Calculating the Risk for Food Crises in the Middle East: The Role of Social Network Analysis

Net food-importing countries face unique challenges in food security. Traditional value chain analysis and food security assessments do not consider a country’s international trade network when calculating the food security levels of a country. This project focuses on wheat trade in the Middle East and North Africa (MENA) region. Wheat is highly consumed in the region, which is overall a net importer of the commodity. In addition, wheat has a cultural importance in the region—riots and civil unrest have often been tied to the rise in bread prices, and the existence of various subsidies of wheat and wheat products demonstrate the importance of making this commodity available to the population. However, the region as a whole is a net-importer of this commodity. Because of this, traditional mechanisms of food security assessments, which generally focus on domestic production, do not give a full picture of a country’s food security risks. Instead, assessments of the security of the wheat supply in most MENA countries should consider international trade indicators. Methodology

This research uses social network analysis methodology, and analyzes the global trade network in terms of the following indicators: number of links, network density, average clustering coefficient, and average betweenness centrality. Then, the top ranking countries are reported in two node-level indicators, eigenvector centrality, which detects major players overall, and authority centrality, which detects the most well connected importing countries. Finally, the analysis looks at 4 major export bans that affected the MENA region between 2005 and 2010, and notes several reactionary steps that MENA countries used to maintain supplies of wheat in the face of these bans. Reasons for failure to maintain supplies were also noted. Policy Question This research attempts to add to the understanding of the global wheat trade network from 1990-2012, with a particular focus on the MENA region. As several export bans between 2005 and 2010 provide exogenous shocks to the markets, another section of the analysis addresses MENA countries’ reactions to these sudden barriers to trade. As a preliminary look at the wheat trade network over time, this research addresses the question: How has the international trade network of grain in the MENA region reacted to economic shocks since 1990? How can analyzing the evolution of this trade network inform food security measures for the region?

Results Overall, the network is becoming more dense but less divided into clusters as countries are diversifying their trade and instigating partnerships outside of their previous clusters. On an individual country level, centrality measures can demonstrate when a country has succeeded or failed in forming new partnerships or increasing trade with important partners, often to overcome a barrier to trade such as an export ban. Finally, countries take different steps to overcome trade bans—forming new partnerships, increasing trade with existing partners, or increasing production. It seems that countries with some political clout or purchasing power from their size are able to accomplish this. Conclusion Social network analysis indicators are accurately descriptive in the actions of a country takes overcome a ban. Depending on changes in the rest of the network, if a country increases its partnerships or increases quantity traded from important partners in order to overcome a ban, this action increases the country’s centrality scores. While this analysis did not test a theory of correlation between a certain indicator and ability to maintain supplies, data from this project could be used to do so. It is recommended that further research explore what indicators would be useful to predict risk, and what indicators (if any), could be useful to predict resilience in the face of that risk.