The Value of
Life
W. Kip Viscusi
Abstract
The economic
approach to valuing risks to life focuses on risk-money tradeoffs for very
small risks of death, or the value of statistical life (VSL). These VSL levels
will generally exceed the optimal insurance amounts. A substantial literature
has estimated the wage-fatality risk tradeoffs, implying a median VSL of $7
million for U.S. workers. International evidence often indicates a lower VSL,
which is consistent with the lower income levels in less developed countries.
Preference heterogeneity also generates different tradeoff rates across the
population as people who are more willing to bear risk will exhibit lower
wage-risk tradeoffs.
Issues pertaining to the value of life and
risks to life are among the most sensitive and controversial in economics. Much
of the controversy stems from a misunderstanding of what is meant by this
terminology. There are two principal value-of-life concepts— the amount that is
optimal from the standpoint of insurance, and the value needed for deterrence.
These concepts address quite different questions that are pertinent to
promoting different economic objectives.
The insurance value received the greatest
attention in the literature until the past several decades. The basic principle
for optimal insurance purchases is that it is desirable to continue to transfer
income to the post-accident state until the marginal utility of income in that
state equals the marginal utility of income when healthy. In the case of
property damage, it is desirable to have the same level of utility and marginal
utility of income after the accident as before. In contrast, fatalities and
serious injuries affect one’s utility function, decreasing both the level of
utility and the marginal utility for any given level of income, making a lower
income level after a fatality desirable from an insurance standpoint. Thus, the
value of life and limb from the standpoint of insurance may be relatively
modest.
The second approach to valuing life is the
optimal deterrence amount. What value for a fatality sets the appropriate
incentives for those avoiding the accident? In the case of financial losses,
the optimal insurance amount, the optimal deterrence amount,
and the ‘make whole’ amount are identical;
however, for severe health outcomes such as
fatalities, the optimal deterrence amount will
exceed the optimal level of compensation.
The economic
measure for the optimal deterrence amount is the risk-money tradeoff for very
small risks of death. Since the concern is with small probabilities, not the
certainty of death, these values are referred to as the value of statistical
life (VSL). Economic estimates of the VSL amounts have included evidence from
market decisions that reveal the implicit values reflected in behavior as well
as the use of survey approaches to elicit these money-risk tradeoffs directly.
Government regulators in turn have used these VSL estimates to value the
benefits associated with risk reduction policies. Because of the central role
of VSL estimates in the economics literature, those analyses will be the focus
here rather than income replacement for accident victims.
Valuing Risks to Life
Although economics has devoted substantial
attention to issues generally termed the ‘value of life’, this designation is
in many respects a misnomer. What is at issue is usually not the value of life
itself but rather the value of small risks to life. As Schelling (1968)
observed, the key question is how much are people willing to pay to prevent a
small risk of death? For small changes in risk, this amount will be
approximately the same as the amount of money that they should be compensated
to incur such a small risk. This risk-money tradeoff provides an appropriate
measure of deterrence in that it indicates the individual’s private valuation
of small changes in the risk. It thus serves as a measure of the deterrence
amount for the value to the individual at risk of preventing accidents and as a
reference point for the amount the government should spend to prevent
small
statistical risks. Because the concern is with statistical lives, not
identified lives,
analyses of
government regulations now use these VSL levels to monetize risk reduction
benefits.
Suppose that the amount people are willing to
pay to eliminate a risk of death of 1/10,000 is $700. This amount can be
converted into a value of statistical life estimate in one of two ways. First,
consider a group of ten thousand individuals facing that risk level. If each of
them were willing to contribute $700 to eliminate the risk, then one could
raise a total amount to prevent the statistical death equal to ten thousand
people multiplied by $700 per person, or $7 million. An alternative approach to
conceptualizing the risk is to think of the amount that is being paid per unit
risk. If we divide the willingness to pay amount of $700 by the risk
probability of one in ten thousand, then one obtains the value per unit risk.
The value per statistical life is $7 million using this approach as well.
Posing hypothetical interview questions to
ascertain the willingness to pay amount has been a frequent survey technique in
the literature on the value of life. Such studies are often classified as contingent valuation surveys or stated preference
surveys, in that they seek information regarding
respondents’ decisions given hypothetical scenarios (see Jones-Lee 1989 and
Viscusi 1992). Survey evidence is most useful in addressing issues that cannot
be assessed using market data. How, for example, do people value death from
cancer compared with acute accidental fatalities? Would people be interested in
purchasing pain and suffering compensation, and does such an interest vary with
the nature of the accident? Potentially, survey methods can yield insights into
these issues.
Evidence from
actual decisions that people make is potentially more informative than
tradeoffs based on hypothetical situations if suitable market data exists.
Actual decision-makers are either paying money to reduce a risk or receiving
actual compensation to face a risk, which may be a quite different enterprise
than dealing with hypothetical interview money. In addition, the risks to them
are real so that they do not have to engage in the thought experiment of
imagining that they face a risk. It is also important, however, that
individuals accurately perceive the risks they face. Surveys can present
respondents with information that is accurate. Biased risk perceptions may bias
estimates of the money-risk tradeoff in the market. Random errors in
perceptions will bias estimates of the tradeoff downward. The reason for this result
can be traced to the standard errors-in-variables problem. A regression of the
wage rate on the risk level, which is measured with error, will generate a risk
variable coefficient that will be biased downward if the error is random. The
estimated wage-risk tradeoff will consequently understate its true value.
Empirical Evidence on the Value of Statistical
Life
A large literature has documented significant
tradeoffs between income received and fatality risks. Most of these studies
have examined wage-risk tradeoffs but many studies have focused on product and
housing risks as well. The wage-risk studies have utilized data from the United
States as well as many other countries throughout the world. The primary
implication of these results is that estimates of the value of life in the U.S.
are clustered in the $4 million to $10 million range, with an average value of
life in the vicinity of $7 million.
Since
the time of Adam Smith (1776), economists have observed that workers
will
require a ‘compensating differential’ to work on jobs that pose extra risk.
These
wage premiums in turn can be used to assess
risk-money tradeoffs and the value of life.
The underlying methodology used for this
analysis derives from the hedonic price and
wage literature, which focuses on ‘hedonic’ or
‘quality-adjusted’ prices and wages.
Rosen (1986) and Smith (1979), among others,
review this methodology.
To see how the hedonic model works, let us
begin with the supply side of the
market. The worker’s risk decision is to choose
the job with the fatality risk p that
provides the highest level of expected utility
(EU). The worker faces a market offer
curve w(p) that is the outer envelope of the
individual firms’ market offer curves. Let
there be two states of the world: good health with
utility U(w) and death with utility
V(w), where this term is the worker’s bequest
function. The utility function has the
property that good health is preferable to ill
health, and workers are risk-averse or risk-
neutral, or U(w) > V(w); U', V' > 0; and
U'', V'' < 0. The job choice is to
MAX EU = (l - p )u(w (p)) + pV(w (p)), p
leading to the
result
dw = U (w) - V (w)
dp (1 - p)U(w) + pV'(w)
The wage-risk tradeoff dw/dp based on the
worker’s choice of a wage-risk combination for a job is the value of statistical
life (VSL), which equals the difference in utility between the two health
states divided by the expected marginal utility of consumption.
What tradeoff rate dw/dp the worker will select
will depend not only on worker preferences but also on the shape of the market
offer curve. The best available market
opportunities will be those that offer the
highest wage for any given level of risk, or the outer envelope of the offer
curves for the individual firms. Each individual firm will offer a wage that is
a decreasing function of the level of safety. The cost function for producing
safety increases with the level of safety, so the wage decline associated with
incremental improvements in safety must be increasingly great to keep the firm
on its isoprofit curve.
Figure 1 illustrates the nature of the hedonic
labor market equilibrium. The curves OCi and OC2 represent two
possible market offer curves from firms with risky jobs. As the risk level is
reduced, firms will offer lower wages. EU1 and EU2 are expected utility loci of two workers, each of whom has
selected their optimal job risk from available market opportunities. The curve
w(p) represents the locus of market equilibria, which consists of the points at
which worker indifference curves are tangent to the market offers. Thus, the
empirical estimation of the hedonic labor market equilibrium focuses on the
joint influence of demand and supply.
The tradeoffs reflected in market equilibria do
not represent a schedule of individual VSL tradeoff values at different risks,
but rather different VSLs for different workers. Worker 1 chooses risk p1
with associated wage w(p1), and worker 2 chooses risk p2
for wage w(p2). However, worker 1 would not accept risk p2
for w(p2) even when that is the point on the hedonic equilibrium
curve. Rather, worker 1 will require wage w1(p2) > w2(p2)
to accept this risk.
The canonical hedonic wage equation is
ln wi = a + Xip + y 1pi + y2qi + Y3WCi + ei,
where wi is worker i’s wage, Xi
is a vector of personal characteristics and job
characteristics, p; is the worker’s fatality
risk, qi is the nonfatal injury and illness risk, and WC; is a
measure of the worker’s compensation benefits. Not all labor market studies of
VSL include the qi and WC; terms. Moreover, there are
some differences in the form of the workers’ compensation benefit term that is
included. The most common is the expected workers’ compensation replacement
rate, which is the product of the injury risk and the benefit level divided by
the wage rate. These differences in the empirical specification account for
some of the differences across studies in the estimated VSL.
As a practical matter, there are many
systematic differences that have becomes apparent in these studies. Workers at
very high risk jobs tend to have lower values of life on average since they
have self-selected themselves into the very risky occupation. Through their job
choices these individuals have revealed their greater willingness to endanger
their lives. Workers at lower risk jobs typically have greater reluctance to
risk their lives, which accounts for their selection into these safer pursuits.
Such differences are apparent in practice, as the estimated values of life for
workers in the average risk jobs tend to be several times greater than those for
workers in very risky jobs.
Other differences correlated with worker
affluence are also evident. Health status is a normal economic good, and
individuals’ willingness to pay to preserve their health increases with income.
Blue-collar workers, for example, have a lower value of life than do
white-collar workers. In addition, there is a positive income elasticity of the
estimated values of risks to life and health. Based on a sample of 50 wage-risk
studies from ten countries, Viscusi and Aldy (2003) estimate that VSL has an
income elasticity of 0.5 to 0.6.
These differences by income level in the VSL
amounts are also borne out in the international evidence on wage-risk
tradeoffs, such as the study of Australia and Japan by Kniesner and Leeth
(1991). Table 1 summarizes representative VSL studies from throughout the
world. More affluent countries such as Japan and Canada tend to have higher
revealed VSL levels than countries such as South Korea, India, and Taiwan. The
major international anomaly is the United Kingdom, for which labor market
estimates have been very unstable across studies and sometimes quite high.
Deficiencies of the U.K. fatality risk data or correlation of these values with
other unobservables may account for this pattern. Because of these limitations,
the benefit assessments for risk reductions in the U.K. are based on stated
preference values rather than labor market values, which is the approach taken
by U.S. regulatory agencies.
Because of individual heterogeneity in
preferences and resources, it is not surprising that estimated values of life
often differ considerably across empirical studies. These differences are not a
sign that such studies are necessarily in error. These samples often consist of
workers with quite different risk levels and who are situated differently.
International comparisons, for example, consistently reveal differences across
countries, not only because of the aforementioned aspects of heterogeneity, but
because of the differences in the social insurance and workers’ compensation
arrangements that may be present in these countries.
The role of heterogeneity is evidenced in
estimates for the implicit value for nonfatal job injuries for different
worker groups. This analysis follows the same general methodological approach
as does the literature on the implicit value of life. The difference is that
the focus is on non-fatal job risks rather than fatalities. On average,
workers value
non-fatal loss injuries on the job at values ranging from $20,000 to $70,000
per expected job injury. Thus, for example, a worker at the high end of this
range would require $2,000 to face a one chance in 25 of being injured that
year.
The estimates of the implicit values of
injuries for other labour market groups who have different attitudes towards
risk vary substantially from this amount. Interestingly, women often work at
hazardous jobs and appear to have wage-risk tradeoffs similar to those of men.
Other personal characteristics generate more evidence of heterogeneity in
preferences. Cigarette smokers and people who don’t use seat belts in their
automobiles work on risky jobs for less per expected injury than do people who
don’t smoke and who use seat belts in their automobiles. What is noteworthy is
that these results are not hypothetical willingness-to-pay values that these
groups have expressed with respect to risks. Rather, they represent actual
differences in compensation based on observed patterns of decisions in the
marketplace. Markets work as expected in that they match workers to the jobs
that are most appropriate for their preferences. This is a constructive role of
market sorting that promotes a more efficient match-up than if, for example,
all individuals were constrained to have the same job riskiness.
Preference heterogeneity has additional
implications as well. Recall from Figure 1 that workers may settle along
different points of the available market opportunities. However, if workers
face the same opportunities locus, then the worker choosing the higher risk p2
must always be paid a wage w(p2) > w(p1) if p2
> p1. Interestingly, that pattern does not always hold. As shown
by Viscusi and Hersch (2001), smokers choose jobs that are riskier than
nonsmokers’ jobs but offer less additional wage compensation for incurring the
risks.. Smokers and nonsmokers face different market offer curves and,
most
important, these offer curves provide for a flatter wage-risk gradient for
smokers. There may be an efficiency-based rationale for these differences, as
smokers are more prone to job accidents, so that there safety-related
productivity is less.
Studies of the
money-risk tradeoffs are not restricted to the labor market. There have been a
number of efforts to assess price-risk tradeoffs for a variety of commodities.
The contexts analysed by economists include the choice of highway speed, seat
belt use, installation of smoke detectors, property values in polluted areas,
and prices of automobiles. The most reliable of these studies outside the labor
market are those pertaining to automobile prices in that they follow the same
kind of approach as is used in the wage-risk literature. In particular, the
analysts obtain price information on a wide variety of automobile models. Using
regression analysis, they assess the incremental contribution of the safety
characteristics per se to the product price, controlling for other product
attributes. The results of these studies suggest a value of life around $5
million.
The Duration and
Quality of Life
The value-of-life terminology is misleading to
the extent that risk reduction efforts do not confer immortality, but simply
extend life. Because of that, the major concern should not be with the value of
life but with the value of extending life for different periods. In the case of
preventing the risk of a young person, the increase in life expectancy that
will be generated will exceed that for preventing a risk of death to older
people. Some kind of age adjustment may be appropriate. The quantity of life
matters, but which years of life matter most? Is a year of life at age 45 more
valuable than a year of life at age five or age 70? How do various health
impairments correlated with age
affect the value one should attach to such
years of life, and should the fact that very young children have not yet received
the value of the education and rearing by their parents matter? The total
‘human capital’, which is the set of personal attributes such as education and
training that affect one’s income, will be greater for older children who are
further along in their development. Resolving such questions remains highly
problematic.
Considerable attention has been devoted to
economic analysis of age effects, including studies by Shepard and Zeckhauser
(1984) and Johansson (2002). If capital markets were perfect, then VSL would
steadily decline with age, reflecting the shortening of life expectancy. If,
however, there are capital market imperfections, then VSL will display an
inverted U-shaped relationship with age. A similar pattern is exhibited
empirically by lifetime consumption patterns, which some theoretical models
have linked to VSL levels over the life cycle. Although empirical estimates of
the age effects are still being refined, the available evidence from survey
data and market-based studies suggests that there is an inverted-U-shaped
relation. The main empirical controversies concern the tails of the age
distribution. To what extent is there a flattening of the VSL-age relation for
the very old age groups, and how should VSL levels be assigned to children?
The quality of the life of the years saved
clearly matters as well. Life years in deteriorating health may be less
valuable to the individual than years in good health.
Some analysts have suggested that the measure
should focus on quality-adjusted life years. Making these quality adjustments
has yet to receive widespread empirical implementation and are often
controversial. There may be quite legitimate fears of government efforts to
target expenditures by denying health care to those whose life
quality is deemed to be
low. People often adapt to changes in health status so that external observers
may overstate the decline in wellbeing that occurs with serious illnesses.
Conclusion
Economic estimates
of the tradeoffs people make between risk and either prices or wages serve a
variety of functions. First, they provide evidence on how people make decisions
involving risk in labor market and product market contexts. The fact that there
are probabilistic health effects does not imply that markets cease to function.
Second, these estimates have proven useful in providing a reference point for
how the government should value the benefits associated with regulations and
other policies that reduce risk. Third, the existence of these estimates and
economists’ continuing efforts to refine the values has served to highlight
many of the fundamental ethical issues involved, such as how society should
value reducing risks to people in different age groups.
W. Kip Viscusi
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