Many federal health, safety, and environmental regulations are primarily designed to reduce the risk of death, illness, or injury from exposure to a particular hazard (e.g., arsenic in drinking water, rollover car crashes, or terrorist attacks on airplanes). The agencies issuing these regulations often place a monetary value on these expected health benefits by determining the number of "statistical lives" that the rules are expected to save, and then multiplying that number by an estimated "value of a statistical life." The term "statistical life" is used to reflect the degree of risk reduction expected in a given population, and does not refer to any individual's life. For example, on January 15, 2010, the Federal Railroad Administration (FRA) within the Department of Transportation (DOT) published a final rule in the Federal Register defining criteria for "positive train control" systems that were required on certain passenger and freight rail lines by the Rail Safety Improvement Act of 2008 ( P.L. 110-432 , 122 Stat. 4854, October 16, 2008). Congress enacted the statutory requirement in the wake of several serious rail accidents involving dozens of fatalities and hundreds of injuries. FRA estimated that the rule would reduce deaths and injuries from this type of accident by more than 50%, valued each "statistical life" expected to be saved by the rule at $6 million, and considered each prevented injury a percentage of the value of a statistical life. The agency ultimately valued the estimated reductions in deaths over the next 20 years at between $175 million and $269 million (in 2009 dollars), and valued the reductions in injuries at between $133 million and $204 million (also in 2009 dollars). Together, these monetized health benefits represented more than 70% of the rule's estimated total benefits. The monetization of reductions in the number of expected fatalities, injuries, and illnesses in the rulemaking process is often controversial, and the process by which federal agencies place monetary values on such benefits is not widely understood. This report summarizes current government-wide requirements for benefit-cost analysis and the monetization of health benefits, and describes agency-specific policies in selected health, safety, and environmental agencies. Also, the report provides examples of final rules published by the selected agencies from 2007 through 2009 that monetized expected health benefits, and describes how those values were used in the economic analyses for the rules. Finally, the report offers some concluding observations. Although a variety of statutes and executive orders require some form of economic analysis during the rulemaking process, the most broadly applicable of those requirements is in Executive Order 12866, which was issued by President Clinton in 1993. The executive order requires covered federal agencies to determine the costs and benefits of all "significant" regulatory actions, and requires more complete benefit-cost analyses for all regulatory actions that are expected to be "economically significant" (e.g., have an annual $100 million impact on the economy). In recent years, an average of about 600 federal rules have been considered "significant" each year, of which about 100 have been considered "economically significant." The executive order also says that agencies are to adopt a regulation only after determining that the benefits of the rule "justify" its costs. With regard to regulatory benefits, Section 6(a)(3)(C) of Executive Order 12866 requires the issuing agency to provide to the Office of Information and Regulatory Affairs (OIRA) within the Office of Management and Budget (OMB) "an assessment, including the underlying analysis, of benefits anticipated from the regulatory action (such as, but not limited to, the promotion of the efficient functioning of the economy and private markets, the enhancement of health and safety, the protection of the natural environment, and the elimination or reduction of discrimination or bias) together with, to the extent feasible, a quantification of those benefits." The executive order requires similar assessments of regulatory costs, and an explanation of why the planned regulatory action is preferable to potential alternatives. The regulatory analysis requirements in Executive Order 12866 are more fully delineated in OMB Circular A-4, which was issued in September 2003. The circular states that it is "designed to assist analysts in the regulatory agencies by defining good regulatory analysis ... and standardizing the way benefits and costs of Federal regulatory actions are measured and reported." Although Circular A-4 states that a "complete regulatory analysis includes a discussion of non-quantified as well as quantified benefits and costs," it also says that a "distinctive feature of [benefit-cost analysis] is that both benefits and costs are expressed in monetary units, which allows you to evaluate different regulatory options with a variety of attributes using a common measure." It goes on to say that agencies "should monetize quantitative estimates whenever possible." Estimates of regulatory health benefits are sometimes derived from risk assessments, which systematically determine whether a particular hazard exists, and if so how much damage or injury can be expected from exposures to that hazard. In addition to its use in benefit-cost analysis, risk assessment can also help agencies identify issues of potential concern (e.g., whether exposure to a given risk agent causes effects such as cancer), and can help them select regulatory options. As noted previously, the number of "statistical lives" expected to be saved reflects the degree of risk reduction expected in a given population, and does not refer to any particular individual. For example, if a regulation is expected to reduce the annual risk of death from a particular hazard by 1 in 100,000 for a population of 100,000, that effect is characterized as representing one "statistical life" extended or "saved" per year. The number of statistical lives saved is a function of the size of the population involved and the size of the risk. For example, if the annual risk of death from the same hazard is reduced by one in 1 million for each of 1 million people, the regulation is also characterized as saving one statistical life per year. Alternatively, if the risk is reduced by 1 in 100,000 for a population of 500,000, the rule is said to save five statistical lives. Circular A-4 indicates that the concept of "opportunity cost" "is the appropriate concept for valuing both benefits and costs," and describes the principle of "willingness-to-pay" as capturing the notion of opportunity cost "by measuring what individuals are willing to forgo to enjoy a particular benefit." The public's willingness-to-pay is often measured using surveys (sometimes referred to as "stated preference" studies) in which respondents are asked how much they would be willing to pay to avoid particular risks or outcomes. For example, if 100,000 people are each willing to pay an average of $50 to reduce a 1 in 100,000 risk of dying from exposure to a particular risk, then the "value of a statistical life" (VSL) for the population relative to that risk is $5 million ($50 times 100,000). An alternative to "willingness-to-pay" is to measure individuals' "willingness-to-accept" a risk, which Circular A-4 says "can also provide a valid measure of opportunity cost." These "revealed preference" studies use data from market transactions or observed behavior to estimate the value of certain risks. One example is wage-risk studies, in which researchers compare workers' earnings in occupations with varying levels of on-the-job risks. Circular A-4 says that revealed preference studies "are sometimes difficult to implement given the complexity of market transactions and the paucity of relevant data." The circular also says that economists tend to view willingness-to-pay as "the most appropriate measure of opportunity costs," and that the willingness-to-pay approach is "the best methodology to use if reductions in fatality risks are monetized." In monetizing health benefits, the circular states that a willingness-to-pay measure is "the conceptually appropriate measure as compared to other alternatives (e.g., cost of illness or lifetime earnings), in part because it attempts to capture pain and suffering and other quality-of-life effects," and also because it "allows you to directly compare your results to the other benefits and costs in your analysis." Circular A-4 draws a clear distinction between monetizing anticipated reductions in the risk of death and placing a value on human life. Some describe the monetized value of small changes in fatality risk as the "value of statistical life" (VSL) or, less precisely, the "value of a life." The latter phrase can be misleading because it suggests erroneously that the monetization exercise tries to place a "value" on individual lives. You should make clear that these terms refer to the measurement of willingness to pay for reductions in only small risks of premature death. They have no application to an identifiable individual or to very large reductions in individual risks. They do not suggest that any individual's life can be expressed in monetary terms. Their sole purpose is to help describe better the likely benefits of a regulatory action. OMB has, in the past, used a particular VSL to assign a monetary value to agencies' quantified (but unmonetized) regulatory health benefits. However, Circular A-4 does not recommend that agencies use a particular VSL in all of their economic analyses. Noting the "considerable body of academic literature" available on the valuation of reductions in premature mortality, the circular simply states that a "substantial majority of the resulting estimates of VSL vary from roughly $1 million to $10 million per statistical life." Circular A-4 permits agencies substantial flexibility in determining how expected reductions in mortality and morbidity are valued, but requires agencies to be transparent with regard to those decisions. The valuation of fatality risk reduction is an evolving area in both results and methodology. Hence, you should utilize valuation methods that you consider appropriate for the regulatory circumstances.