Insights into food security: affordability perspectives from Bayesian belief network models

Abstract

PurposeThis study investigates the dependencies between the Global Food Security Index (GFSI) and its affordability-related indicators using Bayesian belief network (BBN) models. The research also aims to prioritise these indicators within a probabilistic network setting.Design/methodology/approachThe research utilises BBN models to analyse data from 113 countries in 2022. Nine indicators related to food affordability, including income inequality, safety net programmes and trade freedom, are examined to understand their impact on food security. The methodology involves statistical modelling and analysis to identify critical factors influencing food security and to provide a comprehensive understanding of the global food affordability landscape.FindingsThe study reveals that income inequality, the presence and efficacy of safety net programmes and the degree of trade freedom are significant determinants of food affordability and overall food security outcomes. The analysis reveals marked disparities in performance across different countries, highlighting the need for context-specific interventions. The findings suggest that improving safety net programmes, implementing trade policy reforms and addressing income inequality are crucial for enhancing food affordability and security.Originality/valueThis research contributes to the literature by using BBN models to comprehensively analyse the relationship between the GFSI and affordability-related indicators. The study provides novel insights into how different socioeconomic factors influence food security across a diverse range of countries. The study offers actionable recommendations for policymakers to address food security challenges effectively, thereby supporting the development of more equitable and resilient food systems globally.