Valuing Health and Longevity in Regulatory Analysis: Current Issues and Challenges

Economic valuation of health risks plays an important role in informing decisions about environmental, health and safety regulations, indicating the extent to which those affected by a policy or program would agree to exchange income for the benefits it provides. For mortality risks, this willingness to pay is typically expressed as the “Value per Statistical Life” or VSL. The VSL is not the value of a particular individual‟s life. Instead, it measures the rate at which individuals are willing to substitute income for small reductions in their own mortality risks within a defined time period. Currently, US agencies rely on similar research but apply varying VSL estimates, raising concerns related to both the standardization and the differentiation of their values. More standardization seems desirable if agencies continue to follow similar approaches. However, the differences in the risks and populations addressed across agencies suggests that greater differentiation in their VSL estimates is desirable, given that preferences for exchanging income for risk reductions vary depending on these characteristics. The approaches used to value nonfatal illnesses and injuries are more diverse, largely because willingness to pay estimates are lacking for many outcomes of concern. Analysts often use estimates of monetized qualityadjusted life years or averted costs as rough proxies. While more willingness to pay research is needed for nonfatal risks, in the interim the methods used to develop these proxy measures could be improved based on recent research and expert panel recommendations. Jerusalem Papers in Regulation & Governance 2 © Lisa A. Robinson & James K. Hammitt W o rk in g P a p e r N o . 4 | M a y 2 0 1 0 Valuing Health and Longevity in Regulatory Analysis: Current Issues and Challenges Economic valuation of health risks plays a major role in informing decisions about environmental, health and safety regulations, especially as governments around the world increasingly require assessment of regulatory impacts. Regulatory analysis in some form has been mandated in the United States for over 30 years (OMB 1997), and is gradually being implemented in the OECD member countries (OECD 2009). For regulations designed to reduce the risk of illness, injury or premature mortality, counting the number of cases averted is an important initial step in understanding the impacts of alternative policies. Such counts do not convey the relative severity of each outcome, however, nor can they be meaningfully aggregated across different types of effects. Taking the next step of valuing health outcomes in monetary terms provides additional useful insights. Valuation is particularly informative when it addresses trade-offs that are similar to those involved in regulatory decisions. Such decisions require choosing whether to devote resources to achieving health risk reductions, or to allow individuals, firms, or government agencies to use these resources to provide other desirable goods and services. When based on the affected individuals‟ willingness to pay (WTP) for risk reductions, monetary valuation indicates their preferences for trading income (or wealth) for health improvements. These values can be used to determine whether the benefits of alternative regulatory actions are likely to be commensurate with their costs, and also to identify which action, if any, is most likely to maximize the net benefits to society. In combination with other considerations – such as whether the impacts are distributed equitably, and the implications of nonquantifiable effects and other uncertainties – these findings help support sound decisions. Individual WTP for risk reductions is likely to differ from the medical costs or productivity losses associated with incurred cases of illness, injury or premature Jerusalem Papers in Regulation & Governance 3 © Lisa A. Robinson & James K. Hammitt W o rk in g P a p e r N o . 4 | M a y 2 0 1 0 death. The cost of treating a health condition is not the same as the value of reducing its risk of occurrence. For example, treatment does not necessarily return the individual to his or her original health state. WTP often exceeds medical costs and productivity losses by a significant amount because it reflects the value of averting pain and suffering and other quality of life impacts. Because health risk reductions are not directly bought and sold in the marketplace, economists generally use data on related marketed goods or observed behavior (“revealed preferences”) or data from survey research (“stated preferences”) to estimate their value. For example, risk is one of many attributes of different housing locations, job choices, and motor vehicle options. Economists often study related decisions, using statistical methods to separate the value of risk differences from the value of other attributes. Alternatively, they may develop a survey that describes the risk of concern and asks respondents to indicate their WTP for reducing it. Approaches for valuing health risks in regulatory analysis are well established and widely used. Typically, premature mortality and nonfatal illnesses or injuries are valued separately, because only a few empirical studies integrate consideration of both types of effects. Thus this article first summarizes the valuation of mortality risks, then discusses nonfatal risks. It focuses primarily on US practices, describing the approaches used as well as key challenges. Valuing mortality risks As introduced above, health risk reductions are generally valued by estimating individuals‟ willingness to exchange income for the risk change, based on revealed or stated preference studies. For mortality, this WTP is typically expressed as the “Value per Statistical Life” or VSL. 1 1 Both WTP and willingness to accept (WTA) compensation are consistent with the framework for benefit-cost analysis. However, WTA is used less Jerusalem Papers in Regulation & Governance 4 © Lisa A. Robinson & James K. Hammitt W o rk in g P a p e r N o . 4 | M a y 2 0 1 0