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December 18, 2012

The Welfare Costs of Superstorm Sandy

Jaison R. Abel, Jason Bram, Richard Deitz, and James A. Orr

As most of the New York metropolitan region begins to get back to normal following the devastation caused by superstorm Sandy, researchers and analysts are trying to assess the total “economic cost” of the storm. But what, exactly, is meant by economic cost? Typically, those tallying up the economic cost of a disaster think of two types of costs: loss of capital (property damage and destruction) and loss of economic activity (caused by disruptions). But there is another important type of economic loss that often is not estimated or discussed in policymaking decisions: loss of welfare or deterioration in quality of life. Here we focus on how superstorm Sandy (and other such disasters) can have widespread adverse effects on quality of life, and provide some illustrations of how one can try to put an approximate dollar value on this type of cost.

Tallying the physical damage takes time, but it can be measured fairly accurately. Assessing a shortfall in economic activity, although a bit tricky to estimate, is also fairly straightforward, conceptually. Measuring welfare (or “utility”) is a concept often discussed among economists—which makes it all the more ironic that it’s often overlooked in the aftermath of disasters. Part of the reason economists do not address these costs is that they’re difficult to measure in terms of dollars. So how exactly should we think about welfare in this context? Let’s start with an illustration of something tangible—a family childhood scrapbook. The accounting cost or “market value” of such a memento may be modest, say $10. But the value to its owner would likely be much higher…if it were lost in a flood and its owner would be willing to pay $500 to get it back, then one could consider that to be its value. Its loss would represent $10 in terms of physical damage, but the owner would consider it a loss equivalent to $500 in terms of his or her own economic welfare.

A more germane example, in this context, would pertain to a protracted loss of power: how would one value the welfare loss for the millions of homes across the New York metropolitan region that had no power for roughly a week after the storm? One way would be to ask a random sample of such households approximately how much they would have been willing to pay to avoid the entire power outage. In all likelihood, the average would have been considerably higher than one week’s electric bill. To illustrate how substantial these welfare costs can be, let’s say that there are 3 million households with power outages and that the average household would have paid $500 to restore power immediately; that can be thought of as a welfare loss of $1.5 billion. Perhaps this number is on the high side…but then you have similar issues with loss of water, heat, and communications.

Another common source of welfare loss after major disasters is commuting time. With the New York City subway system completely shut down for two full days after the storm and the PATH commuter trains, New Jersey Transit, and much of the Long Island Railroad out of service for considerably longer, commutes and other routine trips took much longer than normal during the week of the storm. For the many commuters who left home early and returned later than usual, in some cases for many days, the phrase “time is money” may be appropriate. For instance, if these commuters, on average, value their leisure time at $20 per hour—not an unreasonable rate, as it is well below the median wage in the region—then someone spending an extra ten hours commuting over the course of the week could be thought of as being worse off by $200. If there are a million such people, that adds up to a loss of  $200 million. You can then add to this calculation people waiting in long lines for gasoline, searching for grocery stores that are open, and so forth.

But, of course, the most profound quality-of-life losses were sustained by the hundreds of thousands of people in the communities most devastated by Sandy: Red Hook, the Rockaways, Long Beach, Coney Island, coastal Staten Island, Hoboken, Spring Lake, Seaside Heights, and many others. First and foremost, roughly 130 people in the United States lost their lives as a result of Sandy—not as many as the more than 600 fatalities from the 1938 Long Island Express hurricane or the more than 1,800 lives lost in Hurricane Katrina, but still a sizable death toll. It is difficult to even try to put a dollar value on the stress and grief associated with lost loved ones—or with having one’s home destroyed or severely damaged, being displaced from one’s community for an extended period of time, losing almost all of one’s personal property, having one’s children relocated (albeit temporarily) to a different school many miles away, or dealing with looters. But if we were to provide such an estimate, it would reflect a high cost. Yet just as there are some offsets to the economic disruption costs, there will likely be the psychological benefits experienced—also difficult to value—as these communities are rebuilt (perhaps stronger than before), people return home, and life returns to normal.

To conclude, while it is almost impossible to accurately estimate the total welfare or quality-of-life cost attributable to Sandy, it is important to acknowledge that it is significant. Why? Because including these effects in the total “economic cost” of a storm has policy ramifications: going forward, many decisions will be made and steps will be taken—by both governments and private institutions—to mitigate or prevent the effects of future storms such as Sandy. Because intelligent decisions and optimal policies need to be based on a sound cost-benefit analysis, focusing only on lost activity, physical damage, and clean-up expenses understates the true costs of a disaster because it misses substantial welfare costs such as those mentioned above. From a policy standpoint, including quality-of-life considerations in the calculus will tend to tip the scales toward taking more preventative measures.

The views expressed in this post are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.


Jaison R. Abel is a senior economist in the Federal Reserve Bank of New York’s Research and Statistics Group.


Jason Bram is a senior economist in the Research and Statistics Group.


Richard Deitz is an assistant vice president in the Research and Statistics Group.


James A. Orr is an assistant vice president in the Research and Statistics Group.

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