The threat of economic disruption to the global economy has never been greater than it is today. Countries and advanced economies the world over are facing threats on multiple fronts. From natural disasters that have arguably grown more erratic and frequent due to climate change, to the rise in cyber-attacks, terrorist activities and other man-made crises. These types of threats are harder to predict, and contain. Governments around the world try to prepare and build resiliency for these types of scenarios using a vast arsenal of tools, yet they still fall short. Time and time again, they fail to accurately account for the required monetary funds to properly mitigate, and respond to this new risk landscape.
It is hard for government agencies to properly anticipate the necessary funds to combat events that have no prior baseline. Predictive modeling can help governments determine what has happened in the past or what a likely event will cost in the future, yet allocating the required funds to properly mitigate these risks can prove futile and politically dangerous even though it is the prudent thing to do. In the U.S. budgetary process, if funds are not used, then their purpose is often questioned and future budgets may be reduced. The city of Washington D.C. recently suffered this type of unfunded loss by exhausting its $6mm snow removal budget when the city, along with much of the east coast, were hit with a major winter storm that dumped up to 40 inches of snow. This single event doubled the annual budget to $12mm. Even with the added funds, it still took weeks before all streets were plowed and individuals and businesses could return to normal. This took a major toll not only on the city’s budget, but the city’s overall GDP. This economic component is often lost on the general public when looking at these events, especially when taken in isolation.
