Unveiling Socioeconomic Factors Shaping Global Food Prices and Security: A Machine Learning Approach

Global concern over food prices and security has intensified due to armed conflicts such as the Russia–Ukraine war, the recent COVID-19 pandemic, and climate change. Traditional analysis of global food prices and their associations with socioeconomic factors has relied on static linear regression models. However, the complexity of socioeconomic factors and their implications extend beyond simple linear relationships. To address this gap, this study aimed to identify critical socioeconomic characteristics and multidimensional relationships influencing food prices and security by incorporating determinants, critical characteristic identification, and comparative model analysis. Machine learning tools were used to uncover the socioeconomic factors influencing global food prices from 2000 to 2022. Four key dimensions of food price security were identified: economic and population metrics, military spending, health spending, and environmental factors. Given the complexity of these dimensions, the support vector regression model’s efficiency rendered it most suitable for precise analyses among the models assessed. The findings revealed shifts in the food price index, particularly in relation to military expenditure, healthcare expenditure, and economic contributions. Based on these findings, the research proposes a framework centered around six thematic areas related to (1) governance; (2) health and environment; (3) environment, climate, and military spending; (4) comprehensive analytical tools; (5) collaborative efforts; and (6) resilience and sustainability. This framework enables policymakers to further expand on actionable recommendations.