Value of statistical life

VSL is derived based on the monetary sum people are willing to pay for reducing the risk of fatality. An example of a suitable monetary sum is the price premium for a safe car, which together with risk reduction estimates for the safe car is sufficient to calculate a VSL. The WTP for As (the change in the risk to die) leads to the value of statistical life such as ... 

VSL (value of statistical life)/value of prevented fatality (VPF)... 

We have used the marginal compensating variation to illustrate the concept of the value of statistical life. However, as is indicated by equation (5.3), in evaluating marginal changes it holds that dCV = dEV. Thus we can use the equivalent variation measure as well in defining the value of statistical life. This is further shown in the Appendix. 

Thus, the sum across household members of their marginal willingness to pay is set equal to the market price of the health good, in order to arrive at the definition of the value of statistical life, assume a two-states world, and also that the expected utility of individual h can be written as follows ... 

The reader is invited to differentiate (A5.3) with respect to and calculate the associated change in expected utility. Next, adjust income through an amount dEV so as to obtain the same change in expected utility. The amount dEV represents the compensation the individual needs in order to be as well off as with an increase in the survival probability. Apparently, it must be the case that dEV = dCV. The value of statistical life in (A5.5) is thus the same regardless of whether we use the (marginal) CV or the EVmeasure. 

Johansson, P.-O. (1994). Altruism and the value of statistical life empirical implica-tions. Journal of Health Economics, 13,111-18. 

Structural damage may result in societal and economic consequences. The utility node Societal consequences describes the expected number of fatalities and injuries given structural collapse and related compensation costs, which may be based on the so-called societal value of statistical life, Holicky (2009). 

In the case of health effects, other methods than stated or revealed preference methods are often used to estimate the impact of externalities and valuating the human health damages. Both productivity losses and costs for hospital admissions or other hospital-related activities are used to monetize health effects. Of special importance for the valuation of health effects are the metrics Value of a Statistical Life / Value of Prevented Fatality (VSL, VOSL or VPF) and Value of a Life Year Lost (VOLY). 

Statistics. One-way analysis of variance and Bonferroni s post hoc tests were used to compare values of half-life and systemic clearance for ATI and ATF. Differences were considered to be significance when p < 0.05. 

Comparisons between studies and methods are usually carried out in terms of the value per statistical life. It is, however, important to bear in mind that the value per statistical life can be expected to vary with the type of risk (e.g. voluntary versus involuntary), the initial risklevel, the size of the risk change, age and income. 

A number of reviews of the value per statistical life in the literature have been carried out see, for example, Fisher et al. (1989), Jones-Lee (1996), Miller (1990) andViscusi (1992,1993). In the survey by Viscusi (1992,1993), the value per statistical life varies between US 0.6 million and US 16.2 million in the surveyed labour market analyses of wage-risk trade-offs, between US 0.07 million and US 4.0 million in the studies of consumer markets, and between US 0.1 million and US 15.6 million in the CV studies (in December 1990 dollars). Viscusi (1992) noted that for labour market studies of wage-risk trade-offs most of the reasonable estimates of the value of life are clustered in the US 3 to US 7 million range (Viscusi, 1992, p. 73). He also noted that this estimate conforms quite well with the results of the large-scale CV studies, whereas the results of the studies of consumer... 

Miller (1990) in his review identified 65 studies of the value per statistical life, and eliminated 18 of those as unreliable, ffe then adjusted the value of the remaining 47 studies according to, for instance, risk perception. After the adj ustments he found a mean value per statistical life of US 2.2 million, with 2.2 million for the labour market studies of wage-risk trade-offs, and US 2.5 million for the CV studies (in 1988 dollars). The adjustments and elimination of studies made hy Miller were, however, to a large extent arbitrary and the review therefore exaggerated the similarity of results across different methods and studies. 

Indirect methods based on market behaviour probably do not capture altruistic values. The issue of altruism has to a large extent been neglected in empbical studies, with the exceptions of that of Jones-Lee et al. (1985), who found that the value per statistical life increased by about a third if (probably paternalistic or safety-oriented) altruism is included, and those of Viscusi et al. (1988) and Johannesson et al. (1996). The investigation of the role of altrirism in the value of health changes is an important issue for future research. 

When workers are killed, are injured or become ill, there are substantial costs beyond those borne by employers. A variety of approaches can be used to estimate these costs. For example, Viscusi and Aldy (2003) provided estimates of the monetary value of each life lost. OSHA updated these estimates (to account for inflation) to 2010 dollars, yielding a value of 8.7 million for each life lost. Multiplying this value by the 4,547 workplace deaths reported by the Bureau of Labor Statistics for 2010, OSHA estimates the annual cost of known workplace fatalities to be nearly 40 billion. 

In New Zealand, the government not only required positive benefit-cost ratios before a project could proceed, but also mandated that the economic returns must reach a prescribed positive level. When this policy was introduced, safety projects became uncompetitive. The traffic safety specialists made a successful case for a switch from the direct cost method to the willingness-to-pay method for estimating the dollar value of a statistical life. The switch resulted in an approximate doubling of the value of a life and got a lot more safety projects over the line in the benefit-cost competition. Sadly, traffic safety advocates in New Zealand currently bemoan a failure to adequately adjust overtime, the values derived of this paradigm shift of well over a decade ago. ... 

In the first step, a screening process will be applied to separate the major potential hazards these will be addressed in more detail. QRA techniques are used to evaluate the extent of the risk arising from hazards with the potential to cause major accidents, based on the prediction of the likelihood and magnitude of the event. This assessment will be based on engineering judgement and statistics of previous performance. Where necessary, risk reduction measures will be applied until the level of risk is acceptable. This of course is an emotive subject, since it implies placing a value on human life. 

A further three atoms of 110 were observed during the next eight days leading to an average half-life of 170/rs (4-160, —60/rs). [Note that the decay times listed for the above single-atom observations are not identical with the best values of the statistical half-lives for the species mentioned.] Subsequent work also identified a second isotope 110 with ti/2 623/rs. 

N, from 2,000 to 20,000 a value of 10,000 can suffice to demonstrate a system which shows typical features of a life form. It is, however, also small enough to permit the statistical transition from disorder to order. 

Taking a value of 107 for N would mean that in our galaxy (with its perhaps 100 billion stars), there could be several million planets with life forms capable of interstellar communication. However, if these were distributed statistically, the nearest would still be 200 light years away from Earth. One point is important the term probability used in the Drake equation is interpreted in the sense of subjective probability (a term from the nomenclature used by statisticians and probability theorists), as the numerical value of this probability is determined only by the experience of the scientist concerned (Casti, 1989). Casti also provides more information on the Drake factors (apart from the factor fs) in the chapter Where are they then In summary, we can say that the Drake equation is a first attempt to quantify the ETI problem in order to move from the area of science fiction and pure speculation to that of serious scientific debate. 

The analysis in Cremieux et al. (2005) yielded life expectancies for men and for women from 1981 to 1998 as predicted by a model and simulated with drug spending at 1981 levels (Fig. 12.1). Using methodology from Murphy and Topel (2005), life expectancy results from the Canadian pharmaceutical spending study can be evaluated in monetary terms. The value at age a of a statistical life is given by... 

To compute this value requires survivor functions for relevant time periods as well as values for v(f). The survivor functions were computed from male and female life tables available for 1982 and 1997 from Statistics Canada. Economic values for additional life years were computed based on Murphy and Topel (2005) and converted to Canadian dollars using the average per capita ratios of Canadian to U.S. income for 1994-2003. These income-adjusted life year values were then multiplied by 1.267 the purchasing power parity (PPP) rate between Canada and the United States in 2004, expressed in 2004 dollars. 

Viscusi, W. Kip, and Joseph E. Aldy. 2003. The Value of a Statistical Life A Critical Review of Market Estimates throughout the World. Journal of Risk and Uncertainty 27(l) 5-76. 

Presently, some countries including the United States and a few Member States of the European Union use statistical methods to establish withdrawal periods. However, most countries employ a simple method the withdrawal period is set at the time point when residues in all tissues in all the animals have depleted to below the respective MRL values. When one has determined that time point, the estimation of a safety span also has to be considered in order to compensate for uncertainties of the biological variability. The dimensions of a safety span depend on various, not easy to specify, factors determined by the study design, the quality of the data, and the pharmacokinetic properties of the drug. Hence, an overall recommendation on the estimation of the safety span cannot be provided. An approximate guide for the safety span is likely to be a value of 10-30% of the time period when all observations are below the MRL. As an alternative, the safety span might be calculated from the tissue depletion curve as a value of possibly one to three times the half-life. 

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