Statistical Evaluation of Food Security Indicators in Arab Countries: A Multivariate Cluster and Factor Analysis of the Food Gap

Povzetek

This study addresses the challenge of food security in the Arab world amidst a widening food gap and increasing reliance on imports, despite the availability of natural and human resources. It aims to provide a statistical assessment of food security indicators for 22 Arab countries and classify them to identify strengths and weaknesses. The study relies on updated 2023 data derived from United Nations reports and the Joint Arab Economic Report. The methodology employs advanced exploratory statistical analysis, specifically Cluster Analysis and Principal Component Analysis (PCA), using Tanagra 1.4.50 software for data processing and interpretation. The results revealed a significant convergence between the two cluster analysis methods (KMeans and HCA) in classifying Arab countries into three distinct groups. Furthermore, Principal Component Analysis (PCA) reinforced these findings by extracting three axes that explain 82.76% of the total variance. The first axis, which includes variables such as agricultural output, the hunger index, and trade exchange, was associated with the first cluster, representing the most food-stable countries (UAE, Algeria, Egypt, Oman, Saudi Arabia, and Morocco). Conversely, the second axis reflected lagging food security indicators in the second cluster, which includes the Comoros, Mauritania, Sudan, Somalia, Syria, and Yemen. The third cluster comprised the remaining countries, which were characterized by indicators related to population size, fish production, and import dependency.