The Cost of Overvaluing a Statistical Life

One of President Trump’s main priorities during his first 100 days in office has been scaling back regulations that affect everything from Wall Street investing to fuel efficiency standards. Each regulation in the administration’s crosshairs purports to do something good, by making the environment cleaner or making Americans safer.

But good intentions aside, regulations also limit choice and increase costs by restricting individuals and companies from acting as they otherwise would. Determining what these costs and benefits are in an objective manner is difficult because that determination relies on an imperfect tool called the value of a statistical life, or VSL.

In short, the VSL quantifies how much people would be willing to pay to save a life. If there were an accurate, objective, and one-size-fits-all measure of the VSL, agencies could use that number to conduct an accurate benefit-cost analysis. A recent study, however, casts doubt on whether the VSL should be considered an accurate tool for policymaking.

When deciding whether to issue a rule, agencies usually conduct a cost-benefit analysis. In simple terms, agencies estimate and compare the expected benefits of a proposed regulation to its expected costs. But estimating costs and benefits is trickier than it might sound, especially when it requires putting a value on protecting human life.

We can quantify how much it will cost producers to purchase and install new technologies for their power plants to limit emissions and comply with a given rule. But assigning a dollar value to a human life that would be protected by a proposed rule (or lost if a rule is not enacted) is more difficult. Even once an agency has quantified the number of lives it expects a particular regulation to save (in itself a daunting task), it must then assign a monetary value to those lives. To do so, agencies turn to VSL estimates.

To estimate the VSL, many studies rely on surveys that ask people how much they are willing to pay to reduce their risk of dying. But people are inherently bad at perceiving differences in risk levels. For example, individuals often overestimate the probability of dying in a plane crash or being involved in a terrorist attack but underestimate everyday risks like getting into a car accident. Because estimating the VSL relies on surveying everyday people’s willingness to pay to reduce risk, our inherent psychological biases can skew the VSL.

Another problem with asking people how much they are willing to pay to reduce risk is that people’s responses to a survey don’t necessarily reflect how they would actually spend their money, especially given that individuals value risk reductions more in some contexts than in others. People often say they are willing to pay more to achieve a beneficial policy outcome than they actually are. This is known as hypothetical bias, and it can lead to VSL estimates that are too high.

Even if studies are able to accurately estimate risk preferences in one context, these preferences may not be appropriate for use in other contexts. For example, wage-risk studies may identify how people feel about on-the-job safety for working-age adults, but these preferences may be inappropriate to apply to air quality or road safety, both of which impact seniors and others who are not in the workforce. Research suggests that the VSL estimated via road safety decisions is much lower than that estimated via surveys and wage studies.

The VSL may also be substantially skewed by publication bias. Publication bias can occur because the incentives researchers and editors face mean larger VSL estimates are more likely to be published. Some studies estimate that if publication bias were accounted for, this would reduce the VSL by 70 to 80 percent, suggesting we are likely using a VSL that is far too high.

Naturally, if policymakers use a VSL that is artificially high, it is easier for a regulation to pass the cost-benefit analysis, thus making it more likely for that regulation to be enacted.

As regulations stack up and more rules are put in place restricting choice and innovation, costs must go up. Unfortunately, when regulations have more costs than benefits, one way or another, those costs get passed on to everyday people.

Randy T. Simmons, Ph.D., is a professor of political economy at the Jon M. Huntsman School of Business at Utah State University and the president of Strata, a public policy research center in Logan, Utah. Megan Hansen is a director of policy at Strata.

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