The Human Capital (HK) and Statistical Life Values (VSL) differ sharply in their empirical pricing of a human life and lack a common theoretical background to justify these differences. We first contribute to the theory and measurement of life value by providing a unified framework to formally define and relate the Hicksian willingness to pay (WTP) to avoid changes in death risks, the HK and the VSL. Second, we use this setting to introduce an alternative life value calculated at Gunpoint (GPV), i.e. the WTP to avoid certain, instantaneous death. Third, we associate a flexible human capital model to the common framework to characterize the WTP and the three life valuations in closed-form. Fourth, our structural estimates of these solutions yield mean life values of 8.35 M$ (VSL), 421 K$ (HK) and 447 K$ (GPV). We confirm that the strong curvature of the WTP and the linear projection hypothesis of the VSL explain why the latter is much larger than other values.
of 39 separate estimates of the monetary value of a “statistical life”—a concept used in cost/benefit analyses of regulations—29 (74%) of the estimates were underpowered. For the 10 studies that had adequate power, the estimate of the value of a statistical life was $1.47 million, but the 39 studies collectively gave a mean estimate of $9.5 million. After our hypothetical examples of coin-flipping researchers, this real-world example leads one to suspect that the figure of $9.5 million is likely to be vastly exaggerated.
This paper updates the cost-per-life-saved cutoff, which is a cost-effectiveness threshold for life- saving regulations, whereby regulations costing more per life saved than this threshold level are expected to increase mortality risk on net. Two competing methods of deriving the cutoff exist: a direct approach based on empirical observation and an indirect approach grounded in economic theory. Both methods build from the assumption that changes in income lead to changes in mortality risk. The likely mechanisms driving this relationship are discussed, with support from recent empirical studies. The indirect approach is preferable in that it avoids the problems of endogeneity of health status and income found with the direct approach. The cost-per-life-saved cutoff value at which regulations increase mortality risk is estimated to have a lower bound value of $75.4 million and an upper bound value of $123.2 million, with a midpoint value of $99.3 million. This cutoff value range is compared with cost-effectiveness estimates for a series of recent policies, including several state expansions of the Medicaid public insurance program in the first few years of the 21st century, an early version of the “travel ban” executive order that restricted refugee admissions into the United States, and nine recent air pollution regulations from the Environmental Protection Agency. The paper concludes that the mortality risk test is an important and underutilized tool in the policy analyst’s toolkit, both as an overall test of regulatory efficacy and as an integral component of calculations of net risk effects of policies.
This paper analyzes the test-retest reliability of subjective survival expectations. Using a nationally representative sample from the Netherlands, we compare probabilities reported by the same individuals in two different surveys that were fielded in the same month. We evaluate reliability both at the level of reported probabilities and through a model that relates expectations to socio-demographic variables. Test-retest correlations of survival probabilities are between 0.5 and 0.7, which is similar to subjective well-being (Krueger and Schkade, 2008). Only 20% of probabilities are equal across surveys, but up to 61-77% are consistent once we account for rounding. Models that analyze all probabilities jointly reveal similar associations between covariates and the hazard of death in test and retest datasets. Moreover, expectations are persistent at the level of the individual and this unobserved heterogeneity is strongly correlated across surveys (r ≈ 0.8-0.9). Finally, we use a life-cycle model to map survival expectations into simulated wealth and labor supply. Though wealth accumulation is sensitive to survival expectations, simulated probabilities from a model that corrects for rounding are sufficiently reliable to yield reliable wealth profiles. Taken together this evidence supports the reliability of subjective survival expectations.
an hour of running statistically lengthens life expectancy by seven hours, the researchers report.
The Benefits of Avoiding Cancer (or Dying from Cancer): Evidence from a Four-Country Study by Anna Alberini, Milan Ščasný :: SSRNFebruary 12, 2017
We use stated-preference methods to estimate the cancer Value per Statistical Life (VSL) and Value per Statistical Case (VSCC) from a representative sample of 45-60-year olds in four countries in Europe. We ask respondents to report information about their willingness to pay for health risk reductions that are different from those used in earlier valuation work because they are comprised of two probabilities — that of getting cancer, and that of dying from it (conditional on getting it in the first place). The product of these two probabilities is the unconditional cancer mortality risk. Our hypothetical risk reductions also include two qualitative attributes — quality-of-life impacts and pain. The results show that respondents did appear to have an intuitive grasp of compound probabilities, and took into account each component of the unconditional cancer mortality risk when answering the valuation questions. We estimate the cancer VSL to be between € 1.9 and 5.7 million, depending on whether the (unconditional) mortality risk was reduced by lowering the chance of getting cancer, increasing the chance of surviving cancer, or both. The VSCC is estimated to be up to € 0.550 million euro, and its magnitude depends on the initial (conditional) cancer mortality and on the improvement in survival. We interpret these as “pure” mortality and cancer risk values, stripped of morbidity, pain or quality-of-life effects. The survey responses show that impacts on daily activities and pain have little or no effect on the WTP to reduce the adverse health risks.
Economists discount future benefit and cost flows for a variety of reasons, including time preference, diminishing marginal utility of consumption, opportunity cost of capital, and risk aversion. Many of these rationales for discounting can be explained using the Ramsey equation found in neoclassical growth theory. This paper argues that Ramsey approaches to discounting are problematic for use in regulatory benefit-cost analysis (BCA) because they are inconsistent with certain foundational principles of BCA. A more useful discounting framework is one that is based on the time value of money, where discounting is used as a way to compare investment projects to a baseline alternative investment. A social discount rate (SDR) used in this manner avoids many ethics controversies that arise in Ramsey discounting approaches with respect to giving preferential treatment to the present generation over future generations, but it still recognizes and accounts for the importance of economic growth. An SDR of about 7 percent appears to be reasonable and is consistent with current guidelines from the Office of Management and Budget.