Interconnectedness and interdependencies of critical infrastructures in the US economy: Implications for resilience

• A systems framework is developed to understand resilience of the US economy.
• Graph theory helps understand topological properties of the US EIO network.
• Hypothetical disruptions on CIS help determine economic interdependencies.
• Implications of interconnectedness and interdependencies on resilience are studied.
• US Economic network is vulnerable to greater CIS interdependencies.

Natural disasters in 2011 yielded close to $55 billion in economic damages alone in the United States (US), which highlights the need to reduce impacts of such disasters or other deliberate attacks. The US Department of Homeland Security (DHS) identifies a list of 16 Critical Infrastructure Sectors (CIS) whose incapacity due to disruptions would have a debilitating impact on the nation’s economy. The goal of this work is to understand the implications of interdependencies among CIS on the resilience of the US economic system as a whole. We develop a framework that combines the empirical economic input–output (EIO) model with graph theory based techniques for understanding interdependencies, interconnectedness and resilience in the US economic system. By representing the US economy as a network, we are able to analyze its topology by separately looking at its unweighted and weighted forms. Topological analysis of the US EIO network suggests that it exhibits small world properties for the unweighted case, and in the weighted case, the throughput of industry sectors follows a power-law with an exponential cutoff. Implications of these topological properties are discussed in the paper. We also simulate hypothetical disruptions on CIS in order to identify industrial sectors that experience the largest economic impacts, and to quantify systemic vulnerability in economic terms. In addition, insights from community detection and hypothetical disruption scenarios help assess vulnerability of individual industrial communities to disruptions on individual CIS. These methodologies also provide insights regarding the extent of coupling between each CIS in the US EIO network. Based on our analysis, we observe that excessive interconnectedness and interdependencies of CIS results in high systemic vulnerability. This information can guide policymakers to design policies that improve resilience of economic networks, and evaluate policies that might indirectly increase coupling between CIS.