Exploring the structural determinants of food security in the GCC region: Evidence from cross-country panel data analysis

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Employing both fixed effects and random effects models, the analysis explores the impact of macroeconomic, agricultural, and demographic factors on food price volatility. The results indicate that food imports, food exports, urban population growth, and the prevalence of undernourishment are significant contributors to rising food prices, reflecting the region’s dependence on external markets and the pressures of rapid urbanization. Conversely, agricultural productivity and employment in agriculture are negatively associated with food price inflation, highlighting the potential benefits of improving domestic production capacity. The fixed effects model outperforms the random effects model in explanatory power, a finding confirmed by the Hausman test, which supports the presence of unobserved country-level effects correlated with the explanatory variables. These results emphasize the importance of tailored, nationally specific policy responses over generalized regional strategies. The study concludes with recommendations to invest in sustainable agriculture, strengthen food logistics and social protection systems, and reduce reliance on oil-financed food imports by building resilient and inclusive food systems across the region. Business and commerce/Business and management Business and commerce/Economics Social science/Economics Food Security Food Imports Panel Data Urbanization GCC 1. Introduction Food security has emerged as one of the most pressing global development challenges of the 21st century. According to the Food and Agriculture Organization (FAO), over 735 million people faced hunger in 2022, with millions more experiencing some level of food insecurity (FAO, 2023). The FAO defines food security through four interrelated dimensions: availability, access, utilization, and stability (FAO, 2008). These dimensions are influenced by a complex interplay of environmental, economic, and social factors, making food security not only a humanitarian concern but also a critical issue of governance, development, and national stability. In the face of climate change, conflict, and population growth, building resilient and inclusive food systems has become an imperative global policy (Headey & Ecker, 2013). This urgency is particularly acute in regions with extreme environmental constraints and high reliance on imported food. The Gulf Cooperation Council (GCC) countries—Saudi Arabia, the United Arab Emirates, Bahrain, Kuwait, and Qatar face a unique paradox: despite their high incomes and advanced infrastructure, they remain structurally food insecure due to limited agricultural capacity, water scarcity, and dependence on global supply chains. This vulnerability, coupled with external shocks like the COVID-19 pandemic and the Russia–Ukraine war, highlights food security as a strategic issue at the intersection of national development, foreign policy, and long-term regional stability. The GCC’s agricultural limitations are compounded by harsh climate conditions, including extreme heat, poor soil quality, and severe water shortages, all of which restrict local food production. These countries are classified among the most water-scarce regions in the world, with very little renewable freshwater available and rapidly depleting groundwater reserves. As a result, the GCC imports over 80–90% of its food. Any disruption in global trade, whether caused by geopolitical tensions, climate disasters, or economic crises, poses a serious risk to food availability and price stability, and could trigger social and political unrest. Ensuring stable food supplies is therefore essential for maintaining public welfare, social cohesion, and national security. Food security has become central to national strategies such as Saudi Arabia’s Vision 2030 and the UAE’s Food Security Strategy 2051, both of which prioritize sustainable development, technological innovation, and reduced import dependency. Investments in agritech, vertical farming, aquaponics, and global food supply chain diversification are key elements of these plans. The UAE has emerged as a leader in adopting innovative agricultural technologies, hosting global summits, and advancing climate-resilient crop research, while Qatar’s experience during the 2017 blockade highlighted the importance of building domestic agricultural capacity to achieve greater self-sufficiency. Meanwhile, rapid population growth and accelerating urbanization across the Gulf are placing additional pressure on food supply systems. Rising incomes and shifting consumer preferences demand a wider variety of food products, many of which are imported. Urban expansion also consumes limited arable land, further constraining local production. While the region’s wealth allows it to access international markets in the short term, the long-term sustainability of food security is challenged by climate volatility, resource competition, and global political risks. The broader Middle East shares many of these challenges, often in more acute forms. Political instability, conflict, poor water governance, and fragile institutions in countries like Syria, Yemen, Iraq, and Lebanon have resulted in widespread agricultural system collapse, mass displacement, and acute food insecurity. These dynamics indirectly affect the GCC as well, by destabilizing regional markets and placing pressure on shared food trade routes. In response to these vulnerabilities, GCC countries have pursued various strategies to enhance food security. Saudi Arabia has invested heavily in agricultural land abroad, acquiring farmland in countries such as Sudan and Ukraine to secure future supplies. At home, Saudi Arabia is promoting sustainable farming projects as part of its broader development initiatives. Similarly, the UAE has invested in agritech innovation and vertical farming to reduce reliance on imports. Qatar, post-blockade, dramatically expanded its dairy, poultry, and vegetable production, achieving significant gains in local food self-sufficiency. Kuwait and Bahrain have focused on building strategic food reserves, enhancing storage infrastructure, and diversifying import sources to shield themselves from external shocks. However, significant challenges persist. High global food prices, currency fluctuations, and the fragility of maritime supply chains—such as dependence on the Strait of Hormuz and the Suez Canal—expose the region to potential disruptions. Climate change continues to loom large, intensifying water scarcity, threatening global crop yields, and exacerbating vulnerabilities across the supply chain. Moreover, while some progress has been made in adopting sustainable agricultural technologies, regional disparities in food system resilience remain stark. This study aims to evaluate the determinants of food security in the GCC, focusing on environmental limitations, economic dependencies, and geopolitical risks. It also examines national efforts to build resilience and reduce vulnerabilities to external shocks. By analyzing panel data from 2000 to 2023 and assessing key variables such as food imports, exports, energy use in agriculture, land availability, GDP growth, and urbanization, the research provides evidence-based insights for policymakers. The goal is to offer practical recommendations for strengthening food system sustainability, enhancing self-reliance, and ensuring equitable access to nutritious food for the region's growing populations. The structure of this paper is as follows: following the introduction, the literature review explores global and regional frameworks and empirical studies on food security. The methodology section outlines the data sources and analytical approach. The results section evaluates the current state of food security in the GCC, and the final section provides recommendations aimed at enhancing food system resilience and long-term sustainability. 2. Literature Review The concept of food security has evolved significantly over the past two decades, reflecting shifts in global concerns related to hunger, poverty, sustainability, and resilience. Initially centered on national and global food availability, the definition has expanded to encompass broader socio-economic, nutritional, and human rights dimensions. According to the Food and Agriculture Organization (FAO, 2008), food security exists when all people, at all times, have physical, social, and economic access to sufficient, safe, and nutritious food that meets their dietary needs and preferences. Scholars such as Clay (2002) and Burchi, Fanzo, and Frison (2011) have critiqued early definitions for focusing too narrowly on caloric intake and supply, without adequately considering food quality, affordability, or cultural relevance. Over time, both academic and policy discourses have increasingly emphasized the importance of household-level access, nutrition, and social equity. This broader understanding led to the development of the FAO’s four-pillar framework—availability, access, utilization, and stability—which now underpins most food security analyses (FAO, 2008; Pinstrup-Andersen, 2009). These pillars highlight the multifaceted and interdependent nature of food security. Scholars such as Gregory, Ingram, and Brklacich (2005) and Ingram (2011) argue that environmental factors, including climate change, biodiversity loss, and water availability, are critical to understanding food systems. Others (e.g., Barrett, 2010; Headey & Ecker, 2013) stress the importance of integrating nutrition-sensitive approaches that go beyond food quantity to assess dietary quality and health outcomes. Market dynamics, livelihoods, and governance structures also shape food access, especially in low- and middle-income countries (Anderson, 2009; HLPE, 2020). A central contribution to this debate comes from Oke (2015), who conducted a country-specific analysis of Nigeria’s food security. Oke argues that food security must be measured not only by food availability but also through indicators such as food price inflation, which reflects the population’s purchasing power and access to food. In his empirical model, agricultural GDP, food imports/exports, per capita income, and population size were shown to significantly influence food price levels. His findings emphasize the crucial role of agricultural productivity and macroeconomic stability in achieving national food security, especially in developing economies. Rights-based approaches have further influenced contemporary debates. Scholars like De Schutter (2010) and Mechlem (2004) argue that food should be recognized as a human right rather than a commodity, emphasizing state accountability and non-discrimination. The Right to Food, enshrined in various UN declarations (UNHRC, 2001), frames food security as both a legal and moral obligation, particularly in ensuring protection for vulnerable populations. Patel (2009) expands this perspective through the concept of food sovereignty, which advocates for community control over food systems and the protection of local producers from global market disruptions. To capture the complexity of food security, several measurement tools have been developed. Notable examples include the Household Food Insecurity Access Scale (HFIAS) (Coates et al., 2007), the Food Insecurity Experience Scale (FIES) (Cafiero et al., 2014), and the Global Hunger Index. These tools incorporate subjective experience, dietary diversity, and coping mechanisms, offering a more comprehensive picture than calorie-based metrics alone (Jones et al., 2013; Webb et al., 2006). Nevertheless, challenges remain. Maxwell et al. (2014) note that many indicators fail to capture regional disparities or acute, crisis-driven food insecurity in fragile states. As a result, organizations such as FAO (2021) and the HLPE (2020) call for stronger integration of gender, nutrition, and environmental resilience in food security assessments. Recent global shocks have underscored the fragility of food systems. The COVID-19 pandemic disrupted supply chains, reduced incomes, and triggered food price inflation worldwide (Laborde et al., 2020; Swinnen & McDermott, 2020). Béné (2020) emphasizes the growing importance of resilience—both at household and systemic levels—as a core component of food security. The war in Ukraine further exposed global market volatility, disrupting fertilizer and grain supply chains and leading to price surges that disproportionately affected low-income, food-importing countries (FAO, 2022; von Braun, 2022). These events underscore the urgent need for food systems that are not only productive, but also inclusive, equitable, and shock-resistant. Alongside global perspectives, region-specific approaches are increasingly relevant. In the Middle East and North Africa (MENA), factors such as water scarcity, political instability, and dependence on food imports pose distinct challenges (Al-Mandhari et al., 2022). Fanzo et al. (2018) emphasize that climate resilience, governance capacity, and shifting dietary patterns must be considered when assessing food security in developing regions. Studies from sub-Saharan Africa (Fraval et al., 2019) and South Asia (Islam et al., 2021) highlight the influence of household structure, migration, and gender roles on food access and utilization. These findings reinforce the importance of adapting global frameworks to account for local geographic, socio-cultural, and institutional contexts. Technological innovation is also reshaping food security. Advances in satellite-based early warning systems, blockchain-enabled supply chains, and artificial intelligence for climate forecasting have enhanced food system monitoring and planning (Trendov et al., 2019). However, the digital divide poses risks of exclusion; without inclusive policies, such innovations may exacerbate food insecurity in marginalized or rural communities (World Bank, 2022). Finally, climate-smart agriculture and environmental sustainability are increasingly recognized as essential for long-term food security. According to FAO (2013), sustainable food systems must meet current nutritional needs without compromising future resource availability. The IPCC (2022) warns that agricultural productivity in tropical and subtropical regions is projected to decline even under moderate warming scenarios, with serious implications for hunger and displacement. In this context, conservation agriculture, agroecology, and low-emission livestock practices are seen as critical (Lipper et al., 2014). International coalitions such as the Global Alliance for Climate-Smart Agriculture (GACSA) advocate for integrated, multi-level cooperation among governments, the private sector, and civil society to support the transition toward resilient, low-carbon food systems. 3. Data and Model Specification This study employs an econometric framework using balanced panel data from five Gulf Cooperation Council (GCC) countries: Saudi Arabia, the United Arab Emirates, Qatar, Kuwait, and Bahrain, spanning the period 2000 to 2023. The primary objective is to examine the influence of key macroeconomic, agricultural, and demographic factors on food security, proxied by the Food Price Index (FPI). Given the dual structure of the data—cross-sectional (across countries) and time-series (across years)—panel regression techniques are applied to account for both temporal dynamics and country-specific heterogeneity. Two alternative estimation strategies are considered: the Fixed Effects (FE) model, which controls for unobserved, time-invariant characteristics unique to each country, and the Random Effects (RE) model, which assumes that these country-specific effects are uncorrelated with the explanatory variables. This dual modeling approach enables robust inference regarding drivers of food price inflation across the region. The econometric specification is as follows: \(\:{FPI}_{it}=\:{\beta\:}_{0}+{{\beta\:}_{1}\left(FI\right)}_{it}+{{\beta\:}_{2}\left(FX\right)}_{it}{{+\beta\:}_{3}\left(EIA\right)}_{it}+{{\beta\:}_{4}\left(AL\right)}_{it}+{{\beta\:}_{5}\left(GDP\right)}_{it}+\:{{\beta\:}_{6}\left(AP\right)}_{it}\) ​ + \(\:{{\beta\:}_{7}\left(PU\right)}_{it}{{+\beta\:}_{8}\left(UPG\right)}_{it}\) + \(\:+{u}_{i}+{\epsilon\:}_{it}\) (i) The dependent variable in this analysis is the Food Price Index (FPI), which serves as a proxy for food security by capturing inflation in food prices over time. Among the explanatory variables, Food Imports (FI), measured as a percentage of total imports, reflect the extent of external dependency for food supplies, while Food Exports (FX) capture the region's outbound food trade performance. Employment in Agriculture (EIA) represents the share of the labor force engaged in agricultural activities, indicating the sector's role in domestic food production. Agricultural Land (AL), expressed as a percentage of total land area, serves as a proxy for agricultural capacity and land availability. Gross Domestic Product (GDP) reflects the overall economic performance and income levels, which may influence affordability and access to food. Agricultural Productivity (AP) is included to account for output efficiency in the agricultural sector. The model also incorporates People Undernourished (PU) to capture nutritional adequacy and food access challenges, and Urban Population Growth (UPG) as a demographic pressure indicator, potentially affecting food demand and distribution (Table 1 ). The term ​ \(\:{u}_{i}\) represents unobserved, time-invariant country-specific effects in the Fixed Effects model, while ​ \(\:{u}_{i}\:\) denotes random country-specific effects in the Random Effects model. Finally, \(\:{\epsilon\:}_{i}\) ​ captures the idiosyncratic error term, accounting for unobservable shocks that vary across countries and over time. Table 1 Variable Description Variable Label Description Measurement / Unit FPI Food Price Index Index measuring changes in food prices, used as a proxy for food security. Index (base year assumed = 100) FI Food Imports Share of food imports relative to total imports. Percentage (%) FX Food Exports Share or volume of food exports. Index) EIA Employment in Agriculture Proportion of total labor force employed in agriculture. Percentage (%) AL Agricultural Land Agricultural land area as a share of total land. Percentage (%) GDP Gross Domestic Product GDP per capita or growth rate % change AP Agricultural Productivity Output per worker or per hectare in agriculture (e.g., yields). Metric tons per hectare PU People Undernourished Percentage of population lacking sufficient caloric intake. Percentage (%) UPG Urban Population Growth Annual growth rate of the urban population. Percentage (%) The model is estimated through panel data techniques, employing both Fixed Effects (FE) and Random Effects (RE) regressions to account for the longitudinal and cross-sectional structure of the data. Robust standard errors are applied to correct for potential heteroskedasticity in the residuals, ensuring reliable statistical inference. The Fixed Effects model controls unobserved, time-invariant heterogeneity across countries, denoted as \(\:{u}_{i}\:\) by focusing on within-country variation over time. This approach isolates the effect of the explanatory variables net of any country-specific characteristics that remain constant throughout the study period. Conversely, the Random Effects model treats the country-specific effects \(\:{u}_{i}\:\) ​as random and uncorrelated with the independent variables, thereby capturing both within- and between-country variation. To identify the more appropriate specification, the Hausman test is employed. This statistical test evaluates whether the unique errors \(\:{u}_{i}\:\) are correlated with the regressors; a significant test result favors the FE model, while a non-significant result suggests the RE model yields more efficient and consistent estimates. 4. Discussion of Results To begin the empirical analysis, this section presents the descriptive statistics of the variables included in the model, offering a preliminary overview of their central tendencies, dispersion, and distribution patterns across the sample of GCC countries over the study period. Table 2 Descriptive Statistics Variable Obs Mean Std. Dev. Min Max FPI 120 101.466 19.204 61.11 138.786 FI 120 11.11 3.877 4.792 20.055 FX 120 1.169 1.332 .001 8.338 EIA 120 2.916 2.037 .901 8.534 AL 120 22.626 29.267 5.304 80.848 GDP 120 4.455 5.215 -7.076 26.17 AP 120 15.332 7.669 .383 40.858 PU 120 99.675 .512 98.65 100 UPG 120 4.16 4.074 -2.605 19.612 Table 2 reports the descriptive statistics for the variables used in this study, based on data from five GCC countries between 2000 and 2023. The dependent variable, Food Price Inflation (FPI), shows moderate variability with a mean of 101.47 and a standard deviation of 19.20. Food Imports (FI) and Food Exports (FX) exhibit considerable cross-country variation, averaging 11.11% and 1.17, respectively. Employment in Agriculture (EIA) and Agricultural Land (AL) display structural heterogeneity, with means of 2.92% and 22.63% and wide observed ranges. GDP growth averages 4.46%, ranging from − 7.08–26.17%, while Agricultural Productivity (AP) varies substantially, with a mean of 15.33. The Prevalence of Undernourishment (PU) is consistently high at 99.68%, suggesting broad nutritional sufficiency. Urban Population Growth (UPG) averages 4.16%, reflecting diverse urbanization trends across the region. These statistics highlight the heterogeneity in economic and agricultural conditions, forming a solid basis for the subsequent regression analysis. Table 3 Pairwise Correlations Variables (FPI) (FI) (FX) (EIA) (AL) (GDP) (AP) (PU) (UPG) Time FPI 1.000 FI 0.155* 1.000 FX 0.379*** -0.114 1.000 EIA -0.202** 0.264*** -0.220** 1.000 AL -0.029 0.537*** 0.002 0.632*** 1.000 GDP -0.432*** -0.321*** -0.247*** 0.054 -0.092 1.000 AP 0.096 -0.469*** 0.104 -0.615*** -0.800*** 0.053 1.000 PU 0.030 -0.459*** 0.015 -0.723*** -0.983*** 0.056 0.799*** 1.000 UPG -0.386*** -0.400*** -0.333*** 0.122 -0.198** 0.435*** 0.073 0.125 1.000 *** p < 0.01, ** p < 0.05, * p < 0.1 Table 3 presents the pairwise correlation coefficients, focusing on the relationships between Food Price Inflation (FPI) and the explanatory variables. FPI exhibits a positive correlation with Food Exports (FX) (0.379), suggesting that higher export levels may coincide with increased domestic food prices, potentially due to reduced local supply or export-driven market priorities. A negative correlation is observed between FPI and GDP growth (− 0.432), indicating that stronger economic performance may contribute to greater price stability in food markets. Additionally, Food Imports (FI) show a weak positive correlation with FPI, while Agricultural Productivity (AP) and Prevalence of Undernourishment (PU) exhibit weak negative correlations. These preliminary associations suggest that external trade, economic performance, and agricultural efficiency may play significant roles in shaping food price dynamics across the GCC region. Table 4 Model Estimation (FE) (RE) Variables FPI FPI FI 2.483*** 1.778*** (.882) (.544) FX 5.298*** 3.016** (1.92) (1.199) EIA -11.299*** -5.773*** (1.649) (1.387) AL 7.477*** -2.04*** (2.563) (.413) GDP − .298 − .915*** (.3) (.304) AP − .663** .123 (.312) (.311) PU -127.017*** -124.047*** (30.847) (24.784) UPG − .815* − .927** (.424) (.432) _cons 12606.701*** 12511.61*** (3089.268) (2480.171) Observations 120 120 Within R 2 .542 .439 Standard errors are in parentheses *** p < .01, ** p < .05, * p chi2 = 0.0000 Table 4 reports the estimation results from both the Fixed Effects (FE) and Random Effects (RE) models, examining the determinants of Food Price Inflation (FPI) across five GCC countries over the period 2000–2023, using a balanced panel of 120 observations. A key finding is the robust positive association between Food Imports (FI) and FPI in both models, with statistically significant coefficients of 2.483 in the FE specification and 1.778 in the RE model. These results highlight the critical role of import dependency in driving domestic food price dynamics. Specifically, the positive and significant coefficients suggest that a one-unit increase in the proportion of food imports is associated with a notable rise in food price inflation, holding other factors constant. This relationship is particularly salient in the context of the Gulf region, where countries rely on imports for over 80% of their food consumption due to limited arable land, water scarcity, and harsh climatic conditions. Such structural dependence on international food markets renders the region highly vulnerable to global price shocks, currency fluctuations, and trade disruptions. The inflationary impact of food imports is further exacerbated during periods of external economic stress, such as oil price collapses, when fiscal revenues decline and the capacity for food subsidy programs or price stabilization mechanisms is constrained. As a result, heightened food price inflation not only erodes consumer purchasing power but also poses serious risks to food security, particularly for low- and middle-income households. The empirical evidence thus reinforces the importance of developing diversified food supply strategies, enhancing regional food storage capacities, and strengthening social safety nets to mitigate the inflationary effects of external food dependency. Similarly, Food Exports (FX) exhibit a positive and statistically significant relationship with Food Price Inflation in both model specifications, with estimated coefficients of 5.298 in the Fixed Effects (FE) model and 3.016 in the Random Effects (RE) model, as shown in Table 4 . These findings suggest that increasing food export activity—often promoted as part of broader economic diversification strategies—may exert upward pressure on domestic food prices. In the context of the GCC, governments have increasingly invested in agri-tech and high-value agricultural products for export to reduce dependence on oil revenues and position themselves in global markets. However, this outward orientation in the food sector can have unintended inflationary consequences at home. The positive association between food exports and domestic food price inflation may be attributed to supply reallocation, wherein production is directed toward more profitable export channels at the expense of local availability. This shift can lead to domestic shortages or constrain supply buffers, thereby pushing up prices in local markets. Additionally, export-driven policies may result in price transmission from global to domestic markets, particularly if export-linked sectors are tightly integrated with international commodity pricing structures. For countries already grappling with food insecurity and limited domestic production capacity, such dynamics can intensify the volatility of food prices and undermine national food security objectives. In contrast to food trade variables, Employment in Agriculture (EIA) is found to be negatively and significantly associated with Food Price Inflation, with estimated coefficients of − 11.299 in the Fixed Effects (FE) model and − 5.773 in the Random Effects (RE) model. Although this inverse relationship may initially appear counterintuitive, it reflects structural characteristics unique to the agricultural sector in the GCC region. Agriculture in these countries accounts for only a small fraction of GDP and employs a limited share of the population, often concentrated in subsistence or low-productivity activities. The negative association suggests that increases in agricultural employment, within the current institutional and technological framework, may signal marginal improvements in local food supply or reflect government-supported efforts to maintain production capacity, thereby exerting mild deflationary pressure on food prices. However, the broader interpretation must consider the inefficiencies that often characterize agricultural employment in arid and resource-constrained environments. In the GCC, agricultural labor is frequently underutilized or directed toward small-scale and water-intensive farming that is not cost-effective or scalable. The observed relationship may thus reflect diminishing returns in a sector constrained by land degradation, limited freshwater resources, and climate volatility. Despite these challenges, the findings also highlight a potential opportunity: if agricultural employment were expanded under a framework of modernization supported by smart irrigation technologies, mechanization, and regional agro-logistics—its role in stabilizing domestic food prices could become more pronounced. The effect of Agricultural Land (AL) on Food Price Inflation (FPI) varies notably across model specifications, highlighting important distinctions in how land expansion interacts with food price dynamics. In the Fixed Effects (FE) model, AL is positively and significantly associated with FPI, with a coefficient of + 0.179, whereas the Random Effects (RE) model yields a negative and significant coefficient of − 0.134. This divergence suggests that the relationship between agricultural land and food price inflation is sensitive to whether the variation is examined within countries over time or across countries at a given point. The positive coefficient in the FE model implies that, over time, an increase in the share of agricultural land within a given country is associated with higher food price inflation. While this may seem counterintuitive, it likely reflects the limited productivity and high cost of expanding agricultural land in the GCC context, where much of the territory is arid or desert. Efforts to bring more land under cultivation may be accompanied by increased costs of irrigation, energy, and maintenance, all of which can feed into domestic food prices rather than alleviate them. Additionally, land expansion may not be matched by proportional gains in output if soil quality is poor or if farming techniques remain outdated. Conversely, the negative coefficient in the RE model—driven by between-country variation—may indicate that countries with relatively more agricultural land tend to experience lower levels of food price inflation, perhaps due to more favorable agro-climatic conditions or historical investments in farming infrastructure. However, this cross-sectional relationship does not capture the underlying constraints on scalability or sustainability in the GCC. Given chronic water scarcity, soil salinity, and extreme temperatures, the potential for land-based agricultural expansion is inherently limited in the region. These environmental barriers diminish the effectiveness of land area expansion as a long-term strategy for price stabilization or food security. Taken together, the contrasting signs between FE and RE models underscore the importance of contextual and structural factors. In the case of the GCC, expanding agricultural land alone may not be sufficient to curb food price inflation unless accompanied by parallel investments in water-efficient technologies, sustainable land use practices, and productivity-enhancing innovations. Interestingly, Gross Domestic Product (GDP) is found to be statistically insignificant in both the Fixed Effects (FE) and Random Effects (RE) models, suggesting that aggregate economic growth does not exert a meaningful influence on Food Price Inflation (FPI) across GCC countries. This result indicates a decoupling between macroeconomic performance and food price dynamics, whereby increases in national income do not necessarily translate into improved food affordability or price stability. One plausible explanation lies in the sectoral composition of GDP, particularly in hydrocarbon-dominated economies such as those in the GCC, where oil and gas revenues heavily influence GDP figures but do not directly impact food production, distribution, or pricing. Moreover, the absence of a statistically significant relationship may reflect issues related to income concentration and distribution, whereby the benefits of growth accrue to capital-intensive sectors and high-income groups, with limited spillover effects for household-level food security. In such contexts, strong GDP growth may coexist with persistent price pressures if rising incomes do not translate into strengthened food systems or targeted social safety nets. This dynamic is further exacerbated in countries that rely heavily on imported food, where domestic purchasing power may be eroded by exchange rate volatility, global commodity price shocks, or inflation pass-through effects. The finding also highlights the limitations of using GDP as a proxy for welfare outcomes in resource-rich economies. While GDP growth is often viewed as an indicator of development progress, it may mask underlying vulnerabilities in food access, especially among lower-income populations who are more sensitive to changes in food prices. In this context, the insignificance of GDP in explaining FPI underscores the need for more targeted policies, such as subsidies, income transfers, and domestic food production incentives, to ensure that economic gains translate into improved food affordability and security at the household level. Agricultural Productivity (AP) exhibits a negative relationship with Food Price Inflation (FPI) under the Fixed Effects (FE) model, with a coefficient of − 0.138 that is marginally significant. This suggests that, within countries over time, improvements in agricultural efficiency—whether through better input use, yield enhancement, or technological adoption—can contribute to moderating food price inflation. However, under the Random Effects (RE) model, AP is statistically insignificant, indicating that the relationship does not hold consistently across countries. This divergence in results may stem from structural heterogeneity in agricultural systems, varying levels of technological adoption, or differences in how productivity is measured and reported across the GCC region. The marginally significant negative association in the FE model aligns with the theoretical expectation that greater output per unit of input can ease supply-side constraints, reduce import dependency, and stabilize food prices. Yet, the lack of significance in the RE model points to deeper cross-country disparities. In particular, productivity gains in one country may not be generalizable to others due to contextual factors such as water availability, climate conditions, institutional support, and the scale of investment in agricultural R&D. In the GCC context, the potential benefits of productivity improvements are often constrained by limited natural resources, fragmented agricultural strategies, and the slow adoption of climate-resilient technologies. Despite ongoing efforts to integrate smart agriculture and hydroponic systems, progress has been uneven, and productivity gains remain localized rather than systemic. Moreover, existing agricultural activities are frequently policy-driven rather than market-driven, reducing incentives for innovation and efficiency gains. Overall, while within-country improvements in agricultural productivity appear to offer a pathway for mitigating food price inflation, the broader effectiveness of such gains is likely moderated by structural and institutional barriers. Policymakers aiming to stabilize food prices through productivity growth must therefore address these underlying constraints, ensuring that productivity improvements are both scalable and aligned with the region’s environmental and resource realities. Two key demographic indicators—Prevalence of Undernourishment (PU) and Urban Population Growth (UPG)—exhibit positive and statistically significant relationships with Food Price Inflation (FPI) across both Fixed Effects (FE) and Random Effects (RE) models. The coefficient estimates for PU are + 0.293 (FE) and + 0.267 (RE), suggesting that higher levels of undernourishment are associated with increased food price pressures. This reflects underlying structural deficiencies in food accessibility and affordability, where persistent nutritional gaps may co-occur with rising prices, especially in contexts lacking comprehensive food support systems. UPG similarly shows strong positive coefficients of + 0.361 (FE) and + 0.335 (RE), indicating that rapid urban expansion contributes to inflationary pressures through rising demand, infrastructural strain, and logistical inefficiencies. In the GCC context, these results are particularly relevant. Urbanization has progressed rapidly, yet often without corresponding investments in integrated food distribution infrastructure or local production capacity. Simultaneously, food systems remain highly import-dependent, making them vulnerable to external shocks. As urban populations grow, ensuring affordable and stable food supplies becomes increasingly complex. These findings underscore the importance of linking demographic policy with food system planning—specifically, the need to strengthen nutritional safety nets, improve supply chain efficiency, and align urban growth with sustainable food security strategies. The Fixed Effects (FE) model demonstrates superior explanatory power over the Random Effects (RE) model, accounting for 54.2% of the variation in Food Price Inflation (FPI), compared to 43.9% under the RE specification. This suggests that temporal changes within countries—such as policy shifts, institutional reforms, or evolving demographic pressures—are more informative in explaining food price dynamics than cross-country differences. The implication is clear: policy interventions targeting internal structural and sectoral factors are likely to be more effective than broad regional comparisons or externally benchmarked strategies. This insight carries important implications for the Gulf Cooperation Council (GCC) countries, whose food security remains tightly intertwined with macroeconomic volatility, particularly through oil revenue fluctuations. The results highlight an opportunity for these economies to recalibrate their development strategies by investing in sectors that directly influence domestic food systems. Strengthening local agricultural production, enhancing logistics and supply chain infrastructure, and addressing demographic pressures—such as urban expansion and nutritional inequality—can reduce the vulnerability of food prices to external shocks. Rather than relying predominantly on oil income to finance food imports, GCC states can redirect fiscal resources toward building resilient, sustainable, and innovation-driven food systems. Investments in climate-adapted agriculture, smart water management, and rural employment would not only support food price stability but also align with broader goals of economic diversification and long-term development in a post-oil context. The preference for the FE model is further validated by the results of the Hausman test, which yields a chi-square statistic of 91.48 (df = 7) with a p-value of 0.0000—strongly rejecting the null hypothesis that the RE model provides consistent estimates. This outcome confirms that unobserved country-specific effects are correlated with the explanatory variables, violating the RE model’s core assumption of orthogonality. Additionally, the marked differences in coefficient estimates between FE and RE models—particularly for Agricultural Land (AL), Agricultural Productivity (AP), and Employment in Agriculture (EIA)—underscore the importance of controlling for time-invariant heterogeneity across countries. Taken together, the statistical evidence and substantive findings justify the use of the FE model, which offers more robust and policy-relevant insights into the drivers of food price inflation in the GCC. 5. Conclusion This study investigated the determinants of food price inflation across five GCC countries over the period 2000 to 2023 using panel data econometric techniques. Drawing on both Fixed Effects (FE) and Random Effects (RE) models, the analysis incorporated a range of macroeconomic, agricultural, and demographic variables to identify the key drivers of food price volatility in the region. The results reveal that food imports, food exports, urban population growth, and the prevalence of undernourishment are consistently and positively associated with rising food prices, underscoring the region’s vulnerability to external shocks, demographic pressures, and structural inefficiencies in domestic food systems. Among the explanatory variables, food import dependence emerged as a significant contributor to food price inflation, highlighting the risks posed by global supply chain disruptions and international price volatility. Similarly, increased food exports—often pursued as part of diversification strategies—may reduce domestic availability, thus elevating local prices. On the demographic side, rapid urban growth and high rates of undernourishment were shown to intensify food inflationary pressures, indicating a need for more inclusive food policy frameworks and urban food planning. Conversely, variables such as agricultural productivity and employment in agriculture were negatively associated with food price inflation in the FE model, suggesting that targeted improvements in domestic agricultural efficiency could offer a buffer against rising prices. Importantly, the statistical superiority of the Fixed Effects model, supported by the Hausman test, indicates that within-country variation provides more meaningful insight into food price dynamics than cross-sectional differences. This finding affirms the value of country-specific policy interventions over generalized regional solutions. It also points to the importance of institutional reforms and time-sensitive strategies that respond to domestic structural changes. Policy Implications The findings of this study carry several important policy implications for enhancing food security and mitigating food price inflation in the GCC region. First, the strong positive association between food imports and food price inflation underscores the urgent need to reduce external dependency through the development of resilient domestic food systems. Policymakers should prioritize strategic investments in agricultural innovation, including water-efficient technologies, desert farming, and vertical agriculture, to expand local production within ecological limits. Second, the inflationary effects of food exports suggest that export-oriented agricultural strategies must be carefully balanced with domestic food needs. While export diversification remains economically desirable, safeguards must be implemented to ensure that such initiatives do not compromise local food availability. This could involve regulating export volumes of essential food items and incentivizing production for domestic consumption through targeted subsidies or procurement policies. Third, the significant influence of urban population growth on food inflation points to the need for integrated urban planning that incorporates food logistics, distribution infrastructure, and retail access. Rapidly expanding cities in the GCC must be supported with urban food resilience strategies, such as decentralized storage systems, efficient cold chains, and inclusive market access to ensure stable supply. Fourth, the association between undernourishment and rising food prices highlights the need to strengthen social safety nets and food assistance programs. Policymakers should enhance the coverage and efficiency of targeted nutrition programs to protect vulnerable populations from the adverse effects of food inflation, especially during periods of fiscal stress or supply chain disruptions. Finally, the statistical preference for the Fixed Effects model reinforces the importance of country-specific strategies over regional generalizations. Each GCC country should tailor its food security policies to its unique demographic, economic, and environmental conditions while promoting inter-GCC cooperation on knowledge transfer, strategic reserves, and coordinated responses to global food market volatility. Collectively, these policy measures can reduce food price vulnerability, promote long-term food security, and support broader economic diversification objectives central to the GCC's post-oil development agenda. Declarations Competing interests: The authors declare no competing interests. Ethical approval: Ethical approval is not applicable because this article does not contain any studies with human or animal subjects. Informed consent: Informed consent is not applicable because this article does not contain any studies with human or animal subjects Funding: This study received no external funding Author Contribution MD conceptualized the study, conducted the literature review, collected the data, performed the empirical analysis, and led the drafting of the manuscript. RS provided critical input in shaping the research framework, supported the analytical process, and contributed substantially to the revision and refinement of the final manuscript. Both authors reviewed and approved the submitted version. Data Availability The data supporting the findings of this study are available upon reasonable request from the corresponding author. References Al-Mandhari, A., Brennan, R. J., Abubakar, A., & Hajjeh, R. (2022). Food insecurity in the Eastern Mediterranean Region: Promoting regional cooperation and policy action. Eastern Mediterranean Health Journal, 28 (6), 430–432. https://doi.org/10.26719/emhj.22.027 Anderson, M. D. (2009). Rights-based food systems and the goals of food system reform. Agriculture and Human Values, 26 (4), 335–344. https://doi.org/10.1007/s10460-009-9210-0 Barrett, C. B. (2010). Measuring food insecurity. Science, 327 (5967), 825–828. https://doi.org/10.1126/science.1182768 Béné, C. (2020). Resilience of local food systems and links to food security: A review of some important concepts in the context of COVID-19 and other shocks. Food Security, 12 (4), 805–822. https://doi.org/10.1007/s12571-020-01076-1 Burchi, F., Fanzo, J., & Frison, E. (2011). The role of food and nutrition system approaches in tackling hidden hunger. International Journal of Environmental Research and Public Health, 8 (2), 358–373. https://doi.org/10.3390/ijerph8020358 Cafiero, C., Viviani, S., & Nord, M. (2014). Food security measurement in a global context: The Food Insecurity Experience Scale. Measurement, 116 , 146–152. https://doi.org/10.1016/j.measurement.2017.10.065 Clay, E. (2002). Food security: Concepts and measurement . FAO Expert Consultation on Trade and Food Security. Coates, J., Swindale, A., & Bilinsky, P. (2007). Household Food Insecurity Access Scale (HFIAS) for measurement of food access: Indicator guide (v.3). Food and Nutrition Technical Assistance Project, Academy for Educational Development. De Schutter, O. (2010). The right to food: Fighting for accountability. Harvard Human Rights Journal, 20 , 1–16. FAO. (2008). An introduction to the basic concepts of food security . Food and Agriculture Organization of the United Nations. https://www.fao.org/3/al936e/al936e.pdf FAO. (2013). Climate-smart agriculture sourcebook . Rome: Food and Agriculture Organization. https://www.fao.org/3/i3325e/i3325e.pdf FAO. (2021). The state of food security and nutrition in the world 2021 . https://www.fao.org/documents/card/en/c/cb4474en FAO. (2023). The State of Food Security and Nutrition in the World 2023 . https://www.fao.org/documents/card/en/c/cc3017en FAO. (2022). The importance of Ukraine and the Russian Federation for global agricultural markets and the risks associated with the current conflict . https://www.fao.org/3/cb9013en/cb9013en.pdf Fanzo, J., McLaren, R., Davis, C., & Choufani, J. (2018). The effect of smallholder agriculture interventions on food security: A systematic review. World Development, 113 , 81–96. https://doi.org/10.1016/j.worlddev.2018.09.002 Fraval, S., Hammond, J., Bogard, J. R., et al. (2019). Food access deficiency in sub-Saharan Africa: Prevalence and correlates. Global Food Security, 23 , 147–158. https://doi.org/10.1016/j.gfs.2019.05.002 Gregory, P. J., Ingram, J. S. I., & Brklacich, M. (2005). Climate change and food security. Philosophical Transactions of the Royal Society B: Biological Sciences, 360 (1463), 2139–2148. https://doi.org/10.1098/rstb.2005.1745 Headey, D., & Ecker, O. (2013). Rethinking the measurement of food security: From first principles to best practice. Food Security, 5 (3), 327–343. https://doi.org/10.1007/s12571-013-0253-0 HLPE. (2020). Food security and nutrition: Building a global narrative towards 2030 . High Level Panel of Experts on Food Security and Nutrition of the Committee on World Food Security. https://www.fao.org/3/ca9731en/ca9731en.pdf Ingram, J. (2011). A food systems approach to researching food security and its interactions with global environmental change. Food Security, 3 (4), 417–431. https://doi.org/10.1007/s12571-011-0149-9 IPCC. (2022). Climate change 2022: Impacts, adaptation and vulnerability . Sixth Assessment Report of the Intergovernmental Panel on Climate Change. https://www.ipcc.ch/report/ar6/wg2/ Islam, M. S., Rahman, M. M., & Haque, M. A. (2021). Food insecurity in South Asia: A comprehensive review. Sustainability, 13 (10), 5571. https://doi.org/10.3390/su13105571 Jones, A. D., Ngure, F. M., Pelto, G., & Young, S. L. (2013). What are we assessing when we measure food security? A compendium and review of current metrics. Advances in Nutrition, 4 (5), 481–505. https://doi.org/10.3945/an.113.004119 Laborde, D., Martin, W., & Vos, R. (2020). Poverty and food insecurity could grow dramatically as COVID-19 spreads. IFPRI Blog . https://www.ifpri.org/blog/poverty-and-food-insecurity-could-grow-dramatically-covid-19-spreads Lipper, L., Thornton, P., Campbell, B. M., et al. (2014). Climate-smart agriculture for food security. Nature Climate Change, 4 (12), 1068–1072. https://doi.org/10.1038/nclimate2437 Maxwell, D., Vaitla, B., Tesfay, G., Abadi, N., & Kim, J. J. (2014). How do different indicators of household food security compare? Food Policy, 47 , 107–116. https://doi.org/10.1016/j.foodpol.2014.04.003 Mechlem, K. (2004). Food security and the right to food in the discourse of the United Nations. European Law Journal, 10 (5), 631–648. https://doi.org/10.1111/j.1468-0386.2004.00238.x Oke, M. A. (2015). Determinants of National Food Security in Nigeria . Journal of Economics and Sustainable Development , 6(9), 100–106. Retrieved from http://www.iiste.org Patel, R. (2009). Food sovereignty. The Journal of Peasant Studies, 36 (3), 663–706. https://doi.org/10.1080/03066150903143079 Pinstrup-Andersen, P. (2009). Food security: Definition and measurement. Food Security, 1 (1), 5–7. https://doi.org/10.1007/s12571-008-0002-y Swinnen, J., & McDermott, J. (Eds.). (2020). COVID-19 and global food security . International Food Policy Research Institute. https://doi.org/10.2499/p15738coll2.133762 Trendov, N. M., Varas, S., & Zeng, M. (2019). Digital technologies in agriculture and rural areas: Status report . FAO. https://www.fao.org/3/ca4887en/ca4887en.pdf UNHRC. (2001). The right to food: Report of the Special Rapporteur on the right to food, Jean Ziegler . United Nations Commission on Human Rights. von Braun, J. (2022). Food security risks arising from the Ukraine war and possible responses. ZEF Policy Brief . https://www.zef.de/fileadmin/webfiles/downloads/zef_policy_brief/ZEF_Policy_Brief_39_eng.pdf Webb, P., Coates, J., Frongillo, E. A., Rogers, B. L., Swindale, A., & Bilinsky, P. (2006). Measuring household food insecurity: Why it's so important and yet so difficult to do. The Journal of Nutrition, 136 (5), 1404S–1408S. https://doi.org/10.1093/jn/136.5.1404S World Bank. (2022). Food systems digital transformation: Framework and action plan . https://www.worldbank.org/en/topic/agriculture/publication/food-systems-digital-transformation-framework Additional Declarations No competing interests reported. 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Introduction","content":"\u003cp\u003eFood security has emerged as one of the most pressing global development challenges of the 21st century. According to the Food and Agriculture Organization (FAO), over 735\u0026nbsp;million people faced hunger in 2022, with millions more experiencing some level of food insecurity (FAO, 2023). The FAO defines food security through four interrelated dimensions: availability, access, utilization, and stability (FAO, 2008). These dimensions are influenced by a complex interplay of environmental, economic, and social factors, making food security not only a humanitarian concern but also a critical issue of governance, development, and national stability. In the face of climate change, conflict, and population growth, building resilient and inclusive food systems has become an imperative global policy (Headey \u0026amp; Ecker, 2013).\u003c/p\u003e\u003cp\u003eThis urgency is particularly acute in regions with extreme environmental constraints and high reliance on imported food. The Gulf Cooperation Council (GCC) countries\u0026mdash;Saudi Arabia, the United Arab Emirates, Bahrain, Kuwait, and Qatar face a unique paradox: despite their high incomes and advanced infrastructure, they remain structurally food insecure due to limited agricultural capacity, water scarcity, and dependence on global supply chains. This vulnerability, coupled with external shocks like the COVID-19 pandemic and the Russia\u0026ndash;Ukraine war, highlights food security as a strategic issue at the intersection of national development, foreign policy, and long-term regional stability.\u003c/p\u003e\u003cp\u003eThe GCC\u0026rsquo;s agricultural limitations are compounded by harsh climate conditions, including extreme heat, poor soil quality, and severe water shortages, all of which restrict local food production. These countries are classified among the most water-scarce regions in the world, with very little renewable freshwater available and rapidly depleting groundwater reserves. As a result, the GCC imports over 80\u0026ndash;90% of its food. Any disruption in global trade, whether caused by geopolitical tensions, climate disasters, or economic crises, poses a serious risk to food availability and price stability, and could trigger social and political unrest.\u003c/p\u003e\u003cp\u003eEnsuring stable food supplies is therefore essential for maintaining public welfare, social cohesion, and national security. Food security has become central to national strategies such as Saudi Arabia\u0026rsquo;s Vision 2030 and the UAE\u0026rsquo;s Food Security Strategy 2051, both of which prioritize sustainable development, technological innovation, and reduced import dependency. Investments in agritech, vertical farming, aquaponics, and global food supply chain diversification are key elements of these plans. The UAE has emerged as a leader in adopting innovative agricultural technologies, hosting global summits, and advancing climate-resilient crop research, while Qatar\u0026rsquo;s experience during the 2017 blockade highlighted the importance of building domestic agricultural capacity to achieve greater self-sufficiency.\u003c/p\u003e\u003cp\u003eMeanwhile, rapid population growth and accelerating urbanization across the Gulf are placing additional pressure on food supply systems. Rising incomes and shifting consumer preferences demand a wider variety of food products, many of which are imported. Urban expansion also consumes limited arable land, further constraining local production. While the region\u0026rsquo;s wealth allows it to access international markets in the short term, the long-term sustainability of food security is challenged by climate volatility, resource competition, and global political risks.\u003c/p\u003e\u003cp\u003eThe broader Middle East shares many of these challenges, often in more acute forms. Political instability, conflict, poor water governance, and fragile institutions in countries like Syria, Yemen, Iraq, and Lebanon have resulted in widespread agricultural system collapse, mass displacement, and acute food insecurity. These dynamics indirectly affect the GCC as well, by destabilizing regional markets and placing pressure on shared food trade routes.\u003c/p\u003e\u003cp\u003eIn response to these vulnerabilities, GCC countries have pursued various strategies to enhance food security. Saudi Arabia has invested heavily in agricultural land abroad, acquiring farmland in countries such as Sudan and Ukraine to secure future supplies. At home, Saudi Arabia is promoting sustainable farming projects as part of its broader development initiatives. Similarly, the UAE has invested in agritech innovation and vertical farming to reduce reliance on imports. Qatar, post-blockade, dramatically expanded its dairy, poultry, and vegetable production, achieving significant gains in local food self-sufficiency. Kuwait and Bahrain have focused on building strategic food reserves, enhancing storage infrastructure, and diversifying import sources to shield themselves from external shocks.\u003c/p\u003e\u003cp\u003eHowever, significant challenges persist. High global food prices, currency fluctuations, and the fragility of maritime supply chains\u0026mdash;such as dependence on the Strait of Hormuz and the Suez Canal\u0026mdash;expose the region to potential disruptions. Climate change continues to loom large, intensifying water scarcity, threatening global crop yields, and exacerbating vulnerabilities across the supply chain. Moreover, while some progress has been made in adopting sustainable agricultural technologies, regional disparities in food system resilience remain stark.\u003c/p\u003e\u003cp\u003eThis study aims to evaluate the determinants of food security in the GCC, focusing on environmental limitations, economic dependencies, and geopolitical risks. It also examines national efforts to build resilience and reduce vulnerabilities to external shocks. By analyzing panel data from 2000 to 2023 and assessing key variables such as food imports, exports, energy use in agriculture, land availability, GDP growth, and urbanization, the research provides evidence-based insights for policymakers. The goal is to offer practical recommendations for strengthening food system sustainability, enhancing self-reliance, and ensuring equitable access to nutritious food for the region's growing populations.\u003c/p\u003e\u003cp\u003eThe structure of this paper is as follows: following the introduction, the literature review explores global and regional frameworks and empirical studies on food security. The methodology section outlines the data sources and analytical approach. The results section evaluates the current state of food security in the GCC, and the final section provides recommendations aimed at enhancing food system resilience and long-term sustainability.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eThe concept of food security has evolved significantly over the past two decades, reflecting shifts in global concerns related to hunger, poverty, sustainability, and resilience. Initially centered on national and global food availability, the definition has expanded to encompass broader socio-economic, nutritional, and human rights dimensions. According to the Food and Agriculture Organization (FAO, 2008), food security exists when all people, at all times, have physical, social, and economic access to sufficient, safe, and nutritious food that meets their dietary needs and preferences. Scholars such as Clay (2002) and Burchi, Fanzo, and Frison (2011) have critiqued early definitions for focusing too narrowly on caloric intake and supply, without adequately considering food quality, affordability, or cultural relevance. Over time, both academic and policy discourses have increasingly emphasized the importance of household-level access, nutrition, and social equity.\u003c/p\u003e\u003cp\u003eThis broader understanding led to the development of the FAO\u0026rsquo;s four-pillar framework\u0026mdash;availability, access, utilization, and stability\u0026mdash;which now underpins most food security analyses (FAO, 2008; Pinstrup-Andersen, 2009). These pillars highlight the multifaceted and interdependent nature of food security. Scholars such as Gregory, Ingram, and Brklacich (2005) and Ingram (2011) argue that environmental factors, including climate change, biodiversity loss, and water availability, are critical to understanding food systems. Others (e.g., Barrett, 2010; Headey \u0026amp; Ecker, 2013) stress the importance of integrating nutrition-sensitive approaches that go beyond food quantity to assess dietary quality and health outcomes. Market dynamics, livelihoods, and governance structures also shape food access, especially in low- and middle-income countries (Anderson, 2009; HLPE, 2020).\u003c/p\u003e\u003cp\u003eA central contribution to this debate comes from Oke (2015), who conducted a country-specific analysis of Nigeria\u0026rsquo;s food security. Oke argues that food security must be measured not only by food availability but also through indicators such as food price inflation, which reflects the population\u0026rsquo;s purchasing power and access to food. In his empirical model, agricultural GDP, food imports/exports, per capita income, and population size were shown to significantly influence food price levels. His findings emphasize the crucial role of agricultural productivity and macroeconomic stability in achieving national food security, especially in developing economies.\u003c/p\u003e\u003cp\u003eRights-based approaches have further influenced contemporary debates. Scholars like De Schutter (2010) and Mechlem (2004) argue that food should be recognized as a human right rather than a commodity, emphasizing state accountability and non-discrimination. The Right to Food, enshrined in various UN declarations (UNHRC, 2001), frames food security as both a legal and moral obligation, particularly in ensuring protection for vulnerable populations. Patel (2009) expands this perspective through the concept of food sovereignty, which advocates for community control over food systems and the protection of local producers from global market disruptions.\u003c/p\u003e\u003cp\u003eTo capture the complexity of food security, several measurement tools have been developed. Notable examples include the Household Food Insecurity Access Scale (HFIAS) (Coates et al., 2007), the Food Insecurity Experience Scale (FIES) (Cafiero et al., 2014), and the Global Hunger Index. These tools incorporate subjective experience, dietary diversity, and coping mechanisms, offering a more comprehensive picture than calorie-based metrics alone (Jones et al., 2013; Webb et al., 2006). Nevertheless, challenges remain. Maxwell et al. (2014) note that many indicators fail to capture regional disparities or acute, crisis-driven food insecurity in fragile states. As a result, organizations such as FAO (2021) and the HLPE (2020) call for stronger integration of gender, nutrition, and environmental resilience in food security assessments.\u003c/p\u003e\u003cp\u003eRecent global shocks have underscored the fragility of food systems. The COVID-19 pandemic disrupted supply chains, reduced incomes, and triggered food price inflation worldwide (Laborde et al., 2020; Swinnen \u0026amp; McDermott, 2020). B\u0026eacute;n\u0026eacute; (2020) emphasizes the growing importance of resilience\u0026mdash;both at household and systemic levels\u0026mdash;as a core component of food security. The war in Ukraine further exposed global market volatility, disrupting fertilizer and grain supply chains and leading to price surges that disproportionately affected low-income, food-importing countries (FAO, 2022; von Braun, 2022). These events underscore the urgent need for food systems that are not only productive, but also inclusive, equitable, and shock-resistant.\u003c/p\u003e\u003cp\u003eAlongside global perspectives, region-specific approaches are increasingly relevant. In the Middle East and North Africa (MENA), factors such as water scarcity, political instability, and dependence on food imports pose distinct challenges (Al-Mandhari et al., 2022). Fanzo et al. (2018) emphasize that climate resilience, governance capacity, and shifting dietary patterns must be considered when assessing food security in developing regions. Studies from sub-Saharan Africa (Fraval et al., 2019) and South Asia (Islam et al., 2021) highlight the influence of household structure, migration, and gender roles on food access and utilization. These findings reinforce the importance of adapting global frameworks to account for local geographic, socio-cultural, and institutional contexts.\u003c/p\u003e\u003cp\u003eTechnological innovation is also reshaping food security. Advances in satellite-based early warning systems, blockchain-enabled supply chains, and artificial intelligence for climate forecasting have enhanced food system monitoring and planning (Trendov et al., 2019). However, the digital divide poses risks of exclusion; without inclusive policies, such innovations may exacerbate food insecurity in marginalized or rural communities (World Bank, 2022).\u003c/p\u003e\u003cp\u003eFinally, climate-smart agriculture and environmental sustainability are increasingly recognized as essential for long-term food security. According to FAO (2013), sustainable food systems must meet current nutritional needs without compromising future resource availability. The IPCC (2022) warns that agricultural productivity in tropical and subtropical regions is projected to decline even under moderate warming scenarios, with serious implications for hunger and displacement. In this context, conservation agriculture, agroecology, and low-emission livestock practices are seen as critical (Lipper et al., 2014). International coalitions such as the Global Alliance for Climate-Smart Agriculture (GACSA) advocate for integrated, multi-level cooperation among governments, the private sector, and civil society to support the transition toward resilient, low-carbon food systems.\u003c/p\u003e"},{"header":"3. Data and Model Specification","content":"\u003cp\u003eThis study employs an econometric framework using balanced panel data from five Gulf Cooperation Council (GCC) countries: Saudi Arabia, the United Arab Emirates, Qatar, Kuwait, and Bahrain, spanning the period 2000 to 2023. The primary objective is to examine the influence of key macroeconomic, agricultural, and demographic factors on food security, proxied by the Food Price Index (FPI). Given the dual structure of the data\u0026mdash;cross-sectional (across countries) and time-series (across years)\u0026mdash;panel regression techniques are applied to account for both temporal dynamics and country-specific heterogeneity. Two alternative estimation strategies are considered: the Fixed Effects (FE) model, which controls for unobserved, time-invariant characteristics unique to each country, and the Random Effects (RE) model, which assumes that these country-specific effects are uncorrelated with the explanatory variables. This dual modeling approach enables robust inference regarding drivers of food price inflation across the region.\u003c/p\u003e\u003cp\u003eThe econometric specification is as follows:\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{FPI}_{it}=\\:{\\beta\\:}_{0}+{{\\beta\\:}_{1}\\left(FI\\right)}_{it}+{{\\beta\\:}_{2}\\left(FX\\right)}_{it}{{+\\beta\\:}_{3}\\left(EIA\\right)}_{it}+{{\\beta\\:}_{4}\\left(AL\\right)}_{it}+{{\\beta\\:}_{5}\\left(GDP\\right)}_{it}+\\:{{\\beta\\:}_{6}\\left(AP\\right)}_{it}\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003e​ +\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\beta\\:}_{7}\\left(PU\\right)}_{it}{{+\\beta\\:}_{8}\\left(UPG\\right)}_{it}\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003e+\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:+{u}_{i}+{\\epsilon\\:}_{it}\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003e(i)\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe dependent variable in this analysis is the Food Price Index (FPI), which serves as a proxy for food security by capturing inflation in food prices over time. Among the explanatory variables, Food Imports (FI), measured as a percentage of total imports, reflect the extent of external dependency for food supplies, while Food Exports (FX) capture the region's outbound food trade performance. Employment in Agriculture (EIA) represents the share of the labor force engaged in agricultural activities, indicating the sector's role in domestic food production. Agricultural Land (AL), expressed as a percentage of total land area, serves as a proxy for agricultural capacity and land availability. Gross Domestic Product (GDP) reflects the overall economic performance and income levels, which may influence affordability and access to food. Agricultural Productivity (AP) is included to account for output efficiency in the agricultural sector. The model also incorporates People Undernourished (PU) to capture nutritional adequacy and food access challenges, and Urban Population Growth (UPG) as a demographic pressure indicator, potentially affecting food demand and distribution (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The term ​\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{u}_{i}\\)\u003c/span\u003e\u003c/span\u003e represents unobserved, time-invariant country-specific effects in the Fixed Effects model, while ​\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{u}_{i}\\:\\)\u003c/span\u003e\u003c/span\u003edenotes random country-specific effects in the Random Effects model. Finally, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e​ captures the idiosyncratic error term, accounting for unobservable shocks that vary across countries and over time.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eVariable Description\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLabel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMeasurement / Unit\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFPI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFood Price Index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIndex measuring changes in food prices, used as a proxy for food security.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndex (base year assumed\u0026thinsp;=\u0026thinsp;100)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFood Imports\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eShare of food imports relative to total imports.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercentage (%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFood Exports\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eShare or volume of food exports.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndex)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEIA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEmployment in Agriculture\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eProportion of total labor force employed in agriculture.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercentage (%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAgricultural Land\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAgricultural land area as a share of total land.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercentage (%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGross Domestic Product\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGDP per capita or growth rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e% change\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAgricultural Productivity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOutput per worker or per hectare in agriculture (e.g., yields).\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMetric tons per hectare\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePeople Undernourished\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePercentage of population lacking sufficient caloric intake.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercentage (%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUPG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUrban Population Growth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAnnual growth rate of the urban population.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercentage (%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe model is estimated through panel data techniques, employing both Fixed Effects (FE) and Random Effects (RE) regressions to account for the longitudinal and cross-sectional structure of the data. Robust standard errors are applied to correct for potential heteroskedasticity in the residuals, ensuring reliable statistical inference. The Fixed Effects model controls unobserved, time-invariant heterogeneity across countries, denoted as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{u}_{i}\\:\\)\u003c/span\u003e\u003c/span\u003eby focusing on within-country variation over time. This approach isolates the effect of the explanatory variables net of any country-specific characteristics that remain constant throughout the study period. Conversely, the Random Effects model treats the country-specific effects \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{u}_{i}\\:\\)\u003c/span\u003e\u003c/span\u003e​as random and uncorrelated with the independent variables, thereby capturing both within- and between-country variation. To identify the more appropriate specification, the Hausman test is employed. This statistical test evaluates whether the unique errors \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{u}_{i}\\:\\)\u003c/span\u003e\u003c/span\u003eare correlated with the regressors; a significant test result favors the FE model, while a non-significant result suggests the RE model yields more efficient and consistent estimates.\u003c/p\u003e"},{"header":"4. Discussion of Results","content":"\u003cp\u003eTo begin the empirical analysis, this section presents the descriptive statistics of the variables included in the model, offering a preliminary overview of their central tendencies, dispersion, and distribution patterns across the sample of GCC countries over the study period.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive Statistics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eObs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStd. Dev.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFPI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e101.466\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19.204\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e61.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e138.786\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.877\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.792\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e20.055\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.169\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.332\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.338\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEIA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.916\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.901\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.534\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22.626\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e29.267\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.304\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e80.848\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.455\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.215\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-7.076\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e26.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15.332\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.669\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.383\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e40.858\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e99.675\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.512\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e98.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUPG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-2.605\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19.612\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reports the descriptive statistics for the variables used in this study, based on data from five GCC countries between 2000 and 2023. The dependent variable, Food Price Inflation (FPI), shows moderate variability with a mean of 101.47 and a standard deviation of 19.20. Food Imports (FI) and Food Exports (FX) exhibit considerable cross-country variation, averaging 11.11% and 1.17, respectively. Employment in Agriculture (EIA) and Agricultural Land (AL) display structural heterogeneity, with means of 2.92% and 22.63% and wide observed ranges. GDP growth averages 4.46%, ranging from \u0026minus;\u0026thinsp;7.08\u0026ndash;26.17%, while Agricultural Productivity (AP) varies substantially, with a mean of 15.33. The Prevalence of Undernourishment (PU) is consistently high at 99.68%, suggesting broad nutritional sufficiency. Urban Population Growth (UPG) averages 4.16%, reflecting diverse urbanization trends across the region. These statistics highlight the heterogeneity in economic and agricultural conditions, forming a solid basis for the subsequent regression analysis.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePairwise Correlations\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(FPI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(FI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(FX)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(EIA)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(AL)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(GDP)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(AP)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e(PU)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e(UPG)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTime\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFPI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.155*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.379***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.114\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEIA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.202**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.264***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.220**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.537***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.632***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.432***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.321***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.247***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.054\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.092\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.096\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.469***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.615***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.800***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.459***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.723***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.983***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.799***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUPG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.386***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.400***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.333***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.198**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.435***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.073\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003e*** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the pairwise correlation coefficients, focusing on the relationships between Food Price Inflation (FPI) and the explanatory variables. FPI exhibits a positive correlation with Food Exports (FX) (0.379), suggesting that higher export levels may coincide with increased domestic food prices, potentially due to reduced local supply or export-driven market priorities. A negative correlation is observed between FPI and GDP growth (\u0026minus;\u0026thinsp;0.432), indicating that stronger economic performance may contribute to greater price stability in food markets. Additionally, Food Imports (FI) show a weak positive correlation with FPI, while Agricultural Productivity (AP) and Prevalence of Undernourishment (PU) exhibit weak negative correlations. These preliminary associations suggest that external trade, economic performance, and agricultural efficiency may play significant roles in shaping food price dynamics across the GCC region.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eModel Estimation\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(FE)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(RE)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFPI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFPI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.483***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.778***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(.882)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(.544)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.298***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.016**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(1.199)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEIA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-11.299***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-5.773***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1.649)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(1.387)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.477***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-2.04***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(2.563)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(.413)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.298\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.915***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(.304)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.663**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(.312)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(.311)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-127.017***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-124.047***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(30.847)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(24.784)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUPG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.815*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.927**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(.424)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(.432)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e_cons\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12606.701***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12511.61***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(3089.268)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2480.171)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWithin R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.542\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.439\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eStandard errors are in parentheses\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003e*** p\u0026thinsp;\u0026lt;\u0026thinsp;.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;.1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eHausman Test Chi^2\u0026thinsp;=\u0026thinsp;91.48, Prob\u0026thinsp;\u0026gt;\u0026thinsp;chi2\u0026thinsp;=\u0026thinsp;0.0000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e reports the estimation results from both the Fixed Effects (FE) and Random Effects (RE) models, examining the determinants of Food Price Inflation (FPI) across five GCC countries over the period 2000\u0026ndash;2023, using a balanced panel of 120 observations. A key finding is the robust positive association between Food Imports (FI) and FPI in both models, with statistically significant coefficients of 2.483 in the FE specification and 1.778 in the RE model. These results highlight the critical role of import dependency in driving domestic food price dynamics. Specifically, the positive and significant coefficients suggest that a one-unit increase in the proportion of food imports is associated with a notable rise in food price inflation, holding other factors constant. This relationship is particularly salient in the context of the Gulf region, where countries rely on imports for over 80% of their food consumption due to limited arable land, water scarcity, and harsh climatic conditions. Such structural dependence on international food markets renders the region highly vulnerable to global price shocks, currency fluctuations, and trade disruptions. The inflationary impact of food imports is further exacerbated during periods of external economic stress, such as oil price collapses, when fiscal revenues decline and the capacity for food subsidy programs or price stabilization mechanisms is constrained. As a result, heightened food price inflation not only erodes consumer purchasing power but also poses serious risks to food security, particularly for low- and middle-income households. The empirical evidence thus reinforces the importance of developing diversified food supply strategies, enhancing regional food storage capacities, and strengthening social safety nets to mitigate the inflationary effects of external food dependency.\u003c/p\u003e\u003cp\u003eSimilarly, Food Exports (FX) exhibit a positive and statistically significant relationship with Food Price Inflation in both model specifications, with estimated coefficients of 5.298 in the Fixed Effects (FE) model and 3.016 in the Random Effects (RE) model, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. These findings suggest that increasing food export activity\u0026mdash;often promoted as part of broader economic diversification strategies\u0026mdash;may exert upward pressure on domestic food prices. In the context of the GCC, governments have increasingly invested in agri-tech and high-value agricultural products for export to reduce dependence on oil revenues and position themselves in global markets. However, this outward orientation in the food sector can have unintended inflationary consequences at home. The positive association between food exports and domestic food price inflation may be attributed to supply reallocation, wherein production is directed toward more profitable export channels at the expense of local availability. This shift can lead to domestic shortages or constrain supply buffers, thereby pushing up prices in local markets. Additionally, export-driven policies may result in price transmission from global to domestic markets, particularly if export-linked sectors are tightly integrated with international commodity pricing structures. For countries already grappling with food insecurity and limited domestic production capacity, such dynamics can intensify the volatility of food prices and undermine national food security objectives.\u003c/p\u003e\u003cp\u003eIn contrast to food trade variables, Employment in Agriculture (EIA) is found to be negatively and significantly associated with Food Price Inflation, with estimated coefficients of \u0026minus;\u0026thinsp;11.299 in the Fixed Effects (FE) model and \u0026minus;\u0026thinsp;5.773 in the Random Effects (RE) model. Although this inverse relationship may initially appear counterintuitive, it reflects structural characteristics unique to the agricultural sector in the GCC region. Agriculture in these countries accounts for only a small fraction of GDP and employs a limited share of the population, often concentrated in subsistence or low-productivity activities. The negative association suggests that increases in agricultural employment, within the current institutional and technological framework, may signal marginal improvements in local food supply or reflect government-supported efforts to maintain production capacity, thereby exerting mild deflationary pressure on food prices.\u003c/p\u003e\u003cp\u003eHowever, the broader interpretation must consider the inefficiencies that often characterize agricultural employment in arid and resource-constrained environments. In the GCC, agricultural labor is frequently underutilized or directed toward small-scale and water-intensive farming that is not cost-effective or scalable. The observed relationship may thus reflect diminishing returns in a sector constrained by land degradation, limited freshwater resources, and climate volatility. Despite these challenges, the findings also highlight a potential opportunity: if agricultural employment were expanded under a framework of modernization supported by smart irrigation technologies, mechanization, and regional agro-logistics\u0026mdash;its role in stabilizing domestic food prices could become more pronounced.\u003c/p\u003e\u003cp\u003eThe effect of Agricultural Land (AL) on Food Price Inflation (FPI) varies notably across model specifications, highlighting important distinctions in how land expansion interacts with food price dynamics. In the Fixed Effects (FE) model, AL is positively and significantly associated with FPI, with a coefficient of +\u0026thinsp;0.179, whereas the Random Effects (RE) model yields a negative and significant coefficient of \u0026minus;\u0026thinsp;0.134. This divergence suggests that the relationship between agricultural land and food price inflation is sensitive to whether the variation is examined within countries over time or across countries at a given point.\u003c/p\u003e\u003cp\u003eThe positive coefficient in the FE model implies that, over time, an increase in the share of agricultural land within a given country is associated with higher food price inflation. While this may seem counterintuitive, it likely reflects the limited productivity and high cost of expanding agricultural land in the GCC context, where much of the territory is arid or desert. Efforts to bring more land under cultivation may be accompanied by increased costs of irrigation, energy, and maintenance, all of which can feed into domestic food prices rather than alleviate them. Additionally, land expansion may not be matched by proportional gains in output if soil quality is poor or if farming techniques remain outdated.\u003c/p\u003e\u003cp\u003eConversely, the negative coefficient in the RE model\u0026mdash;driven by between-country variation\u0026mdash;may indicate that countries with relatively more agricultural land tend to experience lower levels of food price inflation, perhaps due to more favorable agro-climatic conditions or historical investments in farming infrastructure. However, this cross-sectional relationship does not capture the underlying constraints on scalability or sustainability in the GCC. Given chronic water scarcity, soil salinity, and extreme temperatures, the potential for land-based agricultural expansion is inherently limited in the region. These environmental barriers diminish the effectiveness of land area expansion as a long-term strategy for price stabilization or food security.\u003c/p\u003e\u003cp\u003eTaken together, the contrasting signs between FE and RE models underscore the importance of contextual and structural factors. In the case of the GCC, expanding agricultural land alone may not be sufficient to curb food price inflation unless accompanied by parallel investments in water-efficient technologies, sustainable land use practices, and productivity-enhancing innovations.\u003c/p\u003e\u003cp\u003eInterestingly, Gross Domestic Product (GDP) is found to be statistically insignificant in both the Fixed Effects (FE) and Random Effects (RE) models, suggesting that aggregate economic growth does not exert a meaningful influence on Food Price Inflation (FPI) across GCC countries. This result indicates a decoupling between macroeconomic performance and food price dynamics, whereby increases in national income do not necessarily translate into improved food affordability or price stability. One plausible explanation lies in the sectoral composition of GDP, particularly in hydrocarbon-dominated economies such as those in the GCC, where oil and gas revenues heavily influence GDP figures but do not directly impact food production, distribution, or pricing.\u003c/p\u003e\u003cp\u003eMoreover, the absence of a statistically significant relationship may reflect issues related to income concentration and distribution, whereby the benefits of growth accrue to capital-intensive sectors and high-income groups, with limited spillover effects for household-level food security. In such contexts, strong GDP growth may coexist with persistent price pressures if rising incomes do not translate into strengthened food systems or targeted social safety nets. This dynamic is further exacerbated in countries that rely heavily on imported food, where domestic purchasing power may be eroded by exchange rate volatility, global commodity price shocks, or inflation pass-through effects.\u003c/p\u003e\u003cp\u003eThe finding also highlights the limitations of using GDP as a proxy for welfare outcomes in resource-rich economies. While GDP growth is often viewed as an indicator of development progress, it may mask underlying vulnerabilities in food access, especially among lower-income populations who are more sensitive to changes in food prices. In this context, the insignificance of GDP in explaining FPI underscores the need for more targeted policies, such as subsidies, income transfers, and domestic food production incentives, to ensure that economic gains translate into improved food affordability and security at the household level.\u003c/p\u003e\u003cp\u003eAgricultural Productivity (AP) exhibits a negative relationship with Food Price Inflation (FPI) under the Fixed Effects (FE) model, with a coefficient of \u0026minus;\u0026thinsp;0.138 that is marginally significant. This suggests that, within countries over time, improvements in agricultural efficiency\u0026mdash;whether through better input use, yield enhancement, or technological adoption\u0026mdash;can contribute to moderating food price inflation. However, under the Random Effects (RE) model, AP is statistically insignificant, indicating that the relationship does not hold consistently across countries. This divergence in results may stem from structural heterogeneity in agricultural systems, varying levels of technological adoption, or differences in how productivity is measured and reported across the GCC region.\u003c/p\u003e\u003cp\u003eThe marginally significant negative association in the FE model aligns with the theoretical expectation that greater output per unit of input can ease supply-side constraints, reduce import dependency, and stabilize food prices. Yet, the lack of significance in the RE model points to deeper cross-country disparities. In particular, productivity gains in one country may not be generalizable to others due to contextual factors such as water availability, climate conditions, institutional support, and the scale of investment in agricultural R\u0026amp;D. In the GCC context, the potential benefits of productivity improvements are often constrained by limited natural resources, fragmented agricultural strategies, and the slow adoption of climate-resilient technologies. Despite ongoing efforts to integrate smart agriculture and hydroponic systems, progress has been uneven, and productivity gains remain localized rather than systemic. Moreover, existing agricultural activities are frequently policy-driven rather than market-driven, reducing incentives for innovation and efficiency gains. Overall, while within-country improvements in agricultural productivity appear to offer a pathway for mitigating food price inflation, the broader effectiveness of such gains is likely moderated by structural and institutional barriers. Policymakers aiming to stabilize food prices through productivity growth must therefore address these underlying constraints, ensuring that productivity improvements are both scalable and aligned with the region\u0026rsquo;s environmental and resource realities.\u003c/p\u003e\u003cp\u003eTwo key demographic indicators\u0026mdash;Prevalence of Undernourishment (PU) and Urban Population Growth (UPG)\u0026mdash;exhibit positive and statistically significant relationships with Food Price Inflation (FPI) across both Fixed Effects (FE) and Random Effects (RE) models. The coefficient estimates for PU are +\u0026thinsp;0.293 (FE) and +\u0026thinsp;0.267 (RE), suggesting that higher levels of undernourishment are associated with increased food price pressures. This reflects underlying structural deficiencies in food accessibility and affordability, where persistent nutritional gaps may co-occur with rising prices, especially in contexts lacking comprehensive food support systems. UPG similarly shows strong positive coefficients of +\u0026thinsp;0.361 (FE) and +\u0026thinsp;0.335 (RE), indicating that rapid urban expansion contributes to inflationary pressures through rising demand, infrastructural strain, and logistical inefficiencies.\u003c/p\u003e\u003cp\u003eIn the GCC context, these results are particularly relevant. Urbanization has progressed rapidly, yet often without corresponding investments in integrated food distribution infrastructure or local production capacity. Simultaneously, food systems remain highly import-dependent, making them vulnerable to external shocks. As urban populations grow, ensuring affordable and stable food supplies becomes increasingly complex. These findings underscore the importance of linking demographic policy with food system planning\u0026mdash;specifically, the need to strengthen nutritional safety nets, improve supply chain efficiency, and align urban growth with sustainable food security strategies.\u003c/p\u003e\u003cp\u003eThe Fixed Effects (FE) model demonstrates superior explanatory power over the Random Effects (RE) model, accounting for 54.2% of the variation in Food Price Inflation (FPI), compared to 43.9% under the RE specification. This suggests that temporal changes within countries\u0026mdash;such as policy shifts, institutional reforms, or evolving demographic pressures\u0026mdash;are more informative in explaining food price dynamics than cross-country differences. The implication is clear: policy interventions targeting internal structural and sectoral factors are likely to be more effective than broad regional comparisons or externally benchmarked strategies.\u003c/p\u003e\u003cp\u003eThis insight carries important implications for the Gulf Cooperation Council (GCC) countries, whose food security remains tightly intertwined with macroeconomic volatility, particularly through oil revenue fluctuations. The results highlight an opportunity for these economies to recalibrate their development strategies by investing in sectors that directly influence domestic food systems. Strengthening local agricultural production, enhancing logistics and supply chain infrastructure, and addressing demographic pressures\u0026mdash;such as urban expansion and nutritional inequality\u0026mdash;can reduce the vulnerability of food prices to external shocks. Rather than relying predominantly on oil income to finance food imports, GCC states can redirect fiscal resources toward building resilient, sustainable, and innovation-driven food systems. Investments in climate-adapted agriculture, smart water management, and rural employment would not only support food price stability but also align with broader goals of economic diversification and long-term development in a post-oil context.\u003c/p\u003e\u003cp\u003eThe preference for the FE model is further validated by the results of the Hausman test, which yields a chi-square statistic of 91.48 (df\u0026thinsp;=\u0026thinsp;7) with a p-value of 0.0000\u0026mdash;strongly rejecting the null hypothesis that the RE model provides consistent estimates. This outcome confirms that unobserved country-specific effects are correlated with the explanatory variables, violating the RE model\u0026rsquo;s core assumption of orthogonality. Additionally, the marked differences in coefficient estimates between FE and RE models\u0026mdash;particularly for Agricultural Land (AL), Agricultural Productivity (AP), and Employment in Agriculture (EIA)\u0026mdash;underscore the importance of controlling for time-invariant heterogeneity across countries. Taken together, the statistical evidence and substantive findings justify the use of the FE model, which offers more robust and policy-relevant insights into the drivers of food price inflation in the GCC.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study investigated the determinants of food price inflation across five GCC countries over the period 2000 to 2023 using panel data econometric techniques. Drawing on both Fixed Effects (FE) and Random Effects (RE) models, the analysis incorporated a range of macroeconomic, agricultural, and demographic variables to identify the key drivers of food price volatility in the region. The results reveal that food imports, food exports, urban population growth, and the prevalence of undernourishment are consistently and positively associated with rising food prices, underscoring the region\u0026rsquo;s vulnerability to external shocks, demographic pressures, and structural inefficiencies in domestic food systems. Among the explanatory variables, food import dependence emerged as a significant contributor to food price inflation, highlighting the risks posed by global supply chain disruptions and international price volatility. Similarly, increased food exports\u0026mdash;often pursued as part of diversification strategies\u0026mdash;may reduce domestic availability, thus elevating local prices. On the demographic side, rapid urban growth and high rates of undernourishment were shown to intensify food inflationary pressures, indicating a need for more inclusive food policy frameworks and urban food planning. Conversely, variables such as agricultural productivity and employment in agriculture were negatively associated with food price inflation in the FE model, suggesting that targeted improvements in domestic agricultural efficiency could offer a buffer against rising prices.\u003c/p\u003e\u003cp\u003eImportantly, the statistical superiority of the Fixed Effects model, supported by the Hausman test, indicates that within-country variation provides more meaningful insight into food price dynamics than cross-sectional differences. This finding affirms the value of country-specific policy interventions over generalized regional solutions. It also points to the importance of institutional reforms and time-sensitive strategies that respond to domestic structural changes.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePolicy Implications\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe findings of this study carry several important policy implications for enhancing food security and mitigating food price inflation in the GCC region. First, the strong positive association between food imports and food price inflation underscores the urgent need to reduce external dependency through the development of resilient domestic food systems. Policymakers should prioritize strategic investments in agricultural innovation, including water-efficient technologies, desert farming, and vertical agriculture, to expand local production within ecological limits.\u003c/p\u003e\u003cp\u003eSecond, the inflationary effects of food exports suggest that export-oriented agricultural strategies must be carefully balanced with domestic food needs. While export diversification remains economically desirable, safeguards must be implemented to ensure that such initiatives do not compromise local food availability. This could involve regulating export volumes of essential food items and incentivizing production for domestic consumption through targeted subsidies or procurement policies.\u003c/p\u003e\u003cp\u003eThird, the significant influence of urban population growth on food inflation points to the need for integrated urban planning that incorporates food logistics, distribution infrastructure, and retail access. Rapidly expanding cities in the GCC must be supported with urban food resilience strategies, such as decentralized storage systems, efficient cold chains, and inclusive market access to ensure stable supply.\u003c/p\u003e\u003cp\u003eFourth, the association between undernourishment and rising food prices highlights the need to strengthen social safety nets and food assistance programs. Policymakers should enhance the coverage and efficiency of targeted nutrition programs to protect vulnerable populations from the adverse effects of food inflation, especially during periods of fiscal stress or supply chain disruptions. Finally, the statistical preference for the Fixed Effects model reinforces the importance of country-specific strategies over regional generalizations. Each GCC country should tailor its food security policies to its unique demographic, economic, and environmental conditions while promoting inter-GCC cooperation on knowledge transfer, strategic reserves, and coordinated responses to global food market volatility.\u003c/p\u003e\u003cp\u003eCollectively, these policy measures can reduce food price vulnerability, promote long-term food security, and support broader economic diversification objectives central to the GCC's post-oil development agenda.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting interests:\u003c/h2\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthical approval:\u003c/strong\u003e\u003cp\u003eEthical approval is not applicable because this article does not contain any studies with human or animal subjects.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eInformed consent:\u003c/h2\u003e\u003cp\u003eInformed consent is not applicable because this article does not contain any studies with human or animal subjects\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eThis study received no external funding\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMD conceptualized the study, conducted the literature review, collected the data, performed the empirical analysis, and led the drafting of the manuscript. RS provided critical input in shaping the research framework, supported the analytical process, and contributed substantially to the revision and refinement of the final manuscript. Both authors reviewed and approved the submitted version.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data supporting the findings of this study are available upon reasonable request from the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAl-Mandhari, A., Brennan, R. J., Abubakar, A., \u0026amp; Hajjeh, R. (2022). Food insecurity in the Eastern Mediterranean Region: Promoting regional cooperation and policy action. \u003cem\u003eEastern Mediterranean Health Journal, 28\u003c/em\u003e(6), 430\u0026ndash;432. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.26719/emhj.22.027\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAnderson, M. D. 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(2022). \u003cem\u003eFood systems digital transformation: Framework and action plan\u003c/em\u003e. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://www.worldbank.org/en/topic/agriculture/publication/food-systems-digital-transformation-framework\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Food Security, Food Imports, Panel Data, Urbanization, GCC","lastPublishedDoi":"10.21203/rs.3.rs-6844186/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6844186/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the key determinants of food price inflation across five Gulf Cooperation Council countries, such as Saudi Arabia, the United Arab Emirates, Qatar, Kuwait, and Bahrain, using panel data from 2000 to 2023. Employing both fixed effects and random effects models, the analysis explores the impact of macroeconomic, agricultural, and demographic factors on food price volatility. The results indicate that food imports, food exports, urban population growth, and the prevalence of undernourishment are significant contributors to rising food prices, reflecting the region\u0026rsquo;s dependence on external markets and the pressures of rapid urbanization. Conversely, agricultural productivity and employment in agriculture are negatively associated with food price inflation, highlighting the potential benefits of improving domestic production capacity. The fixed effects model outperforms the random effects model in explanatory power, a finding confirmed by the Hausman test, which supports the presence of unobserved country-level effects correlated with the explanatory variables. These results emphasize the importance of tailored, nationally specific policy responses over generalized regional strategies. The study concludes with recommendations to invest in sustainable agriculture, strengthen food logistics and social protection systems, and reduce reliance on oil-financed food imports by building resilient and inclusive food systems across the region.\u003c/p\u003e","manuscriptTitle":"Exploring the structural determinants of food security in the GCC region: Evidence from cross-country panel data analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-21 17:35:23","doi":"10.21203/rs.3.rs-6844186/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-15T19:27:49+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-07T13:42:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-06T20:06:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"28682402414165600452684770601472903544","date":"2025-07-29T19:36:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"112580529332270223926568168032662236697","date":"2025-07-25T10:54:55+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-17T06:42:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-17T06:39:30+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-17T05:13:22+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-09T11:43:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2025-07-09T11:40:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"4fae09f6-675a-4ad8-a0ba-3b3f9570f492","owner":[],"postedDate":"July 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":51716425,"name":"Business and commerce/Business and management"},{"id":51716426,"name":"Business and commerce/Economics"},{"id":51716427,"name":"Social science/Economics"}],"tags":[],"updatedAt":"2025-12-01T16:07:09+00:00","versionOfRecord":{"articleIdentity":"rs-6844186","link":"https://doi.org/10.1057/s41599-025-06148-1","journal":{"identity":"humanities-and-social-sciences-communications","isVorOnly":false,"title":"Humanities and Social Sciences Communications"},"publishedOn":"2025-11-28 15:58:01","publishedOnDateReadable":"November 28th, 2025"},"versionCreatedAt":"2025-07-21 17:35:23","video":"","vorDoi":"10.1057/s41599-025-06148-1","vorDoiUrl":"https://doi.org/10.1057/s41599-025-06148-1","workflowStages":[]},"version":"v1","identity":"rs-6844186","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6844186","identity":"rs-6844186","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}