An Expressed Preference Determination of College Students' Valuation of Statistical Lives: Methods and Implications

Article excerpt

I wish to thank the USU Office of the Provost and the Department of Economics for providing financial support. I would also like to thank Cris Lewis and Paul Jakus for reading earlier drafts of the manuscript and providing invaluable suggestions. Lastly, I am most grateful to my wife for her unfailing support.

   Government agencies typically apply a general value of statistical
   life (VSL) estimate when performing cost-benefit analysis (CBA).
   However, theory suggests that college students attach a value to
   statistical lives that differs from society's VSL; therefore, CBA
   may lead to inefficient levels of risk reduction among students. A
   contingent valuation survey of 137 students was conducted to
   calculate Utah State University students' VSL; the sample's
   representativeness enables statistical inferencing. Regression
   models indicate that the students possessed an adequate
   understanding of mortality risk reduction. The students' inferred
   VSL of $2.96 million is significantly lower than the $5.4 million
   societal estimate computed by Kochi et al. (2006). This suggests
   that students are required to pay more for risk reduction than they
   are willing.

It is customary and often mandatory for government agencies to perform cost-benefit analysis (CBA) before initiating specific programs. Premature death prevention is a conspicuous benefit stemming from many government actions. For example, the CPSC's regulation of space heaters saves an estimated 63 lives annually, the EPA's management of asbestos saves about 10 lives, and OSHA's guidelines concerning hazard communications save around 200 lives (Morall, 2003). However, each of these programs has accompanying costs. Because mortality risk reduction benefits are subject to the law of decreasing marginal utility, there exists a specific level of supplied risk reduction such that benefits per dollar are maximized. Also, because risk reduction is often a non-rival and nonexclusive good, government intervention is frequently required to augment supply. Economists seek to calculate an efficient supply of mortality risk reduction through the determination of the value of a statistical life, or simply, VSL.

VSL is a statement of how much a given population is willing to pay to reduce the total amount of expected premature deaths by one.

To state that a population's VSL is equal to x is to assert that the population values statistical lives at x. This is different from stating that the value of statistical lives among the population is x; the actual amount may be higher or lower. It is important to note that just as individuals within free market determine the price of goods through sovereignty, consumers determine the value of a statistical life through implicitly or explicitly expressed risk-benefit tradeoffs.

U.S. government agencies typically apply a general VSL estimate when analyzing the costs and benefits of specific programs. However, the costs of government programs are frequently borne by homogenous subpopulations. The VSL of these subpopulations may be significantly higher or lower than the societal VSL. It has been shown that a given subpopulation's VSL is typically lower if the members of the subpopulation are younger--under the age of 30--and have a lower than average income (Viscusi, 1992). This suggests that college students' VSL is lower than society's. However, this is impossible to know without performing statistical tests because other factors in addition to age and income can influence willingness to pay (WTP) for mortality risk reduction.

For this article, an expressed preference contingent valuation study was conducted to calculate the VSL of students at Utah State University. Survey results were gathered from 137 students. Statistical inference is possible because (a) these students are shown to be representative of the general USU student body, and (b) the respondents demonstrate an adequate understanding of mortality risk reduction. …


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