a related context, Erdogan et al. (2020) analyze the relationship between carbon dioxide emissions and health expenditures in BRICS-T countries, with a specific focus on chronic diseases. Their findings suggest that increasing CO2 emissions contribute to higher health expenditures, indicating the economic impact of carbon emissions on chronic disease management and healthcare costs. Furthermore, Ehsan et al. (2020) explore the intricate relationship between economic growth, fossil fuel consumption, environmental pollution, and mortality due to chronic diseases in CIS countries. Their study reveals that variations in CO2 emissions and increased fossil fuel consumption correlate positively with mortality rates from cardiovascular disease, diabetes mellitus, cancer, and chronic respiratory diseases. These findings underscore the critical necessity for policy interventions aimed at transitioning towards renewable energy sources to alleviate the burden of chronic diseases linked to environmental pollution. Pontzer (2021) synthesizes existing evidence to elucidate the interconnections between climate change and chronic diseases, emphasizing humanity’s reliance on external energy sources as a central factor. The study highlights the adverse health impacts of industrialization and fossil fuel consumption, which significantly contribute to the prevalence of chronic diseases. It advocates for urgent action towards adopting carbon–neutral energy solutions to mitigate climate change and reduce the health risks associated with chronic diseases. Additionally, Pontzer underscores the importance of comprehensive strategies that address the multifaceted factors influencing chronic disease prevalence in global health contexts. Globalization and mortality. Globalization’s impact on chronic and fatal diseases is multifaceted, necessitating in-depth examination. This M. Zouine et al. Research in Globalization 9 (2024) 100236 4 intersection encompasses various factors such as dietary patterns, economic policies, and environmental changes. Understanding these complexities is crucial for devising effective public health interventions. Analyzing the links between globalization and disease burden requires a comprehensive approach that considers socio-economic disparities, cultural influences, and policy frameworks. Addressing this intricate relationship is essential for mitigating the global burden of chronic and fatal diseases. Hawkes (2006) delves into the phenomenon of the “nutrition transition” driven by globalization, which has led to increased consumption of foods high in fats and sweeteners, contributing to the rise of obesity and diet-related chronic diseases worldwide. The paper examines how globalization affects agri-food systems, altering the availability, type, cost, and desirability of foods. Specific mechanisms of market integration, such as production and trade of agricultural goods, foreign direct investment in food processing and retailing, and global food advertising and promotion, are highlighted as drivers of dietary change. The paper emphasizes the need for policymakers to understand these links to develop effective policies addressing the global burden of chronic disease. Weisz and Vignola-Gagn´ e (2015) provide insights into the policy landscape surrounding chronic noncommunicable diseases (NCDs) in lowand middle-income countries. The emergence of NCDs as a major concern is traced back to collaborative efforts between experts from Eastern and Western Europe in the late 1980 s. The Global Burden of Disease study provided critical evidence for NCD activism, leading to revitalization of the World Health Organization’s normative and coordinative functions. However, despite widening concern, major reallocation of funding toward NCD programs in the developing world has not yet materialized. Siiba et al. (2021) explore the intertwined challenges of climate change and NCDs, particularly in Africa, where these synergistic effects pose significant risks to achieving sustainable development goals. The review identifies pathways through which climate change and globalization influence NCDs, including reduction in food production and nutrition, urbanization, and transformation of food systems. The findings underscore the need for effective policy and public health interventions to mitigate the effects of climate change and globalization on the rising burden of NCDs. Cuevas et al. (2019) investigate the role of economic globalization and trade liberalization in shaping nutritional outcomes, particularly in lowand middle-income countries. While the impacts of globalization on nutritional outcomes remain mixed, foreign direct investment (FDI) is associated with increases in overnutrition and NCD prevalence. The study highlights the importance of nutrition-sensitive trade policy and regulation of FDI to address all forms of malnutrition effectively. Jani et al. (2019) empirically analyzes the impact of globalization on health indicators using panel data. They find that globalization has a positive impact on health, with economic globalization exerting the highest influence on health indicators in less developed countries. However, as countries develop, the social dimension of globalization becomes more important, indicating a nuanced relationship between globalization and health outcomes. Quasinowski and Liu (2020) adopt a sociological approach to understand the emergence of cardiovascular diseases as global health concerns. They trace the globalisation of cardiology, particularly heart failure research, identifying preconditions such as technological innovations, organizational infrastructure, and internationally standardized nomenclature. The study sheds light on the social mechanisms that have propelled cardiovascular diseases onto the global health agenda. 1.2.3. GDP and mortality Understanding how economic growth impacts chronic and fatal diseases is crucial for public health and economic development. As economies grow, lifestyle changes often lead to an increased prevalence of chronic conditions like cardiovascular diseases and diabetes. Therefore, investigating this relationship is vital for developing effective policies to address the rising burden of such diseases. Bloom et al. (2020) propose an innovative framework to assess the macroeconomic impact of economic growth on chronic and fatal diseases, particularly non-communicable diseases (NCDs). By integrating disease prevalence into a human capital augmented production function, they quantify the economic burden of chronic health conditions in terms of foregone GDP. Applying this methodology to China, Japan, and South Korea, they estimate substantial losses associated with cardiovascular diseases, cancer, chronic respiratory diseases, diabetes, and mental health conditions over the period 2010–2030. Dadgar and Norstr¨ om (2022) investigate the intricate relationship between macroeconomic fluctuations driven by economic growth and the prevalence of chronic and fatal diseases across 21 OECD countries from 1960 to 2018. Their analysis reveals that increases in unemployment are associated with decreases in most mortality outcomes, while GDP growth exhibits long-term protective effects on mortality, highlighting the multifaceted dynamics between economic conditions and population health. Su and Zheng (2023) delve into China’s mortality trends over the past four decades, emphasizing the impact of economic growth on the transition from infectious diseases to chronic and fatal diseases, particularly non-communicable diseases (NCDs). They attribute this shift to risk factors like smoking, poor diet, and physical inactivity, which have led to a surge in chronic conditions such as cardiovascular diseases, cancers, chronic respiratory disorders, and diabetes. Additionally, they underscore the importance of prioritizing chronic disease prevention and strengthening primary healthcare to address this evolving health landscape amidst economic growth. Cupido (2020) explores the mortality modeling of multiple populations with a focus on the impact of economic growth on chronic and fatal diseases. Enriching factor-based mortality models with socioeconomic determinants, Cupido highlights the significance of interpretable spatial model features in understanding the spatial impact of economic growth on mortality. By extending the Li-Lee factor-based stochastic mortality modeling framework to include economic growth and spatial patterns of mortality, the study sheds light on the complex interplay between economic growth and chronic and fatal diseases. 2. Research design and methodology In our study, we aimed to assess the impact of higher education, globalization, economic growth, and CO2 emissions on mortality rates related to chronic and fatal diseases. To achieve this, we utilized a dataset primarily sourced from the World Bank, providing comprehensive data on mortality from cardiovascular diseases, cancer, diabetes, and chronic respiratory diseases across various countries. Our panel analysis encompasses 16 MENA countries, selected based on contextual relevance developed in the introduction and data availability. The table below provides a concise overview of the variables integrated into our research model, emphasizing key indicators relevant to mortality and socioeconomic factors. The Table 1 describe the variables used in our model. 2.1. Empirical model and estimation Our study undertakes a comprehensive exploration of the intricate nexus between higher education, globalization, economic growth, and CO2 emissions in the context of chronic and fatal diseases across MENA countries from 2000 to 2020. To establish a robust analytical framework, we meticulously collected extensive data for key variables. The formulated model: ln(MORT)it = α +β1ln(EDU)it +β2ln(GLOB)it +β3ln(GDP)it +β4ln(CO2)it +eit (1) M. Zouine et al. Research in Globalization 9 (2024) 100236 5 was crafted to delve into the nuanced impact of higher education, globalization, economic growth, and CO2 emissions on mortality rates associated with chronic and fatal diseases. Our methodological approach encompasses a comprehensive suite of tests, ranging from exploratory data analysis and diagnostic assessments to multicollinearity checks and correlation analyses. Specifically, we utilized Kao’s cointegration test to examine long-term relationships among variables and the Q-statistic to assess model diagnostics, including the detection of serial autocorrelation. Moreover, we conducted GLS, FMOLS, and DOLS analyses to thoroughly investigate potential effects and ensure the robustness of our findings. This transparent and replicable methodology enhances the study’s credibility, contributing to an advanced understanding of the multifaceted dynamics at the intersection of education, globalization, economic growth, CO2 emissions, and mortality rates from chronic and fatal diseases in the MENA region. In our study, the core focus lies in examining whether higher education contributes to the mitigation of chronic diseases, specifically targeting the roles of globalization, CO2 emissions, and GDP. The inclusion of these variables is justified by their significant impacts on both health outcomes and environmental sustainability in the MENA region. This selection aligns with the region’s economic dynamics, characterized by rapid globalization, substantial CO2 emissions, and GDP growth. We hypothesize that higher education will demonstrate a negative coefficient, indicating a potential association with lower chronic disease rates. This expectation is supported by theories such as Human Capital Theory, which posits that education enhances health literacy and promotes healthier lifestyles, thereby reducing disease incidence. Conversely, CO2 emissions are expected to show a positive coefficient, suggesting a potential exacerbating effect on chronic diseases. Extensive literature has documented the adverse health impacts of environmental pollution from CO2 emissions, including respiratory and cardiovascular diseases. Our study seeks to quantify this relationship within the MENA context, highlighting the urgent need for environmental policies that promote sustainable development and mitigate health risks. Globalization is anticipated to exhibit a negative coefficient, reflecting its potential role in disseminating medical knowledge, technologies, and resources that contribute to improved health outcomes. However, globalization’s impact on health outcomes can vary, influenced by factors such as trade policies, healthcare access, and socioeconomic disparities across different countries within the MENA region. GDP growth is expected to correlate negatively with chronic disease mortality rates, indicative of improved healthcare infrastructure and standards of living associated with economic development. This relationship underscores the importance of economic policies that prioritize public health investments and access to quality healthcare services. 2.2. Estimation model This article employs three distinct estimating approaches to investigate the relationship between higher education and chronic and fatal diseases. Initially, we utilize panel data comprising 16 countries, leveraging the GLS effect method. GLS effectively addresses heteroscedasticity and enhances estimate precision by adjusting for characteristics that remain constant over time or among individuals. Furthermore, GLS is particularly suitable for panel data analysis, accommodating fixed effects that capture unique individual or entity attributes. This method aims to yield robust estimates by simultaneously considering heteroscedasticity and fixed effects within our analytical framework. Noting that estimating specification through GLS yields exactly the same results as the OLS estimation (Frondel,& Vance, 2010) as we can prove: yit =β0+βʹxit +ξi+ ν it,i=1,⋯,N,t=1,⋯,T(2) The standard panel data model in equation (2) specifies yit as a function of time-varying it, individual-specific ξi and idiosyncratic errors ν ν it, To estimate both fixed and between-groups effects, equation (3) refines this model by incorporating deviations of xit from its mean x−i captured by w and b respectively: yit =β0+wʹ(xit −x−i) + bʹx−i+ξi+ ν it (3) Using OLS, we can simultaneously estimate these effects. Averaging equation (3) over time cancels out the term involving w, making it equivalent to the time-averaged form of equation (1): y−i=β0+bʹx−i+ξi+v−i(4) subtracting this averaged equation from Equation (3) results in: yit −y−i=wʹ(xit −x−i) + ν it −v−i(5) Thus, demeaning Equation 2 or 3 and estimating via OLS provides fixedeffects estimates. Estimating panel model in equation (2) via GLS is equivalent to estimating: yit −λ⋅y−i=β0⋅(1−λ) + βʹ(xit −λ⋅x−i) + ξi−λ⋅ξi+ ν it −λ⋅v−i(6) via OLS, where λ adjusts for the time horizon and error variances ξi and it. Employing the same transformation to Equation (3) yields: yit −λ⋅y−i=β0⋅(1−λ) + wʹ(xit −λ⋅x−i) − λ⋅wʹ(xit −x) + bʹx−i −λ⋅bʹx−i+ξi−λ⋅ξi+ ν it −λ⋅v−i(7) Recognizing that (xit −x) = 0 and rearranging gives: Table 1 descriptive variables. Variable Description Mortality from CVD, cancer, diabetes or CRD between exact ages 30 and 70 (%) Likelihood of death from CVD, cancer, diabetes, or CRD between ages 30 and 70. Gross enrollment rates. Tertiary The tertiary education enrollment rate refers to the total proportion of individuals enrolled in tertiary education, regardless of age, expressed as a percentage of the total population of a group of individuals over a five-year period after completion of secondary school Co2 Emission Sources of carbon dioxide emissions include fossil fuel combustion and cement production, which generate carbon dioxide through the consumption of solid, liquid and gaseous fuels, as well as through the flaring of gases. GDP per capita (current US$) To calculate GDP per capita, we divide the gross domestic product by the population in the middle of the year. in turn, is the sum of the gross value added by all producers who reside in the economy, including any taxes on products and excluding any subsidies not factored into the product value. KOF Globalization Index The KOF Globalization Index is designed to assess the degree of globalization of nations worldwide. It is based on three fundamental dimensions: economy, society and politics. Using these dimensions, the index seeks to measure economic exchanges, economic constraints, information flows, people-to-people contacts and cultural proximity of the countries under study. Its objective is to provide a comprehensive assessment of globalization M. Zouine et al. Research in Globalization 9 (2024) 100236 6 yit =β0+wʹ(xit −x−i) + bʹx−i+ξi+ ν it +λ⋅(y−i−β0−bʹx−i−ξi−v−i) (8) where(y−−β0−bʹx−i−ξi−v−i) = 0 Which conclude that both transformations and specifications are identical, demonstrating that OLS estimation of equation (2) yields the same results as estimating the transformed equations via OLS, which is equivalent to estimating equation (3) via GLS. Next, to assess cointegration and ascertain potential long-term relationships among variables, we employ the panel unit root test and Kao cointegration test. Additionally, we adopt dynamic FMOLS and DOLS, introduced by Saikkonen (1991) and Stock and Watson (1993), to estimate equilibrium coefficients in higher-order cointegration systems. These methods address issues such as small sample bias, simultaneity bias, and endogeneity, enhancing the accuracy of our long-term effect analysis. To derive results for specific panels, panel unit-root tests transform individual ADF unit-root tests into a single panel. Baltagi (2008) noted that the high power of these tests has increased their acceptance and popularity among econometricians and economists. The fundamental form of the panel unit-root test is expressed as: yit =Θiyi,t−1+Xitʹδi+∊it (9) where i =1,2,…,Ni =1, 2,…,N cross-section units or series under observation over time frame t =1,2,…,Ti.Xit is the exogenous variable in the model, with any individual trends or fixed effects. The symbol Θirepresents the autoregressive coefficient, while the error term ∊it is assumed to be a mutually independent individual disturbance. The lag order ppp is allowed to vary across individuals. The process operates as follows: first, the ADF test is run for each cross-section, as shown in equation (10) Δyit =Θiyi,t−1+∑θiLΔyi,t−L+amidmt +eit (10) In the second step, two auxiliary regressions were computed: Δyit on Δyi,t−L and dmt to obtain the residuals eit and yi,t−1 on Δyi,t−L and dmt to obtain the residuals vi ,t−1. In the next step, residual standardization was carried out by performing the following calculations: eit=∊it/ σ i(11) yi ,t−1=∊i ,t−1/ σ i(12) where σ i signifies the standard error obtained from each ADF. Finally, the pooled OLS regression was computed using Equation (13): eit= ρ yi ,t−1+∊it(13) In the context of the LLC test, the null hypothesis assumes no unit root. The standard deviation, or t-statistics, must be adjusted for accurate testing. The LLC test requires certain conditions to be met for validity, involving specific relationships between the cross-sectional and time dimensions. To verify the robustness of the results, additional unit-root tests, such as the PP-Fisher and ADF-Fisher Chi-square tests, are used. Regarding the panel cointegration tests, the presence of a long-term relationship was examined using the Pedroni, Kao, and Johansen Fisher cointegration tests. Pedroni introduced several test proposals in 1999 that accommodate heterogeneity in cointegration analysis (Asteriou and Hall, 2007) Pedroni’s test allows for heterogeneity among cointegrated vectors in both the short term and the long term. Similarly, the Kao (1999) cointegration test acknowledges the heterogeneity between cointegration vectors, but it violates the rule of endogeneity of independent variables due to asymptotic equivalence. The cointegration regression equation is represented as follows: yit = α i+δit +β1ix1it +β2ix2it +⋯+βMixMit +eit (14) In equation (14), T represents the number of observations, N represents the number of individuals on the panel, and M represents the number of regression variables. Since there are N individuals on the panel, there will be N different equations for each of the M regressors. The coefficients β1i,β2i,…,βMi represent the differences between individuals in the panel. The parameter α i is the constant effects parameter that captures individual-specific differences. Additionally, if there is a deterministic trend among individuals in the panel, the parameter δit is added to the equation. The FMOLS method, originally developed by Phillips and Hansen (1990), is designed for optimal co-integrating regression estimation. However, in this study, the Pedroni (1999) heterogeneous FMOLS estimator was utilized for the panel cointegration regression. This choice offers advantages such as correcting for endogeneity bias and addressing serial correlation issues (Pedroni, 1999; Hamit-Haggar, 2012). According to Hamit-Haggar (2012), FMOLS is particularly suitable for panel data analysis involving heterogeneous cointegration structures. In the panel FMOLS estimator applied to the coefficient β of the model, the methodology accounts for these complexities in the data structure, enhancing the robustness and reliability of the regression results, the equation is: β* NT −β=(∑N i=1L−2 22i∑T t=1(Xit −Xi)2)−1∑N i=1L−1 11iL−1 22i(∑T t=1(Xit −Xi) μ * it −Tγi) (15) where μ * it = μ it −L21i L22i ΔXit,γi=r21i Ω0 21i−L21i L22i(Γ22i+ Ω0 21i) Finaly, DOLS estimator shares the same asymptotic distribution as the panel FMOLS estimator derived by Pedroni (1999). Both the DOLS and FMOLS estimations were conducted to ensure the consistency and reliability of the results. 2.3. Result and discussion Before presenting the estimation of the empirical model, the table provides an overview of the descriptive statistics for all variables, as explained below. author’s estimation on EVIEWS 12. Table 2 shows that the mean mortality rate is 9.16, with a median of 3.19. The highest recorded mortality rate is approximately 47.65, and the lowest is around 0.96. For education, the mean is about 33.48. The Gross Domestic Product mean is approximately 13771.11. The globalization index mean is around 59.98. Carbon dioxide emissions have a mean of approximately 24199401. The notable disparity between the mean and median values of the variables indicates a non-random distribution within the dataset. Table 2 Descriptive Statistic of the Variables. MORT EDU GDP GLOB CO2 Mean 9.162752 33.47576 13771.11 59.97860 24,199,401 Median 3.187006 31.76469 4146.407 62.26858 10,094,561 Maximum 47.65131 72.96105 98041.36 79.48945 1.04E +08 Minimum 0.962827 9.314580 664.3417 31.90133 678831.0 Std. Dev. 11.28613 15.42167 19277.72 11.96264 28,495,436 Sum 1969.992 7197.287 2960788. 12895.40 5.20E +09 Sum Sq. Dev. 27258.64 50895.20 7.95E + 10 30624.41 1.74E +17 Observations 215 215 215 215 215 M. Zouine et al. Research in Globalization 9 (2024) 100236 7 Specifically, when the mean substantially differs from the median, it suggests that the distribution is skewed or asymmetrical. In this context, such a difference implies that the distribution of the data points is not evenly spread around the center but rather clustered towards one end of the distribution. The subsequent analysis will focus on verifying the normality assumption within the dataset, as indicated in the forthcoming table. author’s estimation on EVIEWS 12 *, **, ***indicate significance respectively at the 1 %, 5 % and. Table 3 demonstrate that the results of the Jarque-Bera test suggest that there is insufficient evidence to reject the null hypothesis, implying that the residuals follow a normal distribution. However, it’s worth noting that the data exhibits slight skewness towards the negative side, as evidenced by the negative skewness value. This suggests that while the overall distribution may approximate normality, there are deviations that warrant consideration, particularly in terms of the skewed tail towards lower values. Further examination and potentially additional statistical tests may be warranted to fully assess the normality assumption and its implications for the analysis. Table 4 shows that the Centered Variance Inflation Factor (VIF) assesses multicollinearity in regression models. In this analysis, the VIF values for the variables—EDU (4.12), GDP (2.31), GLOB (4.85), and CO2 (3.01)— are all below the conventional threshold of 10, indicating low multicollinearity. With no VIF value surpassing this threshold, there is no significant evidence of multicollinearity among the variables. This suggests that the predictor variables do not excessively correlate with each other, enhancing the reliability of the regression analysis. The absence of multicollinearity holds significant implications for the interpretation of regression results. With low multicollinearity, the regression coefficients can be interpreted with greater confidence. Absent multicollinearity-related issues such as inflated standard errors, the regression coefficients provide a stable representation of the relationships between the predictor variables and the outcome variable. Thus, the absence of multicollinearity reinforces the validity of the regression analysis and increases confidence in the interpretation of its findings. The analysis of Table 5 reveals that the Q statistics are consistently non-significant across all lag specifications. This means that the Q test fails to reject the null hypothesis, indicating that there is no significant autocorrelation of errors in the model. In other words, the patterns observed in the residuals are likely due to randomness rather than systematic trends or correlations over time. This finding reaffirms the earlier observation that the distribution of residuals appears to be random, further bolstering the confidence in the validity of the regression model’s results. in Fig. 1 suggests that they conform to a normal distribution, indicating the absence of heteroscedasticity in our model. This observation highlights the robustness of the model and its suitability for the observed data. In the upcoming phase, detailed in the subsequent table, the focus shifts to assessing the presence of a unit root through three distinct tests: Levin, Lin, and Chu (LLC), Augmented Dickey-Fuller (ADF), and Breitung tests. These tests are pivotal for investigating the stability of mean and variance over time, offering comprehensive insights into the stationarity characteristics of the variables under scrutiny. The overarching objective is to establish a thorough understanding of their behavior before delving into the analysis of long-term relationships among MENA countries. To achieve this goal, panel unit-root tests are employed, consolidating the ADF tests conducted at the country level into a single panel. As emphasized by Baltagi (2008), the efficacy of panel unit-root tests has led to their widespread acceptance, utilization, and popularity among econometricians and economists. This approach enables a holistic assessment of the stationarity properties of the variables across the MENA region, laying a solid foundation for subsequent analyses of longterm relationships among the countries. The conducted tests in Table 6 indicate that the variables in our equation exhibit stationarity at order 1 (I(1)), with the null hypothesis (unit root support) accepted for I(0) level values of the variables. However, the null hypothesis is rejected for the first differences. Therefore, we can infer that the variables are integrated at the first order (I(1)). Following this confirmation, the next step involves employing the Kao test to ascertain the presence of a cointegrating relationship. The Kao cointegration test in Table 7 has confirmed the presence of a cointegrating relationship among the variables under study, as evidenced by the Augmented Dickey-Fuller (ADF) values. This finding enables us to quantify the long-term impact of the explanatory variables on CO2 emissions in MENA countries. The regression analysis in Table 8 reveals significant insights into the impact of socio-economic factors on mortality rates associated with chronic and fatal diseases. Higher education emerges as a critical determinant, with a coefficient of −0.10, indicating that higher levels of education are associated with lower mortality rates. This suggests that individuals with greater educational attainment may have better health outcomes due to improved access to healthcare resources and enhanced health literacy. Economic growth also plays a significant role, as indicated by its coefficient of 0.21. The positive coefficient suggests that as economies grow, mortality rates tend to increase. This may be attributed to changes in lifestyle behaviors associated with economic development, such as sedentary lifestyles and unhealthy dietary habits, which contribute to chronic diseases and higher mortality rates. Globalization exhibits a negative coefficient of −0.40, suggesting that increased globalization is linked to lower mortality rates. This implies that globalization facilitates the dissemination of medical knowledge and healthcare innovations, leading to improved health outcomes on a global scale. On the other hand, CO2 emissions show a positive coefficient of 0.44, indicating a correlation between higher CO2 emissions and increased mortality rates. This association likely arises from the role of CO2 emissions in exacerbating air pollution, which is a significant Table 3 Normality test. Mean Median Maximum Minimum Skewness Kurtosis Jarque-Bera Probability −0.09241 0.013444 −1.84542 1.301347 −0.112322 2.84432 2.24144 0.40 Table 4 The Centered Variance Inflation Factor (VIF) test. Variable Centered VIF EDU 4.12421 GDP 2.31938 GLOB 4.85412 CO2 3.01314 C NA Table 5 Q-statistic results. Lag specification AC PAC Q-Statistic P value 1 0.194 0.194 2.1570 0.142 2 0.194 0.163 4.3518 0.114 3 0.045 −0.019 4.4726 0.215 4 0.056 0.022 4.6646 0.323 M. Zouine et al. Research in Globalization 9 (2024) 100236 8 contributor to respiratory and cardiovascular diseases. The analysis of Table 9 provides valuable insights into the long-term dynamics between higher education, GDP, globalization, and mortality due to chronic and fatal diseases, and their respective impacts on health outcomes. Utilizing both FMOLS and DOLS techniques, the coefficients reveal nuanced relationships between these socio-economic factors and health outcomes. Beginning with higher education, both FMOLS and DOLS estimations showcase negative coefficients, indicating an inverse relationship between educational attainment and mortality due to chronic and fatal diseases. Specifically, FMOLS yields a coefficient of −0.26, while DOLS reports a slightly lower coefficient of −0.20. This suggests that higher levels of education may contribute to a reduction in mortality over the long term, possibly through increased health literacy and access to better healthcare. Similarly, the coefficients for GDP demonstrate a negative association with mortality due to chronic and fatal diseases in both FMOLS and DOLS estimations. FMOLS reports a coefficient of −0.70, while DOLS yields a comparable coefficient of −0.69. This implies that as GDP increases, there is a corresponding decrease in mortality, reflecting potential improvements in healthcare infrastructure and standards of living. Furthermore, the coefficients for globalization exhibit negative values across both estimation techniques, albeit with slight variations. FMOLS indicates a coefficient of −0.37, while DOLS reports a coefficient of −0.34. This underscores the potential role of globalization in fostering better health outcomes through the dissemination of medical knowledge, technologies, and resources across borders. Lastly, both techniques indicate a positive relationship between carbon emissions (CO2) and mortality due to chronic and fatal diseases. However, the DOLS coefficient is notably higher, suggesting a stronger association over the long term compared to FMOLS. This underscores the urgency of addressing carbon emissions to mitigate their impact on public health. In the MENA region, chronic diseases such as CVDs, cancer, respiratory diseases, and diabetes impose a significant health burden, contributing to a substantial portion of morbidity and mortality. According to the World Health Organization (WHO), non-communicable diseases (NCDs) account for approximately 70 % of deaths in the region, driven largely by lifestyle factors including urbanization, sedentary lifestyles, unhealthy diets, tobacco use, and obesity (WHO, 2020). Cardiovascular diseases, particularly ischemic heart disease and stroke, are the leading causes of mortality in the MENA region. In 2019 alone, ischemic heart disease was responsible for more than 810,000 deaths, with stroke causing over 370,000 deaths (WHO, 2020). Cancer rates are also rising, with breast, lung, colorectal, and prostate cancers being prevalent forms. Respiratory diseases such as chronic obstructive pulmonary disease (COPD) and asthma present significant health challenges, with COPD alone accounting for approximately 3.2 million disability-adjusted life years (DALYs) lost in 2019 (WHO, 2020). The prevalence of obesity and hypertension in countries like Bahrain, Egypt, Jordan, Kuwait, Saudi Arabia, and the UAE has reached alarming levels. In 2015, obesity affected 30.9 % of adult men and 45.9 % of adult women in the region, while nearly a quarter of the population suffers from hypertension (WHO, 2020). Tobacco use remains a prevalent risk factor for chronic diseases, with approximately 24.3 % of adults in the region reported as smokers, contributing significantly to CVDs, respiratory diseases, and cancer (WHO, 2020). Despite these alarming statistics, public health policy responses to the growing burden of chronic diseases in the MENA region have been slow and inadequate. There is an urgent need for evidence-based interventions that strengthen research capacities and implement comprehensive strategies for the prevention and management of NCDs. Fig. 1. Scatterplot of Residual, Actual, and Fitted Values of the Dependent Variable. Table 6 Unit Root test. Test and variables CO2 EDU GDP GLOB MORT LLC I(1)* I(1)* I(1)* I(1)* I(1)* ADF I(1)* I(1)* I(1)* I(1)* I(1)* PP I(1)* I(1)* I(1)* I(1)* I(1)* Source: author’s estimation on EVIEWS 12 *, **, ***indicate significance respectively at the 1 %, 5 % and 10 % level. Table 7 Kao’s cointegration test. Kao test T-statistic ADF −3.14424* Residual value 0.84240 HAC variance 0.92112 Source: author’s estimation on EVIEWS 12 *, **, ***indicate significance respectively at the 1 %, 5 % and 10 % level. M. Zouine et al.