The 2013-2014 winter has been nothing short of a worse-case scenario for the eastern half of the U.S. In Chicago, temperatures fell below zero an astounding 22 times (the Chicago record for a winter is 25), and let’s not forget the combined 67 inches of snow. In Atlanta, the city literally came to a halt during what became known as “Icepocalypse.” In Philadelphia, we’ve seen a total of 58 inches of snow (third highest on record) including 11 different snow storms dropping one inch or more.
Those three locales give you a pretty good idea of just how wide spread the wrath of winter is this year. While it is difficult to measure the exact impact of the weather on the economy, we can conclude that economic activity will certainly lag in January, February and March. Despite the fact that most economic indices account for seasonal effects, they do not account for outlier years like this one. Weather has been blamed for poor economic reports ranging from job growth, to new housing starts, to manufacturing—but is it justified?
A 2010 study by the American Meteorological Society determined which U.S. states are most sensitive to extreme weather variability as it relates to economic output.
The research concluded that the location with the most sensitive industries had the largest total economic effect. For example, agriculture is the most sensitive on an absolute basis, but the fact that agriculture makes up such a small percentage of most states’ Gross State Product (GSP) means that extreme weather has a small total effect on sensitivity. Conversely, manufacturing, financial services, and real estate have a large relative sensitivity because of their GSP impact. As you can see on the map, the states where these industries have a significant economic impact, translates in higher sensitivity to extreme weather.
The severity of winter in the states colored red and yellow justifies the weather-related hype, while the ones in blue can be ignored for economic purposes. If you include the effects of the Government shutdown, we’ve had four consecutive months of cloudy data that we can’t put into clear context!