JOINT CENTE R

AEI-BROOKINGS JOINT CENTER FOR REGULATORY STUDIES

The Value of a Statistical Life: 

A Critical Review of Market Estimates throughout the World

W. Kip Viscusi and Joseph E. Aldy

Related Publication 03-2

January 2003

Mr. Viscusi is John F. Cogan Jr. Professor of Law and Economics at Harvard Law School. He 

can be contacted by phone at (617) 496-0019 or by email at: kip@law.harvard.edu. Mr. Viscusi’s 

research is supported by the Sheldon Seevak Research Fund; the Harvard Olin Center for Law, 

Business, and Economics; and the Environmental Protection Agency. Mr. Aldy is a Researcher 

at the Department of Economics, Harvard University. Mr. Aldy’s research is supported by the 

Environmental Protection Agency STAR Fellowship program. The authors thank James Hammitt, 

Randall Lutter, and Robert Smith for helpful comments on an earlier draft of this paper. 
Executive Summary 

A substantial literature over the past thirty years has evaluated tradeoffs between 

money and fatality risks. These values in turn serve as estimates of the value of a 

statistical life. This article reviews more than 60 studies of mortality risk premiums from 

ten countries and approximately 40 studies that present estimates of injury risk premiums. 

This critical review examines a variety of econometric issues, the role of unionization in 

risk premiums, and the effects of age on the value of a statistical life. Our meta-analysis 

indicates an income elasticity of the value of a statistical life from about 0.5 to 0.6. The 

paper also presents a detailed discussion of policy applications of these value of a 

statistical life estimates and related issues, including risk-risk analysis. 

 
1

The Value of a Statistical Life: 

A Critical Review of Market Estimates throughout the World 

W. Kip Viscusi and Joseph E. Aldy

Introduction 

 Individuals make decisions everyday that reflect how they value health and 

mortality risks, such as driving an automobile, smoking a cigarette, and eating a mediumrare hamburger. Many of these choices involve market decisions, such as the purchase of 

a hazardous product or working on a risky job. Because increases in health risks are 

undesirable, there must be some other aspect of the activity that makes it attractive. 

Using evidence on market choices that involve implicit tradeoffs between risk and 

money, economists have developed estimates of the value of a statistical life (VSL). This 

article provides a comprehensive review and evaluation of the dozens of such studies 

throughout the world that have been based on market decisions.1

These VSL estimates in turn provide governments with a reference point for 

assessing the benefits of risk reduction efforts. The long history of government risk 

policies ranges from the draining of swamps near ancient Rome to suppress malaria to the 

limits on air pollution in developed countries over the past 30 years (McNeill 1976, 

OECD 2001). All such policy choices ultimately involve a balancing of additional risk 

reduction and incremental costs. 

The proper value of the risk reduction benefits for government policy is society’s 

willingness to pay for the benefits. In the case of mortality risk reduction, the benefit is

 

1

 Past reviews of this literature include Smith (1979), Miller (1990), and Viscusi (1992, 1993, 2000). 

Several researchers have conducted meta-analyses of this literature, including Liu, Hammitt, and Liu 

(1997), Miller (2000), Bowland and Beghin (2001), and Mrozek and Taylor (2002). See Hammitt (2002) 

and Krupnick (2002) for commentaries on the Mrozek and Taylor paper. 
2

the value of the reduced probability of death that is experienced by the affected 

population, not the value of the lives that have been saved ex post. The economic 

literature has focused on willingness-to-pay (willingness-to-accept) measures of mortality 

risk since Schelling’s (1968) discussion of the economics of life saving. 

Most of this literature has concentrated on valuing mortality risk by estimating 

compensating differentials for on-the-job risk exposure in labor markets. While the early 

studies assessed such compensating differentials in the United States, much of the more 

recent work has attempted to estimate risk-money tradeoffs for other developed and some 

developing countries. In addition, economists have also investigated price-risk (pricesafety) tradeoffs in product markets, such as for automobiles and fire alarms. 

Use of the economic research on the value of mortality and injury risks in 

government policy evaluation has been a key benefit component of policy evaluations for 

a wide range of health, safety, and environmental policies. The policy use of risk 

valuations, however, has raised new questions about the appropriateness of these 

applications. How should policymakers reconcile the broad range of VSL estimates in 

the literature? Should the value of a statistical life vary by income? Should the VSL 

vary by the age distribution of the affected population? What other factors may influence 

the transfer of mortality risk valuation estimates from journal articles to policy evaluation 

in different contexts? 

We begin our assessment of this literature with an overview of the hedonic wage 

methodology in Section 1. This approach motivates the discussion of the data and 

econometric issues associated with estimating a VSL. Although there continue to be 

controversies regarding how best to isolate statistically the risk-money tradeoffs, the 
3

methodologies used in the various studies typically follow a common strategy of 

estimating the locus of market equilibria regarding money-risk tradeoffs rather than 

isolating either market supply curves or market demand curves. 

Section 2 examines the extensive literature based on estimates using U.S. labor 

market data, which typically show a VSL in the range of $4 million to $9 million. These 

values are similar to those generated by U.S. product market and housing market studies, 

which are reviewed in Section 3. A parallel literature reviewed in Section 4 examines the 

implicit value of the risk of nonfatal injuries. These nonfatal risks are of interest in their 

own right and as a control for hazards other than mortality risks that could influence the 

VSL estimates. 

Researchers subsequently have extended such analyses to other countries. 

Section 5 indicates that notwithstanding the quite different labor market conditions 

throughout the world, the general order of magnitude of these foreign VSL estimates 

tends to be similar to that in the United States. International estimates tend to be a bit 

lower than in the United States, as one would expect given the positive income elasticity 

with respect to the value of risks to one’s life. 

A potentially fundamental concern with respect to use of VSL estimates in 

different contexts is how these values vary with income. While the income elasticity 

should be positive on theoretical grounds, extrapolating these values across different 

contexts requires an empirical estimate of this elasticity. Our meta-analyses of VSL 

estimates throughout the world in Section 6 imply point estimates of the income elasticity 

in the range of 0.50 to 0.60. The meta-analysis also provides a characterization of the 

uncertainty around the measures of central tendency for the value of a statistical life, i.e., 
4

95 percent confidence intervals for the predicted VSLs. Heterogeneity in VSL estimates 

based on union status (Section 7) and age (Section 8) indicate that the VSL not only 

varies by income but also across these important labor market dimensions. The existence 

of such heterogeneity provides a cautionary note for policy. While policymakers have 

relied on VSL estimates to an increasing degree in their benefit assessments, as Section 9 

indicates, matching these values to the pertinent population at risk is often problematic, 

particularly for people at the extreme ends of the age distribution. 

1. Estimating the value of a statistical life from labor markets 

1.1 The hedonic wage methodology 

More than two centuries ago, Adam Smith (1776) noted in The Wealth of Nations

that: “The wages of labour vary with the ease or hardship, the cleanliness or dirtiness, the 

honourableness or dishonourableness of the employment” (p. 112). Finding empirical 

evidence of such compensating differentials, however, has been problematic. Because of 

the positive income elasticity of the demand for safety, the most attractive jobs in society 

tend to be the highest paid. To disentangle the wage-risk tradeoff from the other factors 

that affect wages, economists have relied on statistical models that control both for 

differences in worker productivity as well as different quality components of the job. 

The primary approach has been hedonic wage and hedonic price models that examine the 

equilibrium risk choices and either the wage levels or price levels associated with these 

choices.2

 Market outcomes reflect the joint influence of labor demand and labor supply, 

 

2

 For more extensive discussion of hedonic analysis, see Griliches (1971), Rosen (1974, 1986), Thaler and 

Rosen (1975), Smith (1979), and Viscusi (1979). 
5

but hedonic models do not examine the underlying economic structure that gives rise to 

these outcomes. For concreteness, we focus on the hedonic wage case. 

The firm’s demand for labor decreases with the total cost of employing a worker. 

The cost of a worker may include the worker’s wage; training; benefits such as health 

insurance, vacation, child care; and the costs of providing a safe working environment. 

Because worker costs increase with the level of safety, for any given level of profits the 

firm must pay workers less as the safety level rises. Figure 1 depicts two firms with 

wage-risk offer curves (isoprofit curves) with wage as an increasing function of risk, OC1

for firm 1 and OC2 for firm 2. For any given level of risk, workers prefer the wage-risk 

combination from the market offer curve with the highest wage level. The outer 

envelope of these offer curves is the market opportunities locus w(p). 

The worker’s supply of labor is in part a function of the worker’s preferences over 

wages and risk. The labor supply is best characterized subject to several mild restrictions 

on preferences. Consider a von Neumann-Morgenstern expected utility model with statedependent utility functions.3

 Let U(w) represent the utility of a healthy worker at wage w 

and let V(w) represent the utility of an injured worker at wage w. Typically, workers’ 

compensation after an injury is a function of the worker’s wage. We assume that the 

relationship between workers’ compensation and the wage is subsumed into the 

functional form of V(w). Further, assume that workers prefer to be healthy than injured 

[U(w) > V(w)] and that the marginal utility of income is positive [U'(w) > 0, V'(w) > 0].4

Workers choose from potential wage-risk combinations along some market 

opportunities locus w(p) to maximize expected utility. In Figure 1, the tangency between 

 

3

 For a discussion of irrational behavior in the presence of mortality risk inconsistent with expected utility 

theory, refer to Viscusi 1998. 

4

 Individual risk neutrality or risk aversion, i.e., U'', V'' ≤ 0 is required to ensure a global maximum. 
6

the constant expected utility locus EU1 and firm 1’s offer curve OC1 represents worker 

1’s optimal job risk choice. Likewise, worker 2 maximizes expected utility at the 

tangency between EU2 and OC2. All wage-risk combinations associated with a given 

worker’s constant expected utility locus must satisfy 

Z = (1 − p)U(w) + pV(w) . 

The wage-risk tradeoff along this curve is given by 

0

(1 ) '( ) '( )

( ) ( ) > − +

− = − = p U w pV w

U w V w

Z

Z

dp

dw

w

p

, 

so that the required wage rate is increasing in the risk level. The wage-risk tradeoff 

consequently equals the difference in the utility levels in the two states divided by the 

expected marginal utility of income. 

 Actual labor market decisions by workers can be depicted by the wage-risk 

combinations at the tangencies of the offer curves and expected utility loci at points (p1, 

w1) and (p2, w2). All that is observable using market data are these points of tangency. 

Expanding beyond our two worker example, observations of a large set of workers can 

show the locus of these workers’ wage-risk tradeoffs, depicted by the curve w(p) in 

Figure 1. Hedonic wage analyses trace out points on this w(p) curve that workers find 

acceptable. 

 The observed labor market decisions (pi, wi) reflect the joint influence of supply 

and demand on the market equilibrium. The estimated tradeoff between wage and risk, 

∂w/∂p, is a local measure of the wage-risk tradeoff for marginal changes in risk. This 

estimated slope corresponds to both the worker’s marginal willingness to accept risk and 

the worker’s marginal willingness to pay for more safety and the firm’s marginal cost of 

more safety as well as the firm’s marginal cost reduction from an incremental increase in 
7

risk. For the worker and firm associated with a given labor market decision (pi, wi), 

∂wi/∂pi reflects both the marginal supply price and the marginal demand price of risk. 

Econometric models that estimate a linear w(p) curve are estimating an average tradeoff 

rate across different levels of risk. 

 The estimated wage-risk tradeoff curve w(p) does not imply how a particular 

worker must be compensated for non-marginal changes in risk. Consider workers 1 and 

2 in Figure 1. Worker 2 has revealed a willingness to accept risk p2 at wage w2(p2) along 

EU2. A change in the risk exposure to worker 1 from p1 to p2 would require a higher 

wage compensation to keep worker w1 on the expected utility locus (EU1), implying that 

w1(p2) > w2(p2) (or alternatively, that ∂w1/∂p2 > ∂w2/∂p2). With large changes in risk, a 

worker’s wage-risk tradeoff will not be the same because the relevant tradeoff must be 

made along the worker’s expected utility locus, not the estimated market wage-risk 

tradeoff. 

1.2 Econometrics and data issues in hedonic labor market analysis 

Most researchers estimate the wage-risk relationship in labor markets by 

specifying a wage equation along the lines of the following: 

wi Hi Xi pi qi qiWCi piHi i = α + ′β + ′β + γ + γ + γ + ′β + ε 1 2 1 2 3 3

where wi is the worker i’s wage rate, α is a constant term, H is a vector of personal 

characteristic variables for worker i, X is a vector of job characteristic variables for 

worker i, pi is the fatality risk associated with worker i’s job, qi is the nonfatal injury risk 

associated with worker i’s job, WCi is the workers’ compensation benefits payable for a 

job injury suffered by worker i, and εi is the random error reflecting unmeasured factors 
8

influencing worker i’s wage rate. The terms α, β1, β2, β3, γ1, γ2, and γ3 represent 

parameters estimated through regression analysis. 

The personal characteristic variables represented by Hi often include a variety of 

human capital measures, such as education and job experience, as well as other individual 

measures, such as age and union status. The job characteristic variables represented by X 

often include indicators for blue-collar jobs, white-collar jobs, management positions, the 

worker’s industry, and measures of physical exertion associated with the job. These two 

sets of variables reflect both workers’ preferences over jobs as well as firms’ offer curves 

for labor. Some studies interact personal characteristics Hi with the fatality risk pi to 

capture how the returns to risk may vary with these characteristics, such as age and union 

status. 

1.2.1 Risk data. An ideal measure of on-the-job fatality and injury risk would reflect 

both the worker’s perception of such risk and the firm’s perception of the risk. Because 

the market opportunity locus reflects both workers’ preferences over income and risk and 

firms’ preferences over costs and safety, information on both sets of beliefs would be 

necessary to appropriately characterize the risk premium. However, very few studies 

have compiled workers’ subjective preferences regarding risks (Viscusi 1979, Viscusi 

and O’Connor 1984, Gerking et al. 1988, and Liu and Hammitt 1999) and there is no 

available research on firms’ risk perceptions. If individuals’ and firms’ subjective risk 

perceptions closely reflect objective measures of fatality risk, then such objective risk 

data could be used instead as a proxy for unobserved subjective risk data.5

 The standard 

 

5

 Gaba and Viscusi (1998) compared qualitative and quantitative subjective measures of on-the-job 

accident risk. They find that for a given level of quantitative risk, a college-educated individual is more 

likely to report that risk as “dangerous” than an individual with less than college education. For example, 

for those workers who report their quantitative risk as comparable to an annual injury risk of less than 1 in 
9

approach in the literature is to use industry-specific or occupation-specific risk measures 

reflecting an average of at least several years of observations for fatalities, which tend to 

be relatively rare events.6

 Measures of job-related fatality and injury risk have included self-reported risks 

based on worker surveys and objective risk measures derived from actuarial tables, 

workers’ compensation records, and surveys and censuses of death certificates. The 

choice of the measure of fatality risk can significantly influence the magnitude of the risk 

premium estimated through regression analysis. The nature of the risk measures also 

raise questions about possible errors in estimation and the need to correct the econometric 

specification to address them. 

 Several early papers on compensating differentials used the University of 

Michigan Survey of Working Conditions and Quality of Employment Survey data that 

include several qualitative measures of on-the-job risk. These measures utilize direct 

surveys of workers and their perceptions of their work environment. For example, 

Hamermesh (1978), Viscusi (1979, 1980), and Fairris (1989) estimated the hedonic wage 

equation with a dichotomous measure of injury risk based on a worker’s perception of 

whether his or her job is “dangerous.”7

 The survey asked workers if their job exposed 

them to physical dangers or unhealthy conditions. These studies estimated statistically 

significant coefficients on this “risk” variable in some of the specifications. Duncan and 

 20, half of the college-educated described their job risk as “dangerous” while only 19 percent of the lessthan-college educated reported their job risk as such. This differences in the “danger” cutoff biases 

estimates of risk premiums in wages, and this bias has implications distinct from typical measurement 

error. Gaba and Viscusi find that subjective quantitative measures of risk yielded wage premiums more 

consistent with estimates based on objective risk measures (BLS). Analysis of the qualitative risk measure 

(a dichotomous 0-1 “is the job dangerous?” variable) produced a much larger risk premium. 

6

 Averaging over a period of 5 to 10 years would likely remove any potential distortions associated with 

catastrophic accidents in any particular year. 

7

 Viscusi (1979, 1980) includes the danger variable in some regressions, although his research focused on 

compensating differentials based on BLS measures of injury and fatality risk discussed below. 
10

Holmlund (1983) undertook a similar analysis of compensating differentials with a 

“danger” variable in a study of male workers in Sweden. 

 Several papers on the U.S. labor market from the 1970s and early 1980s used 

actuarial data (Thaler and Rosen 1975, Brown 1980, Leigh 1981, Arnould and Nichols 

1983). These studies all employed a job-related risk measure based on data collected by 

the Society of Actuaries for 1967. The Society of Actuaries data set provides fatality risk 

data for 37 occupations. Across these 37 occupations, the annual risk averaged 

approximately 1 in 1,000. This fatality risk exceeds averages from other data sets by 

nearly an order of magnitude. To the extent that these data reflect workers in extremely 

high risk jobs, the estimated wage-risk tradeoffs will suffer from a selection bias. As a 

result, one would expect these estimates to be lower than found in more broadly 

representative samples, which has in fact proven to be the case. 

 Another difficulty is that the Society of Actuaries data do not distinguish fatalities 

caused by the job but rather reflect the overall fatality rates of people within a particular 

job category. For example, one of the highest risk occupations based on these actuarial 

ratings is actors, who typically face few risks other than unfavorable reviews. 

 Several studies of U.S. and Canadian labor markets have used workers’ 

compensation records to construct risk measures (Butler 1983, Dillingham 1985, Leigh 

1991, Martinello and Meng 1992, Meng 1991, Cousineau et al. 1992, Lanoie et al. 1995). 

Only three studies have used workers’ compensation data to evaluate compensating 

differentials in U.S. labor markets, which may reflect the decentralized nature and 

differences in information collection associated with state (not Federal) management of 
11

U.S. workers’ compensation programs.8 In contrast, researchers in Canada can obtain 

workers’ compensation-based risk data from Labour Canada (the labor ministry for the 

Federal government) and the Quebec government. 

 For analyses of the United States, the majority of the mortality risk studies have 

used data collected by the U.S. Department of Labor Bureau of Labor Statistics (BLS). 

About 80 percent of the U.S. nonfatal injury risk studies summarized below used BLS 

injury risk data. The BLS has compiled industry-specific fatality and injury risk data 

since the late 1960s. Through the early 1990s, BLS collected its data via a survey of 

industries, and reported the data at a fairly aggregated level, such as at the 2-digit and 3-

digit Standard Industrial Classification (SIC) code level. The aggregation and sampling 

strategy have elicited some concerns about measurement error in the construction of the 

mortality risk variable (see Moore and Viscusi 1988a). 

 Concerns about the BLS fatality risk data led the National Institute of 

Occupational Safety and Health (NIOSH) to collect information on fatal occupational 

injuries through its National Traumatic Occupational Fatalities surveillance system 

(NTOF) since 1980. NIOSH compiles these data from death certificates managed by 

U.S. vital statistics reporting units (NIOSH 2000). These data are reported at the 1-digit 

SIC code level by state. Because NIOSH compiles data from a census of death 

certificates, it circumvents some of the concerns about sampling in the pre-1990s BLS 

approach. Some have raised concerns, however, about the accuracy of reported cause of 

death in death certificates (Dorman and Hagstrom 1998). 

 

8

 Leigh’s (1991) analysis with risk data derived from workers’ compensation records in 11 states appears to 

be the only U.S. study to aggregate such data from more than one state. 
12

 Comparing the BLS and NIOSH fatality risk data over time provides some 

interesting contrasts. The original NIOSH data set for the fatality census averaged over 

1980 – 1985 has a mean fatality risk nearly 50 percent higher than a roughly comparable 

BLS data set averaged over 1972 – 1982.9

 Moreover, the BLS data had greater variation 

(a standard deviation 95 percent greater than its mean) than the NTOF data, although the 

NIOSH data also had substantial variation (standard deviation 23 percent greater than its 

mean) (Moore and Viscusi 1988a). 

Since 1992, the BLS has collected fatal occupational injury data through the 

Census of Fatal Occupational Injuries (CFOI). The BLS compiles information about 

workplace fatality including worker characteristics and occupation, circumstances of the 

event, and possible equipment involved. The BLS draws on multiple sources such as 

death certificates, workers’ compensation records, and other Federal and state agency 

reports. The BLS reports these fatality data by industry at the 4-digit SIC level. In 

contrast to the earlier comparisons of BLS and NIOSH data, more recent years’ data on 

fatality risk collected through the CFOI now show that the BLS measure includes 

approximately 1,000 more fatalities per year than the NIOSH measure (NIOSH 2000). 

Table 1 illustrates the recent national rates of job-related fatalities at the one-digit 

industry level for the four-year period in which both NIOSH and CFOI data are publicly 

available. In every instance the BLS measure shows a higher risk mortality rate, which in 

some cases, such as wholesale trade, is quite substantial. 

The risk variables used in several of the non-U.S. studies were based on jobrelated accident and mortality data collected by foreign governments. For example, the 

 

9

 Averaging fatality risk data over several years can reduce the distortion of a catastrophic event in one year 

in one industry on the measure of an industry’s risk. 
13

data sets used in Shanmugam (1996/7, 1997, 2000, 2001) were from the Office of the 

Chief Inspector of Factories in Madras. Several of the United Kingdom studies employ 

data provided by the Office of Population Censuses and Surveys (Marin and 

Psacharopoulos 1982, Sandy and Elliott 1996, Arabsheibani and Marin 2000) while 

others used unpublished data from the U.K. Health and Safety Executive (Siebert and 

Wei 1994). In their study of the South Korean labor market, Kim and Fishback (1999) 

obtained their accident data from the Ministry of Labor. Few of these studies indicate 

whether the mortality risk data were derived from samples or censuses of job-related 

deaths. 

While the large number of studies of labor markets around the world evaluated 

the compensating differential for an on-the-job death and/or on-the-job injury, very few 

attempted to account for the risk of occupational disease. Lott and Manning (2000) used 

an alternative data set to estimate the risk premium for jobs with higher cancer risk 

associated with occupational exposure to various chemicals (see Section 2). 

1.2.2 Wages and related data. Labor market studies of the value of risks to life and 

health match these risk measures to data sets on characteristics of wages, workers, and 

employment. Some researchers survey workers directly to collect this information, such 

as Gegax et al. (1991) for the United States, Lanoie et al. (1995) for Canada, Shanmugam 

(1996/7) for India, and Liu and Hammitt (1999) for Taiwan, among others. For the 

United States, researchers have also used the University of Michigan’s Survey of 

Working Conditions (SWC), the Quality of Employment Survey (QES), the Bureau of 

Labor Statistics’ Current Population Survey (CPS), the Panel Study of Income Dynamics 

(PSID), and decennial census data. Similar types of surveys undertaken in other 
14

countries have also provided the data necessary to undertake hedonic labor market 

analysis, such as the General Household Survey in the United Kingdom (e.g., Siebert and 

Wei 1994 and Arabsheibani and Marin 2000). 

 The dependent variable in virtually all labor market analyses has been a measure 

of the hourly wage. With some data sets, researchers have had to construct the wage 

measure from weekly or annual labor earnings data. For some data sets, a worker’s aftertax wage rate is provided, which can put wage and workers’ compensation benefits in 

comparable terms. While many studies have included pre-tax wages as the dependent 

variable, this would not likely bias the results significantly so long as workers’ income 

levels and tax rates do not differ substantially. If the regression model includes workers’ 

compensation benefits, then both the wage and these benefits should be expressed in 

comparable terms (both in after-tax or both in pre-tax terms) to ensure proper evaluation 

of the benefits’ impacts on wages.10

Typically, researchers match a given year’s survey data on wages and worker and 

employment characteristics with risk data for that year, or preferably, the average over a 

recent set of years. Some researchers have restricted their samples to subsets of the 

surveyed working population. For example, it is common to limit the analysis to fulltime workers, and many have focused only on male, blue-collar workers. Restricting the 

sample in this manner partially addresses the measurement problem with industry-level 

risk values common to most risk datasets by including only those workers for whom the 

risk data are most pertinent. 

 

10 Most hedonic wage-risk studies have not accounted for labor taxes in their construction of the wage 

variable or in their interpretation of the value of a statistical life. Exceptions include several papers that 

have focused on the effects of workers’ compensation on the compensating differential for occupational 

risk (e.g., Moore and Viscusi 1990a). This reflects the more common use of pre-tax wages in the broader 

labor market analysis literature. 
15

1.2.3 Wage vs. log(wage). Most researchers have estimated the wage equation using 

linear and semi-logarithmic specifications. Choosing a preferred functional form from 

these two specifications cannot be determined on theoretic grounds (see Rosen 1974). To 

identify the specification with greatest explanatory power, Moore and Viscusi (1988a) 

employed a flexible functional form given by the Box-Cox transformation. The Box-Cox 

transformation modifies the dependent variable such that the estimated regression model 

takes the form: 

i i i i i i i i i

i H X p q qWC p H w

α β β γ γ γ β ε

λ

λ

= + ′ + ′ + + + + ′ + − 1 2 1 2 3 3

1 . 

This approach presumes that a λ exists such that this model is normally 

distributed, homoskedastic, and linear in the regressors. Note that the case where λ Æ 0 

represents the semi-logarithmic functional form and the case where λ Æ 1 represents the 

linear functional form. The flexible form under the Box-Cox transformation can test the 

appropriateness of these two restrictions on the form of the model. Using maximum 

likelihood methods, Moore and Viscusi’s estimate for λ equaled approximately 0.3 for 

their data. While this value is more consistent with a semi-logarithmic form than a linear 

form, the authors reject both specifications based on a likelihood ratio test. The estimated 

value of a statistical life based on the Box-Cox transformed regression model, however, 

differed only slightly from the log(wage) specification. Shanmugam (1996/7) replicated 

this flexible form evaluation with his evaluation of compensating differentials in India. 

His maximum likelihood estimate for λ equaled approximately 0.2. While Shanmugam 

rejected the semi-logarithmic and linear models, he found that the semi-logarithmic 
16

functional form also generated results closer to those found with the unrestricted flexible 

form.11

1.2.4 Errors in variables problem with risk measures. Every compensating differential 

study employs a less than perfect measure of any particular worker’s job-related fatality 

risk. The majority of these studies have used fatality risk measures from the BLS 

averaged across the entire industry. Such an approach, however, suffers from 

measurement error. As noted above, some researchers have found that the pre-1992 BLS 

data sets (and NIOSH data sets to a lesser extent) suffer from incomplete reporting. The 

industry averages constructed by the BLS do not exactly reflect realized industry 

averages. Further, applying industry averages to individuals may result in errors 

associated with matching workers to industries due to response error in worker surveys. 

Mellow and Sider (1983) evaluated several surveys that asked workers and their 

employers to identify the workers’ industry and occupation (among other questions). In 

their assessment of the January 1977 Current Population Survey, 84 percent of workers 

and their employers agreed on industry affiliation at the three-digit SIC code level while 

only 58 percent agreed on the three-digit occupational status. Merging a worker 

characteristics data set with a risk measure data set based on industry affiliation (or 

occupation status) can result in a mismatch of worker characteristics and industry risk. 

Mellow and Sider’s statistical analysis of the 16 percent “mismatched” workers by 

industry affiliation showed that the errors in matching reduced the compensating 

differential for injury risk by about 50 percent in their samples. 

 

11 In the product market context, Atkinson and Halvorsen (1990), Dreyfus and Viscusi (1995), and Gayer, 

Hamilton, and Viscusi (2000) employ Box-Cox transformations to evaluate their respective hedonic price 

models (the first two focused on automobile prices and the third on housing prices). 
17

Even with a perfect industry measure of fatality risk and appropriate matching of 

workers and their industry, measurement error still exists since some workers bear risk 

that differs from their industry’s average. For example, different occupations within an 

industry may pose different levels of risk. This measurement error can be characterized 

as: 

i i i p = p +η * , 

where pi reflects the observed industry average fatality risk, pi* reflects the unobserved 

(to the econometrician) fatality risk associated with worker i’s job, and ηi reflects the 

deviation of that job’s risk from the industry average. Random measurement error will 

result in a downward bias on coefficient estimates, and the least squares estimate of the 

coefficient on fatality risk in this example would be inconsistent: 

2 2 1

2

1, ˆ γ

σ σ

σ

γ

η

















+ →

p

p p

OLS

where the signal-noise ratio determines the extent of the downward bias towards zero. 

 In addition to the downward effect on the risk coefficient, applying industry-level 

risk data to individual observations may also induce some correlation in the residuals 

among individuals within industries. Robust (White) standard errors would not 

appropriately correct for this correlation and result in inappropriately small standard 

errors. Hersch (1998) and Viscusi and Hersch (2001) employ robust standard errors 

correcting for within-group (within-industry) correlation. 

1.2.5 Omitted variables bias and endogeneity. Failing to capture all of the determinants 

of a worker’s wage in a hedonic wage equation may result in biased results if the 

unobserved variables are correlated with observed variables. Dangerous jobs are often 
18

unpleasant in other respects. Omission of non-pecuniary characteristics of a job may bias 

the estimated risk premium if an omitted variable is correlated with risk. For example, 

one may find a correlation between injury risk and physical exertion required for a job or 

risk and environmental factors such as noise, heat, or odor. While some studies have 

attempted to control for these unobservables by including industry or occupation dummy 

variables (see below), a model may still suffer from omitted variables bias. 

 Several studies have explored how omitting injury risk affects the estimation of 

mortality risk. Viscusi (1981) found that omitting injury risk resulted in a positive bias in 

the mortality risk measure for union affiliated workers. Cousineau et al. (1992) also 

found that omitting injury risk may cause a positive bias in the estimation of the 

coefficient on mortality risk. The high correlation (collinearity) between injury and 

mortality risks, however, can make joint estimation difficult. Some studies have 

attempted to estimate regression equations with both types of risk and have found nonsignificant coefficients on at least one of the measures, including Smith (1976), Leigh 

(1981), Dillingham and Smith (1984), and Kniesner and Leeth (1991). 

 While including injury risk in a regression model could address concern about one 

omitted variable, other possible influences on wages that could be correlated with 

mortality risk may not be easily measured. Several papers have investigated this bias. 

Garen (1988) notes that “individuals may systematically differ in unobserved 

characteristics which affect their productivity and earnings in dangerous jobs and so these 

unobservables will affect their choice of job risk” (p. 9). One example Garen offers is 

“coolheadedness,” which may make a worker more productive under the stresses of a 

dangerous job but may not be relevant in a safe job. In this case, an econometrician 
19

would prefer to include both the mortality risk variable and the interaction or the 

mortality risk variable with a variable measuring coolheadedness as regressors in the 

hedonic labor market model. Failing to include this interaction term results in biased 

least squares estimation. Garen attempts to address this concern with an instrumental 

variables technique, although subsequent researchers such as Hwang et al. (1992) have 

noted the difficulty in identifying appropriate instruments for his procedure. Employing 

this instrumental variables technique, Garen found a mortality risk premium about double 

what the standard least squares model produced. 

 The significant increase in the risk premium associated with a method to account 

for unobserved productivity is consistent with the theoretical and simulation findings in 

Hwang et al. (1992). They estimate that for plausible parameter estimates, models that 

fail to account for heterogeneity in unobserved productivity may bias estimates of the risk 

premium by about 50 percent and could result in incorrectly (negative) signing of the risk 

variable. With the exception of some non-union samples in several studies (e.g., Dorsey 

1983 and Dickens 1984), the empirical literature presents very little evidence of this 

wrong signing. Siebert and Wei (1994) have also found that accounting for the 

endogeneity of risk can increase the risk premium compared to a standard least squares 

approach. Recent theoretical research, however, has also illustrated the potential for 

over-estimating the risk premium by failing to control for unobservables (Shogren and 

Stamland 2002). They note that workers with the ability to avoid injury select into risky 

jobs while those less able to avoid injury (“clumsy” workers) select into less-risky jobs. 

They argue that risk premiums could be overestimated by a factor of four with plausible 

parameter estimates in their simulations. Whether there will be such biases hinges on the 
20

monitorability of individual’s safety-related productivity. If these differences are 

monitorable, as in Viscusi and Hersch (2001), there will be a separating compensating 

differential equilibrium for workers of different riskiness. 

 Viscusi and Hersch (2001) note that differences in workers’ preferences over risk 

can affect the shape of their indifference curves and workers’ safety behavior and, by 

affecting firms’ cost to supply safety, can influence firms’ offer curves. They evaluated 

the wage-risk (injury) tradeoff of workers with a data set that includes measures of risk 

preferences (e.g., smoking status) and measures of workers’ prior accident history. While 

smokers work, on average, in industries with higher injury risk than non-smokers, 

smokers also are more likely to have a work-related injury controlling for industry risk. 

Smokers also are more prone to have had a recent non-work-related accident. As a result, 

Viscusi and Hersch find that nonsmokers receive a greater risk premium in their wages 

than do smokers because the safety effect flattens smokers’ offer curves enough to offset 

smokers’ preferences for greater wages at higher risk levels. 

 To address potential omitted variable bias arising from differences in worker 

characteristics, employing a panel data set could allow one to difference out or dummy 

out individual-specific unobservables, so long as these are constant throughout the time 

period covered by the panel. Unfortunately, very few data sets exist that follow a set of 

workers over a period of several years. Brown (1980) used the National Longitudinal 

Study Young Men’s sample over 1966 – 1973 (excluding 1972) with the Society of 

Actuaries mortality risk data. While he reported results that were not consistent with the 

theory of compensating differentials for a variety of nonpecuniary aspects of 

employment, he did estimate a positive and statistically significant coefficient on the 
21

mortality risk variable. Brown noted that his estimate of the risk premium was nearly 

three times the size of the estimate in Thaler and Rosen (1975), which first used the 

Society of Actuaries mortality risk data. 

1.2.6 Compensating differentials for risk or inter-industry wage differentials. Several 

recent papers have claimed that estimates of risk premiums in this kind of wage 

regression analysis actually reflect industry wage premiums because the fatality risk 

variables typically reflect industry-level risk (Leigh 1995, Dorman and Hagstrom 1998). 

Both Leigh and Dorman and Hagstrom evaluate the proposition that risk premiums 

simply reflect industry premiums by comparing compensating differential models 

without dummy variables for industry affiliation of each worker with models that include 

such dummy variables. 

Their claim that industry premiums mask as risk premiums in these wage 

regressions suffers from several deficiencies. First, a large number of studies have 

included industry dummy variables in their statistical analyses and found significant 

compensating differentials for risk. For example, the first wage-risk tradeoff study by 

Smith (1974) employed six industry dummies and yielded a statistically significant 

compensating differential for risk. Viscusi (1978a) included 25 industry dummy 

variables in his analysis based on the Survey of Working Conditions danger variable (0, 

1 variable reflecting a worker’s subjective perception of on-the-job risk), although he 

excluded the dummy variables from the analysis based on the industry-level BLS risk 

data.12 In both sets of analyses, danger and the BLS risk measure were statistically 

significant and generated very similar estimates of the risk premium. Freeman and 

 

12 Viscusi (1978a) excluded the dummy variables out of concern of inducing multicollinearity between an 

industry-level risk measure and industry dummy variables. Refer to the discussion below on 

multicollinearity. 
22

Medoff (1981) found a statistically significant risk premium in their analyses that 

included 20 industry dummy variables and the BLS injury rate measure. In their 

evaluation of the U.K. labor market with an occupational mortality risk variable, Marin 

and Psacharopoulos (1982) found a statistically significant risk coefficient while their 

SIC code dummies were insignificant. Dickens (1984) estimated regression models with 

the BLS fatality risk measure and 20 industry dummy variables (1- and 2-digit SIC code 

industries). For the union sample, he found a positive and statistically significant 

coefficient on risk. Leigh and Folsum (1984) included 2-digit SIC code industry dummy 

variables in their wage regressions, and they found statistically significant coefficients on 

mortality risk in all eight mortality risk models reported. Dillingham (1985) estimated 

regression models with industry dummy variables (at the 1-digit SIC code level) and 

without. In both cases, he found statistically significant and positive coefficients on his 

measure of mortality risk. Moreover, the coefficients were virtually identical (0.0023 vs. 

0.0022), although the standard error was higher for the model with industry dummy 

variables (perhaps related to risk-industry dummy variable collinearity). Cousineau et al. 

(1992) included 29 industry variables in their evaluation of the Canadian labor market 

that estimated statistically significant coefficients on both injury and mortality risks. Lott 

and Manning (2000) included 13 industry dummy variables in their evaluation of longterm cancer risks in U.S. labor markets, and found a statistically significant risk premium 

based on industry-level measures of carcinogen exposure. 

Second, inserting industry dummy variables into the regression equation induces 

multicollinearity with the risk variable. Previous researchers such as Viscusi (1979) have 

noted this as well. Hamermesh and Wolfe (1990) employed dummy variables for five 
23

major industries in their analysis of injury risk on wages. They note that a finer 

breakdown by industry could be used. A complete set of dummy variables at the 3-digit 

SIC code level, however, would completely eliminate all variation in the injury risk 

variable, which is measured at the 3-digit SIC code level (p. S183). While 

multicollinearity does not affect the consistency of the parameter estimates, it will 

increase standard errors. 

 This induced multicollinearity is also evident in the Dorman and Hagstrom results 

for the models using NIOSH fatality risk data.13 Dorman and Hagstrom interact the 

NIOSH fatality risk measure by a dummy variable for union status (and for non-union 

status in the second set of regressions). Contrary to their hypothesis, including industry 

dummy variables does not reduce the coefficient in the union-risk interaction models. 

Inducing multicollinearity does depress the t-statistics slightly, although not enough to 

render the coefficients statistically insignificant. The models with the non-union-risk 

interaction reflect the induced multicollinearity, as the t-statistics fall below levels 

typically associated with statistical significance moving from the standard model to the 

industry dummy model. While the coefficients in these industry dummy-augmented 

models fall from their levels in the standard models, they are not statistically different 

from the standard models’ coefficients. Based on the NIOSH fatality risk data, the 

Dorman and Hagstrom results appear to illustrate that including collinear regressors 

 

13 We focus our discussion here on their NIOSH data-based results for several reasons. First, we believe 

that the NIOSH data are superior to the 1980s BLS fatality risk data as discussed above. Second, the 

regression models based on non-NIOSH data in Dorman and Hagstrom have insignificant risk coefficients 

for all specifications – without industry dummy variables, with industry dummy variables, and with other 

industry characteristics. While we are not certain why they did not find significant risk coefficients with 

the BLS risk data as previous researchers had, it seems moot to argue that a coefficient on a risk variable 

actually represents an industry effect if that coefficient is not significant. 
24

(industry variables) can increase standard errors but not significantly affect the 

magnitudes of the parameter estimates. 

2. The value of a statistical life based on U.S. labor market studies 

 The value of a statistical life should not be considered a universal constant or 

some “right number” that researchers aim to infer from market evidence. Rather, the 

VSL reflects the wage-risk tradeoffs that reflect the preferences of workers in a given 

sample. Moreover, transferring the estimates of a value of a statistical life to non-labor 

market contexts, as is the case in benefit-cost analyses of environmental health policies 

for example, should recognize that different populations have different preferences over 

risks and different values on life-saving. If people face continuous safety choices in a 

variety of contexts, however, the same individual should exhibit the same risk-money 

tradeoff across different contexts, provided the character of the risks is the same. 

Researchers have undertaken more than 30 studies of compensating differentials for risk 

in the U.S. labor market. Some studies have evaluated the wage-risk tradeoff for the 

entire labor force, while others have focused on subsamples such as specific occupations 

(e.g., police officers in Low and McPheters 1983), specific states (e.g., South Carolina in 

Butler 1983), blue-collar workers only (e.g., Dorman and Hagstrom 1998 and Fairris 

1989), males only (e.g., Berger and Gabriel 1991), and union members only (e.g., 

Dillingham and Smith 1984). These hedonic labor market studies also vary in terms of 

their choice of mortality risk variable, which can significantly influence the estimation of 

a value of a statistical life (for comparison of NIOSH and BLS data, refer to Moore and 

Viscusi 1988a and Dorman and Hagstrom 1998). 
25

 Table 2 summarizes the estimated VSLs for the U.S. labor market from the 

literature over the past three decades.14 Because some studies provided multiple 

estimates, in these instances we provide illustrative results based on the principal 

specification in the analysis. Table 2 provides a sense of the magnitude and range of U.S. 

labor market VSLs and illustrates the influence of factors such as income and the 

magnitude of risk exposure as well as specification issues such as including nonfatal 

injury risk and worker’s compensation.15 

 Viscusi (1993) reported that most surveyed studies fall within a $3.8 - $9.0 

million range, when converted into year 2000 dollars. 16, 17 While we include more 

papers from the United States as well as findings from other countries, the general 

conclusion remains unchanged. Half of the studies of the U.S. labor market reveal a 

value of a statistical life range from $5 million to $12 million. Estimates below the $5 

million value tend to come from studies that used the Society of Actuaries data, which 

 

14 We selected papers for inclusion in this literature review with a modest set of criteria. First, a study 

should be written in English. Second, a study should be published in either an academic journal or a book. 

Third, a study should provide enough information to calculate a value of a statistical life. In several cases, 

information provided in more recent papers (such as per capita incomes reported for some studies in 

Viscusi 1993 and Mrozek and Taylor 2002) has been used with reported coefficient estimates to calculate 

the value of a statistical life for a study. Our aim is to characterize as best as possible the universe of wagerisk studies in the literature, so we have not attempted to purge this assessment of so-called “low quality” 

studies or to modify the value of a statistical life estimates from such studies. For some studies, we have 

presented a range of the value of a statistical life estimates and for others we have presented an illustrative 

point estimate. In the case of the latter, we have focused on the reported results for the whole sample (as 

opposed to union-only, blue-collar only, managerial-only, etc. samples) based on the econometric 

specification preferred by the studies’ authors. 15 While these estimates have been adjusted to constant year dollars (2000 US$), they have not been 

adjusted for differences in the samples’ income levels. The per capita income data provided in Table 2 

coupled with income elasticity estimates in Section 6 can be used to modify the values of a statistical life in 

Table 2 so that they reflect a common income level. 

16 In this paper, we present all VSLs in terms of 2000 US$. All domestic values are converted using the 

CPI-U deflator series (Council of Economic Advisers 2002 Table B60). International conversions are 

made using purchasing power parity exchange rates from the Penn World Table 6.0 (see Summers and 

Heston 1991, Aten et al. 2001, and Internet: http://pwt.econ.upenn.edu) and then converted to year 2000 

dollars with the CPI-U deflator series. 

17 Viscusi (1993) actually reports $3 – $7 million in December 1990 US$. This has served as a reference 

range in several subsequent value of a statistical life studies. Note that we have updated this range to 2000 

US$. 
26

tends to reflect workers who have self-selected themselves into jobs that are an order of 

magnitude riskier than the average. Many of the studies yielding estimates beyond $12 

million used structural methods that did not estimate the wage-risk tradeoff directly or 

were derived from studies in which the authors reported unstable estimates of the value of 

a statistical life. Our median estimated VSL from Table 2 is about $7 million, which is in 

line with the estimates from the studies that we regard as most reliable. In terms of 

methodology, we are more confident in the results presented in Viscusi (1978a, 1979), 

which include the most extensive set of non-pecuniary characteristics variables to explain 

workers’ wages, and the results presented in Moore and Viscusi (1988a), which include 

the NIOSH mortality risk data in lieu of the pre-1992 BLS mortality risk data. 

A salient research issue of policy importance is the effect of income levels on the 

wage-risk tradeoff. For example, Hamermesh (1999) notes that as wage inequality has 

increased over the last several decades, so have on-the-job mortality risks diverged. He 

notes that workplace safety is highly income-elastic. This result is related to the findings 

in Viscusi (1978b) that the value of a statistical life is increasing in worker wealth. 

Similarly, Viscusi and Evans (1990) have estimated the income elasticity of the value of 

statistical job injury risks to be 0.6 to 1.0. The effect of income on the wage-risk tradeoff 

is evident in a historical evaluation of employment risks as well. Kim and Fishback 

(1993) estimated compensating differentials for mortality risk in the railroad industry 

over the period 1893 – 1909 and found implicit values of statistical life on the order of 

$150,000 in today’s dollars.18 Our meta-analysis below examines the role of income 

differences in generating the variation in VSL estimates. 

 

18 Also refer to Fishback and Kantor (1992) for a historical evaluation of compensating differentials for 

occupational risk in the late 19th century. 
27

 While most hedonic labor market studies focus on the risk of accidental death or 

accidental injury, several papers have attempted to explore the effect of occupational 

disease. Lott and Manning (2000) evaluated the effect of carcinogen exposure on 

workers’ wages within the context of changing employer liability laws. In lieu of the 

standard mortality risk measures, the authors employ the Hickey and Kearney carcinogen 

index, which represents worker carcinogen exposure at the 2-digit SIC code level.19 

They find that workers’ wages reflect a risk premium for carcinogen exposure. Lott and 

Manning convert their results into a value of a statistical life assuming that the index is a 

proportional representation of the actual probability of getting occupational-related 

cancer, that 10 – 20 percent of all cancer deaths result from occupational exposures, and 

that the probability of a worker getting cancer ranges from 0.04 to 0.08 percent per year. 

We have modified their reported VSL range to account for a latency period.20 Based on 

these assumptions, the authors estimate that the value of a statistical life based on 

occupational cancer would range from $1.5 – $3.0 million. Assuming that occupational 

cancers, however, comprise a smaller fraction of all cancer deaths would increase the 

implicit VSL.21

 

19 Smith (1983) also used the Hickey and Kearney carcinogen exposure index to complement his 

assessment of compensating differentials for injury risk in U.S. labor markets. He found that the index is 

significantly positively correlated with wages implying a risk premium for workers’ operating in 

environments exposing them to more carcinogens. Further, Smith included a measure of total suspended 

particulates (TSP) based on the workers’ locations and found a statistically significant and positive 

correlation between TSP and workers’ wages. Since TSP has been linked with various respiratory diseases, 

this could illustrate another premium for workers’ bearing long-term risks on the job. 

20 The values we report reflect an assumed latency period of 10 years and a real discount rate of 3 percent. 

Note that the VSL estimates in parentheses in Table 2 represent the values reported by Lott and Manning. 

Employing a real discount rate of 7 percent, consistent with U.S. Office of Management and Budget (1992) 

guidance, over a 10-year period effectively reduces the VSL reported by Lott and Manning in half. 

21 Meng (1991) attempted to account for occupational disease by including a variable that reflected the rate 

of heart attacks and industrial disease. However, very modest information about the basis for this variable 

is provided. It is difficult to discern whether the variable captures long-term work-related risks or simply a 

selection effect that contaminates the data like the Society of Actuaries data set. 
28

 Several early papers in the literature did not find statistically significant 

compensating differentials for on-the-job mortality risk. For example, Leigh (1981) 

estimated a risk premium for injuries but not for fatalities. Dorsey (1983) likewise did 

not find a mortality-based risk premium. The Leigh study coupled the Society of 

Actuaries mortality data with BLS injury data. The combination of greater measurement 

error in the data and the high correlation between injury risks and mortality risks 

probably led to the insignificance of the mortality risk variable. The Dorsey study uses 

industry-level averages, instead of worker-specific values, as its unit of observation. This 

averaging across industry for wages and related explanatory variables may have reduced 

the variation necessary to discern the effects of job-specific influences on wage, such as 

job risk. 

 More recent papers by Leigh (1995) and Dorman and Hagstrom (1998) also do 

not find compensating differentials in many model specifications. As discussed above, 

we do not find their inter-industry wage differential discussion compelling. Nevertheless, 

Table 2 includes their results based on the NIOSH data with industry dummy variables.22 

 Some of these analyses of U.S. labor markets investigated the potential 

heterogeneity in the risk preferences of workers in the labor force in which there is 

worker sorting by level of risk. The empirical issue is whether the wage-risk tradeoff 

takes a linear or concave shape. A linear form would imply that an incremental increase 

of risk in the labor market requires a proportional increase in the wage differential. A 

concave form, however, would imply a less than proportional increase in the wage 

differential, perhaps reflecting sorting by workers based on their risk preferences. 

 

22 The authors also report estimates using the less reliable pre-1992 BLS risk data. 
29

To evaluate the shape of this tradeoff, one can modify the wage equation 

regression model to include both mortality risk and the square of mortality risk. If the 

latter term is not significant, then the wage-risk tradeoff is linear for the range of risks 

and wages covered by the study’s sample. If the squared term is significant and negative, 

then the wage-risk tradeoff takes a concave form. Viscusi (1981), Olson (1981), Dorsey 

and Walzer (1983), and Leigh and Folsum (1984) all found evidence that the risk-wage 

tradeoff curve is concave.23 All four studies include regression models with a quadratic 

representation of mortality risk. 

Figure 2 illustrates how the value of a statistical life varies with mortality risk for 

a sample of six regression models from these four papers. Viscusi (1981; linear) and 

Leigh and Folsum (L&F 1984; linear) represent regression models where the dependent 

variable is the hourly wage while the other four lines represent regression models with 

the logarithm of the wage as the dependent variable. All six models include measures of 

nonfatal injury risks (probability of a lost-workday accident and, in some cases, duration 

of lost-workday accident). The slopes of the risk-VSL lines in this figure are similar 

within the wage-specification type where the wage-based models appear to have a steeper 

tradeoff than do the logarithm of wage-based models (with the exception of the Dorsey 

and Walzer model, although this may reflect the fact that the sample in their study faced 

mortality risks 2 to 3 times smaller on average than the samples in the other studies). 

Based on these models, populations of individuals who select into jobs with very minor 

risks (e.g., on the order of 1 in 100,000) have implicit values of statistical life ranging 

from $12 to $22 million. Increasing the risk ten-fold, to levels that are close to the mean 

 

23 Siebert and Wei (1998) found a concave wage-risk relationship with a risk and risk-squared specification 

for their analysis of the Hong Kong labor market. Meng and Smith (1990, 1999) also found the same 

relationship in their assessments of the Canadian labor market. 
30

mortality risks in these studies, modestly reduces the VSL into the range of $10 to $18 

million. Figure 2 illustrates that very high risks result in small values of statistical lives, 

although caution should be exercised when considering extrapolations beyond the 

samples’ ranges. 

3. Evidence of the value of a statistical life from U.S. housing and product markets 

 Housing and product market decisions also reflect individual tradeoffs between 

mortality risk and money. The main methodological difference is that economists 

typically estimate a hedonic price equation rather than a hedonic wage equation. The 

underlying theory is essentially the same, as comparison of Rosen (1974) with the wage 

equation analysis above will indicate. 

 Table 3 presents the results from ten studies that evaluated the price-risk tradeoffs 

for seatbelt use, cigarette smoking, home fire detectors, automobile safety, bicycle 

helmets, and housing price responses to hazardous waste site risks.24 The studies in 

general find an implicit value of a statistical life on the same order of magnitude as the 

labor market studies, although they tend to be a little lower. 

The lower estimates may reflect several characteristics of these studies that 

distinguish them from the labor market studies. First, some product decisions do not 

provide a continuum of price-risk opportunities (unlike the labor market that does offer a 

fairly continuous array of wage-risk employment options) but rather a discrete safety 

decision. For example, Dardis’ (1980) evaluation of smoke detectors represents such a 

 

24 All these studies evaluate risk-income tradeoffs in the United States. In our survey of the literature, we 

found only one study focusing on behavior outside of the United States. Ghosh, Lees, and Seal (1975) 

study the tradeoff between highway speeds and mortality risk in the United Kingdom. They derive a value 

of a statistical life estimate of $0.9 million.
31

discrete choice. In such a case, the consumer’s decision to purchase a smoke detector 

reveals only the lower bound on the willingness to pay for the reduced risk. Similarly, 

the study by Jenkins, Owens, and Wiggins (2001) examines the purchase of bicycle 

helmets. It is interesting, however, that their results show VSLs increasing over the first 

half of the life cycle. 

Second, the types of products considered in some studies may induce selection 

based on risk preferences. For example, the low estimated VSL for cigarette smokers 

found by Ippolito and Ippolito (1984) presumably reflects the non-random character of 

the smoking population. Their research focuses on cigarette smokers, and they estimate a 

VSL lower than from most product market studies. The lower VSL is consistent with the 

findings in Hersch and Viscusi (1990) and Viscusi and Hersch (2001) who find that 

individuals who engage in risky behaviors, such as cigarette smoking and driving without 

seatbelts, have lower implicit values for injury than do those who do not engage in such 

behavior. 

 Third, several studies are based on inferred, instead of observed, price-risk 

tradeoffs. Consider the seat belt and child seat studies by Blomquist (1979) and Carlin 

and Sandy (1991). In these studies, drivers’ or occupants’ safety is traded off with the 

time to secure a seat belt or a child seat. The authors assume a given time cost – for 

example, Blomquist assumes that it takes 8 seconds to secure a seat belt. Then this time 

is monetized at the individual’s wage rate. Unlike labor market studies where the 

monetary value of the attribute in question (job wage) is observed, these studies do not 

observe the actual time drivers take to buckle their seat belts. Moreover, they do not 

account for other aspects of seat belt use, such as the costs of discomfort of wearing a 
32

seatbelt, which would increase the implicit valuation of a statistical life derived by this 

methodology. 

 The studies focused on automobile purchases and home purchases do not suffer 

from the need to infer the monetary component of the price-risk tradeoff. For example, 

Atkinson and Halvorsen (1990) and Dreyfus and Viscusi (1995) evaluate the risk-price 

tradeoff for automobiles. They construct hedonic price models very similar to the 

hedonic wage models used in labor market analyses. Both studies include car purchase 

price (annual average for a given model) as the dependent variable and an array of 

automobile attributes as explanatory variables, such as vehicle size, power, reliability, 

fuel economy, and safety (fatal accident rate). Just as in the labor market hedonic studies, 

the coefficient on the safety variable in these automobile price hedonic studies reveals the 

price-risk tradeoff. Automobile purchases should be less likely to suffer the selection 

bias of the cigarette smoking study or the discreteness in decision of the fire alarm study. 

 Gayer, Hamilton, and Viscusi (2000) evaluate the tradeoff between housing prices 

and cancer risk associated with hazardous waste sites. The authors develop a housing 

hedonic price model, similar in form to the labor market hedonic studies. The dependent 

variable is the price of a house sold over a five-year period in the greater Grand Rapids, 

MI area, and explanatory variables include house characteristics such as number of 

bedrooms and bathrooms, neighborhood characteristics, property tax rates, measures of 

proximity to a Superfund hazardous waste site, and calculated cancer risk associated with 

exposure from the nearest hazardous site. The VSL interpretation from this study is 

analogous to that of Lott and Manning (2000). The hedonic price model generates the 

value of avoiding a statistical cancer case, which may not necessarily reflect the value of 
33

a statistical life to the extent that some cancers are treatable. If housing decisions are 

made based on the expectation that cancers associated with hazardous waste site exposure 

are terminal, then this price-risk tradeoff can be considered comparable to a VSL.25 

4. The value of a statistical life based on non-U.S. labor market studies 

 While about 25 wage-risk studies of the U.S. labor market were published in the 

1970s and 1980s, only three studies on non-U.S. labor markets appeared in the literature 

during this period. We have identified another 19 labor market hedonic studies in both 

developed and developing countries outside of the U.S. context published since 1990. 

The studies presented in Table 4 include evaluations of wage-risk tradeoffs in labor 

markets in Australia, Austria, Canada, Japan, and the United Kingdom. More recent 

work in developing countries has focused on Asia, including analyses of labor markets in 

Hong Kong, India, South Korea, and Taiwan. 

 Marin and Psacharopoulos (1982) undertook the first hedonic labor market 

analysis of job risks outside of the United States in their study of the U.K. labor market. 

Based on wage and risk data from the 1970s, they found a value of a statistical life of 

about $3.5 million. Arabsheibani and Marin (2000) sought to replicate the earlier Marin 

and Psacharopoulos analysis for the United Kingdom. By employing a similar 

methodology and more recent wage and risk data from the same sources as in the original 

study, the authors evaluated the stability of VSL estimates over time. They found, 

consistent with other studies of the U.K. labor market during the 1980s, a higher value of 

a statistical life than did Marin and Psacharopoulos. While the evaluation of the whole 

 

25 We have discounted the reported VSLs assuming that homeowners perceive a 10-year latency period and 

use a 3 percent discount rate in making their housing decisions. 
34

U.K. labor force yielded a relatively large VSL of about $18 million, regression analyses 

of subsamples resulted in VSLs ranging up to $68 million (in this case, for non-manual 

workers). While this result qualitatively conforms to the U.S. findings of lower VSLs for 

workers in higher risk jobs (see Figure 2), the magnitude of the U.K. compensating 

differentials seems implausibly large. 

The results from several of the studies of the United Kingdom reveal 

compensating differentials on the order of 10 percent of total worker wage income. One 

regression result (with a VSL of $63 million) from Sandy and Elliott (1996) implies a 

compensating differential for mortality risk comprising nearly 20 percent of worker 

wages. These risk premia are substantially larger than the compensating differentials 

evident in other developed countries’ labor markets, even those countries with higher per 

capita incomes. Moreover, risk levels cannot account for the high wage share of 

compensating differentials as the mortality risk is lower than in many U.S. studies. The 

large U.K. compensating differentials may reflect correlation between the risk measure 

and other unobservables that yield substantial returns to the worker. 

 After the United States, no country has been the focus of more hedonic labor 

market analyses of wage-risk tradeoffs than Canada. The Canadian studies appear to 

produce compensating differentials more in line with the U.S. experience than with the 

evidence from the U.K. labor market. With the exception of the Lanoie, Pedro, and 

LaTour (1995), most Canadian labor market VSLs fall within the range of $3 – $6 

million. The Lanoie et al. findings of a VSL on the order of $18 million – $20 million 

may reflect their data collection methodology. They surveyed about 200 workers in the 

Montreal area and solicited workers’ perceptions of risk with a risk information ladder 
35

similar to that in Gegax et al. (1991), which suffered from using a truncated job risk scale 

that omitted most job risks in the U.S. economy. Analyses by Lanoie et al. with industry 

risk measures provided by the Quebec Compensation Board did not yield statistically 

significant risk coefficients, while the perceived risk measures generated these large 

VSLs. This result contrasts with the findings of Cousineau, Lacroix, and Girard (1992) 

who found a statistically significant compensating differential for risk using mortality 

risk data from the same source on a sample of more than 30,000 Quebec workers. 

 With the exception of some U.K. studies, the compensating differentials estimated 

in developed country analyses tend to find risk premiums ranging between 1 – 2 percent 

of labor income. These results are broadly consistent with the findings in the Duncan and 

Holmlund (1983) that used Swedish workers’ perceptions of danger in lieu of measured 

industry mortality risks. The authors estimated a statistically significant and positive 

compensating wage differential for dangerous jobs on the order of about 2 percent of 

wages. Swedish workers’ perceptions of danger yield comparable compensating 

differentials to measured industry mortality risk in both U.S. and European studies (see 

Viscusi 1979 for an example from the U.S. labor market). 

 Researchers have also evaluated the VSL in several of the newly industrialized 

countries of Asia, including Hong Kong, South Korea, and Taiwan. Note that these 

countries have on-the-job mortality risks three to five times greater than the average in 

Australia, the United States, and the United Kingdom. Further, the average worker 

earnings are two to four times lower than labor earnings in developed countries. 

Kim and Fishback (1999) examined the South Korean labor market over the 1984 

– 1990 period. Unlike many of the studies in developed countries, which employ worker-
36

level data, their unit of observation is at the industry level. Kim and Fishback estimate a 

VSL of approximately $0.5 million. They note that the estimated VSL is about 94 times 

the average annual earnings of workers. Siebert and Wei (1998) estimate a VSL for the 

Hong Kong workforce that is larger than the Korean estimate by about a factor of three. 

The ratio of VSL to average annual earnings for Hong Kong is about 150. These 

estimates are of the same order of magnitude as the ratio of VSL to annual earnings 

evident in the U.S. labor market. 

 Liu, Hammitt, and Liu (1997) and Liu and Hammitt (1999) estimated the wagerisk tradeoff in Taiwan. The Liu, Hammitt, and Liu study focuses on all non-agricultural 

workers while Liu and Hammitt base their analysis on in-person surveys of petrochemical 

workers. In the former case, the authors use 3-digit industry level risk data, while the 

latter paper uses workers’ risk perceptions derived from a survey instrument similar to 

that in Gegax et al. (1991). Workers’ risk perceptions in the petrochemical industry yield 

a mortality risk rate about 35 percent greater than the rate published by the Taiwan Labor 

Insurance Agency, the data source for the Liu, Hammitt, and Liu study.26 While 

petrochemical workers face higher average mortality risk (perceived and measured) than 

the average for all non-agricultural workers in Taiwan, the higher wages and income 

associated with petrochemical workers in 1995 relative to the broader workforce in the 

early to mid 1980s probably explains why Liu and Hammitt estimated a VSL about twice 

what Liu, Hammitt, and Liu found. 

 Estimates for the Indian labor market yield a value of a statistical life greater than 

the VSLs in other developing countries despite the fact that per capita income in India is 

 

26 The difference between the mean subjective mortality risk and the objective mortality risk reported by 

the Taiwan Labor Insurance Agency is statistically significant at the 1 percent level. 
37

an order of magnitude smaller than in these countries. Shanmugam (1996/1997, 1997, 

2000, 2001) assessed the wage-risk tradeoff in a variety of studies using survey data of 

manufacturing workers in Madras, India in 1990. The VSL estimates from these studies 

range by nearly a factor of four, even though they reflect the same wage and risk data, 

illustrating how a variety of econometric specifications can produce in some cases a 

range of results. 

5. The implicit value of a statistical injury: U.S. and international estimates 

 Complementing the research on the returns to bearing fatal risks in the workplace, 

a significant number of studies have evaluated the risk premium associated with bearing 

nonfatal job risks. The hedonic labor market studies of nonfatal risk employ the same 

econometric approach as used for mortality risk. As discussed above, some studies that 

attempt to estimate jointly the effects of fatal and nonfatal risks on workers’ wages do not 

find significant effects of risk on wages for at least one of the risk measures. Fatal risk is 

highly correlated with nonfatal risk, so joint estimation may result in large standard errors 

due to collinearity. Omitting one of these variables when estimating the other could 

result in an upwardly biased estimate of the return to that type of risk. 

 Table 5 summarizes 31 studies from the U.S. labor market (Table 5a) and 7 

studies of labor markets outside of the United States (Table 5b) that have found 

statistically significant influences of nonfatal job risk on wages. These studies employ 

three different measures of nonfatal job risks: the overall injury rate, the rate of injuries 

severe enough to result in a lost workday, and the rate of total lost workdays. Studies 

using different measures of nonfatal job risks will generate different risk premiums 
38

because the return to the frequency of injuries (the injury rate) will usually differ from the 

return to the severity of injuries (lost workdays rate). In two studies with specialized 

data, Butler (1983) constructed an injury rate from South Carolina workers’ 

compensation claims, restricting his risk measure to only the more serious work accidents 

and French and Kendall (1992) and French (1990) constructed an injury rate for railroad 

workers based on data collected by the Federal Railroad Administration. As an 

alternative to these objective measures of risk, Viscusi and O’Connor (1984) and Hersch 

and Viscusi (1990) used workers’ own assessments of risk with risk scales based on the 

BLS injury rate and the BLS lost workday accident rate, respectively. These authors 

estimated comparable wage-risk tradeoffs based on subjective risk perception as the other 

studies based on objectively measured industry-level risk. 

 These value of statistical injury studies yield a wide range of estimates, reflecting 

both the differences in the risk measures used as well as whether mortality risk is 

included in the results. While several studies have very high values of injury, such as 

McLean, Wendling, and Neergaard (1978), Leigh and Folsum (1984), and Biddle and 

Zarkin (1988), most studies have estimates in the range of $20,000 – $70,000 per injury. 

The value of statistical injury appears to vary with workers’ preferences over risk, 

consistent with some of the findings based on the mortality risk literature. As a proxy for 

risk attitudes, several studies have used information about workers’ behavior outside of 

the workplace, such as smoking status and seatbelt use, to identify the effect of risk 

preferences on wage-risk tradeoffs. Hersch and Viscusi (1990), Hersch and Pickton 

(1995), and Viscusi and Hersch (2001) all found that smokers have lower injury risk 

premiums than do non-smokers. Hersch and Viscusi as well as Hersch and Pickton also 
39

found that individuals who do not wear seatbelts have lower injury risk premiums than do 

individuals who regularly wear seatbelts. 

 The study by Hersch (1998) is of particular interest because it used genderspecific risk measures. Many previous studies had focused on male samples only 

because estimates using industry-based measures often failed to yield significant risk 

premiums for women. Researchers hypothesized that women did not work in risky jobs 

that would pose health and safety risks. The estimates by Hersch indicate that the 

nonfatal injury risk for women is over two-thirds the size of that for men and that the 

wage-injury risk tradeoff rates are similar for men and women. 

The evidence outside of the United States, while based on a smaller set of studies, 

also indicates significant injury risk premiums. For example, the Cousineau et al. (1992) 

result falls within the U.S. range of about $20,000 – $70,000. However, several other 

Canadian labor market studies provide some estimates that are lower, such as Martinello 

and Meng (1992) and Meng and Smith (1999). The value of statistical injury estimates 

for India are much smaller, likely reflecting the effect of per capita income on wage-risk 

tradeoffs. The low values of statistical injury are somewhat surprising given that these 

same studies generated fairly large values of statistical life. 

6. The effects of income on the value of a statistical life 

 The review of the VSLs above shows that developing countries tend to have 

lower values of statistical life than do developed countries. A variety of factors could 

account for such an outcome, such as cultural influences on risk preferences and 
40

variations in labor market institutions. The dominant cause, however, is most likely that 

developing countries are poorer, and safety is a normal good, as shown in Viscusi 

(1978a). The value of a statistical life should increase with per capita income. To assess 

the relationship between the value of a statistical life and income, we first review several 

meta-analyses of the wage-risk literature. Second, we provide our estimates of the 

income elasticity of the value of a statistical life based on the meta-analysis approaches 

employed in four previous studies with a data set we constructed from our review of the 

papers presented in Tables 2 and 4. Third, we report income elasticities for a number of 

specifications in our preferred meta-analytic approach. 

 Since wage-risk studies employ a measure of income (usually a function of hourly 

or weekly labor earnings) as the dependent variable, an individual study cannot estimate 

the effect of income on the premium for bearing mortality risk. The injury risk study of 

Viscusi and Evans (1990) used experimental data coupled with market evidence to 

estimate income elasticities of injury risk valuations from 0.6 to 1.0. A meta-analysis of 

existing VSL studies can facilitate the calculation of the income elasticity for the value of 

a statistical life. The type of meta-analysis used in the VSL literature attempts to evaluate 

the VSL (the constructed dependent variable) as a function of a number of studies’ 

characteristics (such as mean income of the sample population, mean mortality risk, and 

econometric specification). 

 The published meta-analyses on the value of a statistical life literature vary in 

terms of their sample construction, explanatory variables, and regression technique. Liu, 

Hammitt, and Liu (1997) sampled 17 wage-risk studies surveyed in Viscusi (1993) and 

regressed VSL on income and mean risk. The Liu et al. sample comprised primarily 
41

U.S. wage-risk studies. They reported a statistically insignificant income elasticity of 

0.53. Miller (2000) developed an international sample including more than 60 wage-risk, 

product-risk, and contingent valuation studies. Miller employed a relatively small set of 

explanatory variables, including income. For five models, Miller estimated statistically 

significant income elasticities ranging from 0.85 to 0.96. Bowland and Beghin (2001) 

conducted a meta-analysis with a set of 33 wage-risk and contingent valuation studies 

surveyed in Viscusi (1993) and Desvousges et al. (1995). They matched the data on 

these studies with a variety of country-specific data on demographics, human capital, etc. 

Bowland and Beghin employed robust regression with Huber weights to address concerns 

about the non-normality in the residuals of their data. Bowland and Beghin reported 

statistically significant income elasticities of 1.7 and 2.3. In contrast to the previous three 

papers, Mrozek and Taylor (2002) constructed a sample of about 200 observations 

reflecting multiple VSL estimates from 33 wage-risk studies (eight of which evaluated 

non-U.S. labor markets). They employed the most extensive set of control variables, 

including those characterizing a study’s sample, risk measure, specification, and earnings. 

For two models, Mrozek and Taylor impute statistically significant estimates of 0.46 and 

0.49 for the income elasticity for the value of a statistical life. 

 To further explore the relationship between income and WTP, we have conducted 

a meta-analysis based on the U.S. and international VSLs reported in this paper. Our 

sample includes the VSLs for 49 studies presented in Tables 2 and 4.27 Each study yields 

one observation. Refer to the appendix for a description of the explanatory variables and 

their summary statistics. 

 

27 Studies for which we do not have an income measure were omitted. 
42

We replicated the results from the four previous meta-analysis studies with our 

wage-risk study sample (see Table 6). For the Liu et al. (1997) model, we replicated their 

econometric specification exactly. Miller (2000) reported a number of specifications. 

Only model 3 of Miller’s meta-analysis employed per capita incomes converted to US 

dollars on a purchasing power parity (PPP) basis. Because we constructed all VSLs and 

annual incomes based on a PPP basis, we replicated his model 3. Note that dummy 

variables for contingent valuation surveys and wage-risk studies are unnecessary since 

our data set comprises only wage-risk studies. Bowland and Beghin (2001) reported 

results for linear, log-linear, and trans-log specifications. While they presented very 

limited information about most of their control variables, we have attempted to replicate 

their set of controls. We do not have information on the average age of the sample used 

in the VSL studies, and we have omitted this variable from our specification. We have 

proxied for percent of sample in union-affiliated jobs by accounting for whether the VSL 

study includes union membership as a control and whether the VSL is union-based. Our 

studies do not provide average educational attainment, so we have proxied these values 

with national annual average educational attainment for the over-25 population from 

Barro and Lee (1996). We replicated the robust regressions with Huber weights. Our 

analyses with log-linear and trans-log specifications, however, yielded insignificant 

coefficients on income. We only present the results from the linear robust regression 

model with our data. Mrozek and Taylor (2002) reported results from four specification 

models. We have focused on their model 2 since model 1 yields virtually identical results 

and models 3 and 4 are U.S.-specific. We have included all the control variables that 
43

Mrozek and Taylor report, with the exception of a dummy variable for white collar-based 

VSLs. Our sample does not include any white collar-based VSLs. 

 Three of the four specifications yield statistically significant coefficients on the 

relevant income variable. We found a comparable point estimate with much narrower 

bounds than Liu et al. with their specification, perhaps reflecting our larger sample, as 

their sample is essentially a subset of our sample. We found a smaller coefficient on the 

income variable than Miller with his specification, although a very comparable 

coefficient on the Society of Actuaries risk data dummy variable (equivalent to Miller’s 

variable label “risk beyond workplace” with our data). For comparison with Bowland 

and Beghin’s choice of using “marginal willingness to pay” as their dependent variable 

(apparently equivalent to the VSL expressed in terms of hourly wage instead of annual 

labor income), we modified our dependent variable accordingly. Imputing the income 

elasticity with the linear income coefficient in this model requires the sample means of 

VSL ($3,350 per hour worked, assuming 2000 hours worked per year and $6.7 million 

VSL) and income ($26,006). With 23 explanatory variables and only 41 observations, 

the Mrozek and Taylor specification yields very few precise coefficient estimates. 

While the reported income elasticities from these four studies vary by a factor of 

3, the imputed elasticity point estimates with our data set cover a much smaller range (see 

Table 7). With these studies’ specifications, we found income elasticities from about 0.5 

to 0.6. The 95 percent confidence interval upper bounds fall below 1.0 for two of the 

three statistically significant income elasticities estimated by the specifications outlined 

in these four studies. The apparently large variation in income elasticities in this 
44

literature apparently reflects authors’ choices of studies for inclusion more so than 

choices over control variables, regression techniques, etc. 

 To augment the replications of previously published meta-analysis specifications, 

we evaluated a large number of specifications. Based on the existing literature, we 

focused on two regression techniques, ordinary least squares and robust regression with 

Huber weights. We have varied the control variables from as few as 3 to as many as 18, 

recognizing that coefficient estimates’ precision will decline with the length of the righthand side of the regression equations given our sample size. We chose to include 

explanatory variables of the following types: income and mean risk (common to all 

specifications), type of risk measure, and specification variables. 

The estimated coefficient on the income variable is rather stable across both 

regression techniques and for a wide variation in the number of control variables (see 

Table 8). For the OLS specifications, the income elasticity varies from 0.49 to 0.60. The 

95 percent confidence intervals never range below 0.2 and never exceed 0.95. For the 

robust regression specifications, the income elasticity varies from 0.46 to 0.48. The 95 

percent confidence intervals never fall below 0.15 and never exceed 0.78.28 The income 

coefficients in all specifications are statistically significant at the 1 percent level. 

 Based on the approximately 50 wage-risk studies from 10 countries, we can 

conclude from these results that the income elasticity for the value of a statistical life is 

 

28 We also estimated a regression with the Mrozek and Taylor set of control variables, with an important 

exception of replacing the hourly earnings variable with the natural logarithm of annual income. With an 

OLS specification, we estimated an income elasticity of 0.76 with a 95 percent confidence interval of 0.20 

– 1.32 (n=41). In the robust regression with Huber weights specification, we estimated again a point 

estimate for the income elasticity of 0.76, but with a much tighter 95 percent confidence interval of 0.73 – 

0.79 (n=38). The income variable was statistically significant at the 5 percent level in the OLS regression 

and significant at the 1 percent level in the robust regression. The iterative weighting process in robust 

regression with this much larger set of control variables effectively eliminated several low-VSL studies 

relative to specification 6 in Table 8, which may have resulted in the larger elasticity. 
45

less than 1.0. Across a number of specifications with our data, our point estimates of the 

income elasticity range between about 0.5 and 0.6. Note that in none of our 

specifications did the income elasticity’s 95 percent confidence interval upper bound 

exceed 1.0. 

 Current practice by regulatory agencies is effectively in line with these findings. 

The U.S. EPA (1999) accounted for income growth into the 22nd century in monetizing 

the long-term mortality risk reduction benefits from stratospheric ozone protection.29 In a 

regulatory context, the U.S. EPA (2000b) also accounted for income growth over a thirtyyear period in monetizing the mortality risk reduction benefits from reduced particulate 

matter pollution associated with the diesel sulfur standard for heavy-duty trucks. In both 

these cases, the U.S. EPA employed an income elasticity of 0.4 and conducted sensitivity 

analyses with a low-end elasticity of 0.1 and a high-end elasticity of 1.0. If our results of 

the income elasticity apply over time, the point estimate chosen by the EPA is reasonable, 

although more narrow bounds may be appropriate for the sensitivity analysis. 

 The meta-analysis regressions can also serve to characterize some of the 

uncertainty in value of a statistical life estimates. We constructed the mean predicted 

VSL values presented in the last two rows of Table 8 by first using the estimated 

coefficients from the meta-analysis regressions to predict the natural logarithm of VSL 

for each study. Then we converted each predicted log(VSL) to a predicted VSL. We 

averaged these over all studies that were included in each regression model’s sample to 

produce the average values reported in the table. We constructed the average 95 percent 

confidence intervals by first estimating the prediction error for each study from the metaanalysis regressions. We then used this prediction error to construct study-specific 95 

 

29 The Science Advisory Board (2000) has supported EPA’s adjustment of the VSL for income growth. 
46

percent confidence intervals. The values for the lower and upper bounds of these 

confidence intervals were then averaged over all the studies in the regression sample. 

The U.S. specific results are based on regression samples that include non-U.S. studies; 

only the averaging is U.S.-specific.30

 The mean predicted VSLs from the meta-analysis regression models for the whole 

sample vary from $5.0 to $6.2 million, and from $5.5 to $7.6 million for the U.S. sample. 

An assessment of median predicted VSLs produced very similar results. For most 

regression models, the 95 percent confidence interval upper bound is double or more than 

the 95 percent confidence interval lower bound. Note that the small number of degrees of 

freedom in specifications 3 and 6 yielded very large bounds around the measure of 

central tendency. While this analysis can characterize some of the uncertainty around the 

value of a statistical life, several caveats are in order. First, the description of uncertainty 

presumes that the proper VSL meta-analysis model has been specified. Otherwise, the 

bounds around the predicted means are not valid. Second, this assessment of uncertainty 

regards the value of a statistical life constructed from a sample of wage-risk studies of 

prime-aged workers. Policy applications of these VSLs in benefits transfer should 

consider appropriate modifications to the VSL point estimate and the distribution around 

it. 

 

30 We also calculated the median predicted VSL values for the full and U.S. samples. For the full sample, 

the median predicted VSL never exceeded the mean predicted VSL in any of the six regression models by 

more than 14 percent, and the mean predicted VSL never exceeded the median predicted VSL by more than 

15 percent. For the U.S. sample, the median predicted VSL exceeded the mean predicted VSL in all six 

regressions, but never by more than 7 percent. 
47

7. The effects of union affiliation on the value of a statistical life 

 Since the U.S. and international evidence from labor markets and other product 

markets demonstrate a significant wage-risk tradeoff, numerous researchers have 

explored several factors that may influence the compensating differential for risk. The 

relationship between union affiliation and the wage-risk tradeoff has received substantial 

attention in the literature. Most studies of the U.S. labor market find that union 

affiliation is positively correlated with a greater wage-risk tradeoff while the international 

evidence is much more mixed. 

 Workers in union jobs may enjoy an additional premium for bearing risk greater 

than those in nonunion jobs for several reasons. First, if firms face an upward sloping 

labor supply curve, then the absence of collective bargaining may result in inefficiently 

low level of workplace safety. Viscusi (1980) shows that if the marginal worker’s 

valuation of workplace safety differs from the average worker’s valuation, then the firm 

would provide a suboptimal level of safety. If the marginal worker is willing to accept 

less of a decrease in wage for an incremental improvement in safety than the average 

worker, then workplace safety would be too low. If the marginal worker tends to be 

younger and less experienced while the average worker tends to be older and wealthier, 

then the average worker with greater wealth and family obligations may have a greater 

preference for workplace safety than the marginal worker. In light of this inefficiently 

low provision of safety, unions may bargain over workplace safety in addition to wages 

and other benefits on behalf of the inframarginal workers who may place greater value on 

risk reduction than the marginal worker. 
48

 A second, and not entirely unrelated phenomenon, is that workplace safety may 

be a quasi-public good and suffer the common under-provision associated with such 

goods due to free-riding (Dillingham and Smith 1984). If a firm provides some level of 

workplace safety (e.g., a fire extinguishing system), then one worker’s consumption of 

this safety does not preclude another worker from enjoying the same consumption. Since 

safety is quasi-public, a worker lacks the incentive to truthfully reveal his or her 

preference for safety, especially since an increase in safety would likely correspond to a 

decline in the wage (or some other benefit). Collective action can overcome such freeriding. In this case, collective bargaining by a union with a firm could reflect all 

workers’ true preferences for safety, and result in higher aggregate demand for safety by 

workers than what would be expected in a non-union setting. This higher demand would 

translate into greater compensating differentials for job-related injury and fatality risk. 

 Third, if some workers lack adequate information about the safety at their 

workplace, then they may underestimate the actual risks they face. Workers 

underestimating their on-the-job risk would demand lower wages than if they held correct 

perceptions of risk. Unions potentially could provide workers with more accurate 

information about their on-the-job risks (Viscusi 1979, Olson 1981). Unions can take 

advantage of economies of scale in providing information not available to unorganized 

non-union workers (Dillingham and Smith 1984). Unions may also negotiate for 

mechanisms that increase worker exposure to safety information. For example, Olson 

noted that a 1976 BLS survey showed that 36 percent of all workers covered by a 

collective bargaining agreement worked in establishments that sponsored joint firmworker safety committees. 
49

 To evaluate the extent to which these factors influence compensating differentials 

for on-the-job risk, researchers have taken two estimation approaches (see Table 9). 

First, some have split their samples into union and non-union subsamples and estimated 

the wage regressions separately for each subsample. Second, others have included an 

interaction term, risk variable x union dummy variable, in the wage regression model. In 

our review of papers evaluating union effects, only Olson (1981) assessed the union-risk 

premium with both the separate sample regression approach and the risk-union 

interaction approach. Both approaches yield substantial differences in compensating 

differentials for union and non-union members. 

 Regardless of estimation strategy, most assessments of the U.S. labor market 

found higher risk premiums for union workers than for non-union workers (see Table 9a). 

Of the ten U.S. labor market value of life studies we reviewed that evaluated the role of 

unions in risk premiums, nine found union workers enjoyed greater compensating 

differentials for bearing risk than nonunion workers. In contrast to accepted theory, 

several of these papers found that non-union workers had insignificant or statistically 

significant negative compensating differentials for risk.31

Table 9b summarizes the rather mixed effects of unionization on premiums for 

nonfatal risks in the U.S. labor market. In the studies that controlled for fatal risks, the 

compensating differential for injury risk for nonunion workers often exceeded the 

differential for union workers, even in the same studies where the union fatality risk 

premium was greater (e.g., Olson 1981, Dorsey 1983, Dorsey and Walzer 1984). Olson 

 

31 For example, Dorsey (1983) estimated a statistically significant negative coefficient on the fatality risk 

variable and a statistically significant positive coefficient on the union x risk interaction variable. This 

combination (with the union x risk coefficient greater in magnitude than the fatality risk coefficient) results 

in a positive compensating differential for union workers and a negative differential for non-union workers. 
50

found that union workers enjoyed a greater positive premium than nonunion workers for 

injury incidence but a negative premium for injury duration (number of lost workdays), in 

contrast to nonunion workers’ positive premium. 

In regressions that did not include a variable for fatality risk in the estimation 

model, the findings reflected the results for fatality risk discussed above. Hamermesh 

and Wolfe (1990) found that the extra compensating differential for union workers 

reflected injury incidence, while injury duration was insignificant, similar to Olson. 

While Freeman and Medoff (1981) could not discern between the statistically significant 

injury risk premiums for union and non-union workers, Smith (1983), Biddle and Zarkin 

(1988), Fairris (1992), and Hersch and Pickton (1995) all found greater compensating 

differentials for nonfatal risks for union than for nonunion workers. 

 While the majority of the U.S. research illustrates greater risk premiums for union 

workers, the research on labor markets in other countries often reveals a more ambiguous 

union impact on risk premiums. Marin and Psacharopoulos (1982) conducted the first 

analysis of compensating differentials for risk in the U.K. labor market, and found that 

union affiliation had an insignificant impact on the risk premium. In an attempt to 

replicate this initial study, Arabsheibani and Marin (2000) also could not find any 

evidence supporting a union-risk premium based on membership or union strength. In 

contrast, Siebert and Wei (1994) found higher union risk premiums when accounting for 

potential endogeneity of risk. Subsequent research by Sandy and Elliott (1996) countered 

this finding with analysis indicating larger compensating differentials for risk for 

nonunion members. These four researchers collaborated in a follow-up study, Sandy et 
51

al. (2001), which concluded with a qualified claim that nonunion workers enjoy greater 

risk premiums. 

 For the Canadian labor market, several analyses have found little support for a 

positive impact of union affiliation on compensating differentials for risk. In a series of 

analyses with union x fatality risk interaction terms, Meng (1989, 1991), and Martinello 

and Meng (1992) found no significant effect for accident risk, while Meng and Smith 

(1990) found a negative statistically significant coefficient that when combined with the 

fatality risk coefficient would translate into no compensating differential for union 

members (in contrast to the positive risk premium for non-union members). In contrast, 

Cousineau et al. (1992) found for a large Quebec sample that union members received 

larger premiums for both fatality risk and injury incidence, but a smaller premium for 

injury duration, than nonunion members. Further, Lanoie et al. (1995) found significant 

positive compensating differentials for perceived risk for the union sub-sample from their 

survey of Montreal work establishments. 

Finally, the few analyses of developing country labor markets have also found 

mixed effects of union affiliation on a worker’s risk premium. In his analysis of the 

Indian labor market, Shanmugam (1996-7) included a union x fatality risk interaction 

term, and found that union members alone enjoy a compensating differential for risk. For 

the South Korean labor market, Kim and Fishback (1999) could not statistically discern 

compensating differentials for risk between union and non-union workers. 
52

8. The effects of age on the value of a statistical life 

 Evaluating wage-risk tradeoffs in labor markets to estimate the value of a 

statistical life raises the important question of whether life expectancy affects the value of 

a statistical life. Age affects the duration of life at risk and also may be correlated with 

other variables that affect one’s willingness to bear risk, which are not age effects per se 

but rather reflect changing preferences over the life cycle. Numerous analyses have 

shown that the magnitude of the VSL is a decreasing function of age, whereas the value 

of any particular year of life may increase with age (Rosen 1988).32 Wholly apart from 

life expectancy effects, accounting for the effect of age in the utility function in terms of 

deteriorating health and quality of life would have generated similar results.33 

 Using this framework, Rosen undertook several simulations based on his previous 

empirical work (Thaler and Rosen 1975). He estimated that the value of a statistical lifeyear, which is equal to the expected consumer surplus for another year, for the averageaged individual in the sample ranges from about $31,000 to $130,000, based on discount 

rates ranging from 0 to 12 percent. Using data on life expectancy by age, Rosen 

calculated the value of a statistical life for comparable individuals who varied from 36 

years to 48 years of age. The 48-year old’s VSL is 10 percent less than the 36-year old’s 

VSL. 

 Several researchers have undertaken direct empirical estimates of the effect of age 

on the return to risk in hedonic labor market analysis. A simple approach to estimating 

 

32 Rosen’s research yields results similar to the Jones-Lee (1976), Jones-Lee et al. (1985), and Shepard and 

Zeckhauser (1982, 1984) finding that the value of a statistical life takes an inverted-U shape with respect to 

age. Also refer to Garber and Phelps (1997) for an assessment of quality-adjusted life-years (QALYs) in 

the cost-effectiveness literature. Jenkins et al. (2001) provide some evidence that the value of a statistical 

life is increasing over childhood through the pre-retirement years. 33 Modifying utility functions in this way would be analogous to constructing QALY measures, common to 

valuations based on health status in the health economics literature. The QALYs are usually based on 

stated preference methods (see Cutler and Richardson 1997). 
53

the effect requires interacting the mortality risk and age variables in the regression model. 

While some researchers did not identify significant coefficients to this interaction (Meng 

and Smith 1990 and Shanmugam 1996/7, 2001), several others found statistically 

significant and negative estimates broadly consistent with the theory (see Table 10). 

Research by Thaler and Rosen (1975), Viscusi (1979), Arnould and Nichols (1983), and 

Moore and Viscusi (1988b) yielded the negative relationship between age and the return 

to risk. Within the context of a local housing market, Portney (1981) found that the value 

of a statistical life based on trading off mortality risk associated with local air pollution 

exposure and housing prices declines significantly with age: an individual under the age 

of 45 has a VSL 20 times greater than an individual over the age of 65. 

Dillingham, Miller, and Levy (1996) employed a modified approach by focusing 

on the willingness to pay to avoid a fully impaired worklife, where death is the extreme 

case of impairment. The authors construct a risk variable that reflects injury frequency, 

severity, and probability of fatality. They assume that a worklife shortened by a fatal 

injury is equivalent to a worklife shortened by a permanently disabling injury. Their 

“value of remaining worklife” ranges from $3.1 - $4.7 million for the whole sample, 

although it decreases with age. For example, they estimate that a 50-year old values 

remaining worklife at half the value held by a 30-year old. 

Some researchers have proposed a value per discounted expected life-year 

approach (Moore and Viscusi 1988b). In lieu of a value of a statistical life, one could 

adjust the VSL measure for the life expectancy of individuals by essentially annuitizing 

the estimated VSL. Based on actuarial tables, one could estimate the life expectancy for 

the average-aged individual in a study and then develop estimates of the life-year value 
54

(either by assuming a discount rate, or as in several studies, estimating a discount rate 

based on revealed preference procedures – see below). Then this life-year could be 

applied to other situations where life expectancy would differ from the average in the 

study in question. This approach provides an “age-adjusted” or “life expectancyadjusted” VSL alternative to the standard VSL. While the life-year approach does 

address the concern that values of a statistical life should vary with life expectancy, they 

assume that the marginal value of another year is constant across the age spectrum and 

across time for a given individual.34

 Accounting for the effects of age on the value of a statistical life through a lifeyear approach requires the discounting of future consumer surplus (since, on the margin, 

the compensating differential should equal the present discounted value of a worker’s 

expected consumer surplus). A variety of papers have imputed workers’ or consumers’ 

implicit rates of discount (Moore and Viscusi 1988b, 1990a, 1990b, 1990c, Viscusi and 

Moore 1989, Dreyfus and Viscusi 1995). While the Moore and Viscusi (1988b) and 

Dreyfus and Viscusi papers estimate implicit discount rates and marginal discounted lifeyears based on reduced form models, the other three papers develop structural models to 

estimate discount rates. Also note that all of these models are based on data from labor 

markets, except for the Dreyfus and Viscusi’s automobile hedonic study. Rational 

individuals can implicitly discount their health capital at a different rate than what they 

face in markets (e.g., real interest rate for a home mortgage) since health status is a nontraded commodity (one cannot “save” good health at age 25 for consumption at age 75). 

 

34 Based on Rosen’s life cycle model, one would expect that the marginal value of another year is greater 

for an elderly person than for a middle-aged person but that the value of all future years declines with age 

for a given individual. Accounting for health status may counter the effect of increasing marginal values 

for a one-year life extension for an elderly person. If health status is decreasing in age, then it may be 

ambiguous whether the marginal value of another year increases with age. 
55

Despite this possibility, these models estimate discount rates that are broadly consistent 

with the real rates of interest typical workers and consumers face (see Table 11). 

9. The application of the value of a statistical life to public policy decisions 

 At least in the countries with a high level of development, governments 

recommend or require economic analyses of proposed regulations and public policies.35 

Regulatory agencies in the United States, the United Kingdom, and Canada, have been 

most prominent in their use of VSL estimates to value the benefits of proposed 

environmental, health, and safety rules. In some cases, regulatory agencies have 

modified the VSL to account for distinctive characteristics of the risk and the affected 

population. In the United States, some environmental laws preclude the promulgation of 

regulations based on benefit-cost analysis. In these cases, analysts have turned to riskrisk analysis – based in part on an application of the value of a statistical life – to provide 

some guidance about whether a proposed policy is in fact risk reducing. 

9.1 The use of VSLs in government decision-making around the world 

9.1.1 United States. Over the past twenty years in the United States, executive orders by 

Presidents Carter, Reagan, and Clinton have mandated economic impact analyses of all 

significant Federal regulations (E.O. 12044, E.O. 12291 and E.O. 12866). Beginning 

with the Reagan Administration, these executive orders vested with the Office of 

Management and Budget (OMB) the responsibility for overseeing and coordinating the 

review of regulatory impact analyses. OMB has published guidelines for all Federal 

agencies, such as its report with respect to the use of “best practices” in these analyses 

 

35 For example, refer to the guidance to improve regulatory decision-making provided by the OECD (1995) 

to its member countries. 
56

(U.S. OMB 1996). The guidance recommends the use of a value of a statistical life to 

monetize the benefits associated with rules that change the population’s mortality risk. 

While OMB does not recommend a specific VSL or set of VSLs, it does provide a 

discussion of the issues agencies should consider when choosing a VSL based on the 

current state of knowledge. 

Until the 1980s the dominant policy approach to valuing the benefits of reduced 

risks of death was based on various human capital measures, such as the present value of 

lost earnings and medical expenses. These values are lower than the VSL amounts. 

Typical of this approach was the 1982 analysis by OSHA of its proposed hazard 

communication regulation. OSHA valued lives saved based on the cost of death, which 

was the human capital value, because in its view life was too sacred to value. After OMB 

rejected the regulation, claiming that the costs exceeded the benefits, OSHA appealed the 

decision to then Vice-President Bush. W. Kip Viscusi was asked to settle the economic 

dispute between the two agencies. By valuing life properly using a VSL, the estimated 

benefits exceeded the costs. The regulation was approved the day after his analysis 

reached the Reagan White House.36 Thus, the historical impetus for the adoption of the 

VSL methodology was that these values boosted assessed benefits by roughly an order of 

magnitude, improving the attractiveness of agencies’ regulatory efforts. 

The flexibility provided to U.S. agencies in choosing a VSL appropriate to the 

population affected by their specific rules has resulted in significant variations in the 

selected VSL both across agencies and through time (see Table 12 and Adler and Posner 

2000). In addition, some regulatory impact analyses have included a range of benefits 

reflecting different assumptions about the VSL, often reflecting the age profile of the 

 

36 For a review of the substance of this analysis, see Viscusi (1992a). 
57

affected population by using a VSL adjusted by the number of life-years saved (e.g., the 

FDA rule restricting tobacco sales to children, 61 FR 44396, and the EPA rule regulating 

the sulfur content of gasoline, 65 FR 6698). The U.S. Environmental Protection Agency 

(EPA), responsible for more costly Federal rule-makings than any other U.S. agency, has 

developed economic guidelines for its regulatory impact analyses (U.S. EPA 2000a). 

The EPA guidelines recommend a VSL of $6.2 million (2000US$), reflecting the 

arithmetic mean of 26 studies reviewed in Viscusi (1992a). 

In contrast, the U.S. Federal Aviation Administration (1998) recommends a value 

of a statistical life of $3 million in its 2002 economic analyses of regulations.37 This 

comparatively low value of life may reflect in part an anchoring effect. The U.S. 

Department of Transportation was a leader in valuing mortality risk reductions, but began 

doing so in an era in which the present value of lost earnings was the dominant approach. 

The agency has slowly increased the value attached to reduced risks of death, but it has 

continued to lag behind the estimates in the literature.38

9.1.2 United Kingdom. In the United Kingdom, the Cabinet Office has likewise provided 

guidance for economic analyses for the government’s regulatory and policy-making 

agencies (U.K. Cabinet Office 2000, H.M. Treasury 1997). While the guidance does not 

specify the value of a statistical life to be used by agencies, it does recommend careful 

consideration of the challenges in applying values estimated in the economic literature to 

potentially different risk and population contexts of the policy or regulation. 

 

37 Refer to Table E-1 at Internet: http://www.api.faa.gov/economic/EXECSUMM.PDF and 

http://www.api.faa.gov/economic/742SECT2.PDF. 38 The low VSL used by the agency also reflects a political dimension as well. In the early 1990s when 

Viscusi prepared the report that was subsequently published as Viscusi (1993) for the Federal Aviation 

Administration, that branch of the agency favored a higher value of life than was later mandated for use 

throughout the department. 
58

The U.K. Department of the Environment, Transport, and Regions (DETR; 

formerly Department of Transport) has employed a willingness-to-pay based value of 

preventing a fatality since 1988 in its regulatory and policy analyses (Chilton et al. 1999). 

The value of preventing a fatality selected in 1988, $1.2 million (£500,000 in 1987 

prices), is still used by the Department. This value reflects contingent valuation-based 

estimates of individuals’ willingness-to-pay for risk reduction. The U.K. Health and 

Safety Executive uses the DETR value of preventing a fatality as a starting point for its 

regulatory impact analyses. The HSE has employed a value of preventing a fatality 

double the DETR value for cancer-related fatalities, concluding that individuals’ dread of 

the disease significantly outweighs the affects of latency on willingness-to-pay (Andrews 

and McCrea 1999).39

Whereas U.S. agencies rely on market-based VSL estimates, in the U.K. the 

emphasis is on contingent valuation estimates. This difference in approach no doubt 

stems in part from the different character of the empirical evidence in the two countries. 

There have been dozens of studies of U.S. wage-risk tradeoffs, most of which have been 

in a reasonable range. There have been far fewer such analyses for the U.K., and the 

resulting empirical estimates have been much more unstable. 

9.1.3 Canada. In Canada, the Privy Council Office published guidelines for benefit-cost 

analysis in 1995. While these guidelines do not specify one or a set of values of a 

statistical life, they do note the need for serious consideration of the value of life, 

 

39 Note that the Science Advisory Board to the U.S. Environmental Protection Agency considered the 

effects of latency and dread within the context of cancer-related mortality. The SAB (2000) concluded that 

the literature does not currently support a modification of the value of a statistical life to reflect dread. In 

contrast, the SAB did recommend that economic analyses account for latency by discounting future 

mortality to the present time consistent with the approach to other categories of benefits and costs. 
59

determinants of the appropriate VSL, and possible approaches to presenting useful 

information for policymakers and the public. The value of a statistical life is left to the 

discretion of agencies promulgating regulations, again much like the U.S. and U.K. 

approaches. 

Transport Canada reviewed the economic analyses for 145 transportation-related 

projects over 1982 – 1993 (Blanchard 1996). The VSL used in these analyses ranged 

from $400,000 to $3.2 million. A recent analysis of a Canadian proposed rule on tobacco 

products information used a range of the value of a statistical life of $1.7 - $5.7 million,40

with higher values for individuals under age 65 (Hara Associates 2000). A comparable 

range and age-based VSL differential was employed in an evaluation of a Canadian 

proposal for cleaner vehicles in fuels as well (Lang et al. 1995). 

9.1.4 Multinational organizations. The value of a statistical life has also received 

attention in multinational contexts. The Intergovernmental Panel on Climate Change 

(IPCC), established by the United Nations Environment Programme and the World 

Health Organization in 1988 to provide technical support to participants in global climate 

change negotiations, discussed issues regarding the value of a statistical life in its 1995 

and 2001 assessments of the economic and social dimensions of climate change (IPCC 

1996, 2001). Moreover, the European Commission (EC) began in 2000 a process to 

prepare guidance for benefits analysis to improve benefit-cost analysis procedures within 

the EC. The EC effort has focused a substantial amount of time and resources on the 

value of a statistical life question (EC 2000). 

 

40 The conversion to U.S. dollars for the Hara Associates analysis was based on the annual market 

exchange rate reported by the Federal Reserve (refer to Internet: 

http://www.federalreserve.gov/releases/g5a/current/) because the Penn World Table does not provide 

conversions for the most recent years. 
60

9.2 Issues Pertaining to Benefits Transfers 

 The vast majority of the studies reviewed in this paper cover prime-aged workers 

who have chosen to bear the risk of accidental, immediate death. Many of the studies 

based their quantitative analyses on samples from the 1970s and 1980s. With the 

exception of occupational safety policies, most government regulations affect mortality 

risks with different qualitative characteristics or affect populations with characteristics 

that differ from those of the samples in these studies. The demographic characteristics of 

specific groups and the population at large likely differ today (and will differ in the 

future) from what they were 20 to 30 years ago. The risk-money tradeoffs of those 

affected by government policy may differ from those values estimated in hedonic market 

studies. 

For example, many environmental regulations address carcinogen exposure. 

Reduced cancer-related mortality presents several issues that merit consideration when 

employing a VSL. First, individuals may be willing to pay more to avoid dying of cancer 

than to avoid an instantaneous accidental death. Revesz (1999) hypothesizes that this 

“dread” effect of cancer mortality should result in an upward revision to the VSL. This 

reasoning apparently underlies the higher VSL used by the UK Health and Safety 

Executive for cancer-related fatalities. The EPA’s Science Advisory Board (2000) 

recommended against any “dread”-related modification to the value of a statistical life on 

the grounds that the current literature did not support any such change. 41 In particular, 

contingent valuation estimates of cancer mortality risks have produced values similar to 

 

41 While Revesz suggests other adjustments to the value of a statistical life based on the qualitative 

characteristics of the risk, such as involuntariness of the risk, the Science Advisory Board (2000) noted that 

the existing literature does not justify adjustments for these effects. 
61

those observed for accidental deaths.42 The market-based evidence for cancer risks 

discussed above is similar to the results for accident risks. 

 Cancer-related mortality also differs significantly from occupational mortality in 

terms of the timing of the death. The long latency period for cancer (and other chronic 

health conditions affected by government regulations) should be discounted in the 

benefits analysis (Revesz 1999, Science Advisory Board 2000). Given the rough 

similarity of the implicit rates of discount discussed in Section 8 to current market 

interest rates, a benefit-cost analysis could employ one common discount rate for all 

categories of benefits and costs. 

 Several researchers have questioned the discounting of benefits of reducing future 

risks to life when evaluating public policies. Heinzerling (1998, 1999, 2000) has 

criticized the discounting of statistical lives on moral grounds. She claims that a 

statistical life cannot be discerned from an actual life, which society would not attempt to 

price. This concern pertains to both current and future risk reductions and is not a 

discounting argument per se. While Heinzerling expresses significant concerns with the 

concept of statistical lives and in their discounting, individuals make private risk-income, 

risk-time, and risk-risk tradeoffs every day, as evidenced by the literature surveyed in this 

paper, as well as by simple casual observation.43 Further, as the studies presented in 

Table 11 illustrate, individuals value risks in the future less than they value 

commensurate risks they face today. Both Revesz and Heinzerling raise concerns about 

 

42 See Magat, Viscusi, and Huber (1996). 

43 This discussion implicitly acknowledges an important aspect of most occupational, safety, and 

environmental health risks: these risks are relatively small. In cases of certain, or near-certain death, the 

empirical economic evidence and this argument are not relevant (see our discussion in Section 1). 
62

discounting statistical lives in future generations (also see Arrow et al. 1996 for a survey 

of intergenerational discounting). 

 Failure to discount creates potentially fundamental paradoxes (see Keeler and 

Cretin 1983). Suppose that the cost structure for reducing risks to life is unchanged over 

time. Then it is always desirable to defer any life saving policy and invest the money that 

would have been spent on risk reduction. Indeed, continual postponement of a life saving 

effort is always desirable if future benefits are never discounted but the money spent on 

these policies continues to grow at the rate of interest. 

 Similarly, suppose that future life saving benefits are not discounted. If, however, 

one converts the cost allocations to a terminal value rather than discounting the benefits 

back to a present value, the effect will be identical in terms of whether the policy passes a 

benefit-cost test. What the discounting critics generally fail to grasp is that what is being 

discounted are not lives but rather society’s willingness to pay for these future risks to 

life. 

 The differences in the age of the population in labor market studies and of the 

population affected by a regulation should also affect the value of a statistical life. While 

fewer studies have focused on the effects of age on the return to risk, those that have tend 

to find that the value of a statistical life declines with age, consistent with the theoretical 

work. Since these labor hedonic studies include samples where age usually ranges no 

higher than about 60 years, it may be difficult to extrapolate these results for older 

populations. This is a significant issue in benefits transfer, since many environment and 

public health policies (as opposed to worker safety programs) deliver benefits to the 

elderly. For example, recent air quality regulations promulgated by the U.S. 
63

Environmental Protection Agency disproportionately benefit older individuals as 

illustrated by the average increase in life expectancy of less than 15 years (U.S. EPA 

1999, Science Advisory Board 1999a). In response to this, the EPA’s Science Advisory 

Board (SAB) has questioned the appropriateness of EPA’s choice of a VSL for this 

population. The SAB notes that it “question[s] the application of a WTP estimate for 

prime-aged individuals to a population of older individuals and people who are in poor 

health” (p. 6). The SAB (1999b) has recommended that the EPA revise its VSL in light 

of this concern. 

 Several agencies already employ age-adjusted VSLs. As noted above, the 

economic analysis of a Canadian tobacco regulation employed a smaller VSL for 

individuals older than 65. The U.S. Food and Drug Administration regularly employs the 

value of statistical life-years, instead of VSLs, as a way to monetize the health benefits of 

their proposed rules. The U.S. Environmental Protection Agency has reluctantly 

conducted benefit-cost analyses with the value of statistical life-years approach. The 

clear findings in the theory and existing empirical evidence support such age adjustments. 

Future empirical research should further refine age-specific estimates of the value of a 

statistical life for use in regulatory analyses. 

9.3 The role of risk-risk analysis 

While the value of a statistical life can be used to monetize the benefits for risk 

policy evaluation, most laws do not require that agencies undertake such balancing and 

some laws in the United States actually preclude the consideration of benefit-cost 

analysis in setting standards. For example, the courts have interpreted the Clean Air Act 

such that the U.S. Environmental Protection Agency cannot base ambient air quality 
64

standards on benefit-cost analysis or an assessment of the costs.44 Even if a benefit-cost 

test cannot be applied, policies that on balance harm individual health presumably should 

not be adopted. Several forms of risk-risk analysis can account for these various risk 

effects of policies in an effort to ascertain whether risk regulations on balance are risk 

reducing. While risk-risk analysis cannot determine if a policy improves societal welfare, 

it can identify the policies that clearly do not improve societal welfare because they result 

in a net increase in mortality risk. 

 Four types of risk-risk tradeoffs could influence the net effect of a policy or 

regulation on a population’s risk exposure. First, a policy may reduce risks of one type 

while increasing risks of another type. For example, the U.S. Food and Drug 

Administration considered banning saccharin, the artificial sweetener, in response to an 

animal study finding that it may be a potential human carcinogen. However, banning 

saccharin would likely increase the risks associated with obesity. In this case, the U.S. 

Congress allowed the use of saccharin in foods subject to a warning label on products 

containing the sweetener (Viscusi 1994b). 

Second, policies to reduce risks may create incentives for individuals to undertake 

less individual effort to reduce their exposure to risks (moral hazard). These behavioral 

responses will offset some of the risk reduction of the policy, and could potentially 

increase net risks. For example, Peltzman (1975) described how drivers responded to 

mandated safety devices in automobiles by changing their driving behavior. Drivers in 

 

44 For example, the 1997 final rule for the national ambient air quality standard for ozone introduced the 

section on the regulatory impact analysis with the following: “As discussed in Unit IV of this preamble, the 

Clean Air Act and judicial decisions make clear that the economic and technological feasibility of attaining 

ambient standards are not to be considered in setting NAAQS, although such factors may be considered in 

the development of State plans to implement the standards. Accordingly, although, as described below, a 

Regulatory Impact Analysis (RIA) has been prepared, neither the RIA nor the associated contractor reports 

have been considered in issuing this final rule” (62 FR 38856). 
65

“safer cars” drove more recklessly than before. The empirical evidence showed that 

while the safety measures reduced fatalities among automobile occupants, these gains 

have been offset at least in part by increases in pedestrian deaths and nonfatal accidents. 

 Third, risk-reduction policies may result in regulatory expenditures that directly 

increase fatalities. For example, policies to remove asbestos from buildings may increase 

asbestos exposure by workers and use of excavation equipment at Superfund hazardous 

waste sites may result in construction-related injuries and fatalities (Graham and Wiener 

1995). Viscusi and Zeckhauser (1994) illustrate through an input-output analysis the 

total direct and indirect injury and fatality risks associated with expenditures by industry. 

Regulations that require a reallocation of resources to industries with higher risks, for 

example towards construction to build new wastewater treatment facilities or to install 

pollution control technologies would result in an offsetting increase in mortality risk. 

 Fourth, the costs of risk-reduction policies decrease income available to finance 

other health and safety expenditures. Wildavsky (1980, 1988) noted that the costs of 

risk-reduction policies reduce national income, some of which would otherwise be used 

to promote health and safety. This argument focuses on the correlation between income 

and health, evident in both international cross-sectional data and U.S. time-series data 

(Viscusi 1983, 1994b, Graham et al. 1992, Lutter and Morrall 1994; see Smith et al. 1994 

for a critique of the international evidence).45 These analyses illustrate that wealthier 

countries have lower mortality rates associated with greater health and safety 

investments. Graham et al. (1992) extended the previous analyses on the mortalityincome relationship by focusing on the effect of permanent income. They conclude that: 

 

45 This relationship is also revealed within a cross-section of the U.S. population. Viscusi (1978b) found 

that work-related risk exposure decreases with worker wealth. 
66

“If government regulation reduces the level (or the rate of growth) of permanent income, 

it is likely to cause smaller health investments and an eventual decline in health status 

compared to what would have occurred without the economic burdens of regulation” (p. 

336).46

 Keeney (1990, 1994, 1997) formalized Wildavsky’s proposition and found that 

some expensive regulations aimed at reducing mortality risks actually increase mortality 

risks by reducing national income. Keeney’s model yields various estimates for the 

induced-expenditure associated with an additional fatality that depend on the distribution 

of the burden of the policy costs. Based on the mortality-income relationships estimated 

by Kitawaga and Hauser (1973) and Frerichs et al. (1984), Keeney estimates that between 

$13.6 million and $15.2 million of expenditures would induce a fatality, assuming costs 

are borne proportional to income.47, 48 This research inspired a legal opinion of an OSHA 

regulation in the early 1990s.49 U.S. Federal Appeals Court Judge Steven F. Williams 

wrote that regulations that do not pass a risk-risk analysis would be counterproductive. 

The U.S. Office of Management and Budget then suggested to OSHA that it consider 

risk-risk analysis in conducting its regulatory impact analyses. 

 While the research on the income-mortality relationship influenced several policy 

discussions in the early 1990s in the United States, the results from these studies point 

 

46 Portney and Stavins (1994) question whether the income losses from regulations would reduce the 

population’s health status. They claim that the nonlinear relationship between income and health and the 

modest impact of most regulations on the economy would not likely have a significant effect on health 

status. While individual regulations may involve small costs as a share of the economy, it is important to 

note that environmental regulations alone cost about 1 – 2 percent of U.S. economic output (U.S. OMB 

2001). 

47 We have updated the reported values in Keeney (1990) to 2000 U.S. dollars using the CPI-U deflator. 

48 The Keeney estimates are near the high end of 11 studies cited in Lutter and Morrall (1994) on the 

income gains necessary to avert one fatality. They are very similar to Lutter and Morrall’s estimated range 

of $11.4 million to $15.2 million for the United States. 

49 Refer to UAW v. OSHA, United States Court of Appeals for the District of Columbia Circuit, 89-1559. 
67

towards a peculiar inconsistency. For the studies with expenditure-induced fatalities on 

the order of $5 million per fatality, the expenditure associated with a loss of one life is on 

par or perhaps below what individuals are willing to pay to reduce the risk of one fatality 

in the population based on the labor market hedonic studies (Viscusi 1994b). Moreover, 

the mortality-income studies also suffer several other potential problems. Some of these 

do not appropriately account for the simultaneity in the data – higher incomes allow 

individuals to invest more in health, but poor health often draws down an individual’s 

income (Chapman and Hariharan 1994).50 Some of these studies also may suffer from 

omitted variable bias resulting in misidentifying the relationship between income and 

health. 

 As an alternative to deriving the income-mortality relationship from aggregate 

data, Viscusi (1994a) illustrates how to generate an estimate for the expenditure-induced 

fatality rate based on the value of a statistical life and the marginal propensity to spend on 

health. Viscusi shows that 

marginal propensity to spend on health

value of a statistical life marginal expenditure per statistical life lost = . 

This approach requires an estimate of the value of a statistical life and an estimate of the 

marginal propensity to spend on health. The literature surveyed in previous sections 

provides estimates for the numerator. For the marginal propensity to spend on health, 

Viscusi estimated this based on an analysis of 24 OECD countries over the 1960 – 1989 

period and a time-series analysis for the United States over the same period (Viscusi 

 

50 A recent paper by Gerdtham and Johannesson (2002) attempts to address this problem by controlling for 

initial health status in regressions of mortality risk on income (with other relevant controls) for a study 

focusing on Sweden in the 1980s and 1990s. They find that an income loss ranging from $7.5 – $10.8 

million would induce an expected fatality in Sweden. The range reflects variation in the progressivity of 

the burden of the regulation. 
68

1992b, 1994a, 1994b). Over an array of specifications for both data sets, the marginal 

propensity to spend on health ranged from 0.08 to 0.12. This implies that for every dollar 

increase in national income, an additional 8 to 12 cents are spent on health care. 

Assuming a marginal propensity to spend on health of 0.1 and a VSL of about $6 million, 

the marginal expenditure per statistical life lost would be about $60 million.51

 Lutter, Morrall, and Viscusi (1999) extended Viscusi’s (1994a) model to account 

for the effect of income to influence risky behaviors (such as smoking, drinking, and poor 

diet) as well as the consumption of health care. Their statistical analyses illustrate that 

higher incomes promote better health habits, including reduced excessive drinking and 

cigarette smoking and increased exercise. Including the effects of income on risky health 

behavior reduces the estimated marginal expenditure per statistical life lost from the 

Viscusi (1994a) by more than a factor of 3 to $17 million. Lutter et al. note that policies 

that increase national income would reduce one fatality for every $17 million increase. 

The authors also note that since many regulations have costs per life saved of $100 

million or more, the expenditures may be wasteful and counterproductive.52

 If a regulation directly reduces mortality risk with regulatory-induced 

expenditures yielding a high cost per life saved ratio, then the indirect increase in 

mortality risk may exceed the direct decrease in risk resulting in an aggregate increase in 

 

51 Viscusi (1994a, 1994b) selected a VSL estimate of $5 million (in 1992 US$) because it represented the 

midpoint of the basic range for VSLs of $3 million - $7 million in Viscusi 1993. In 2000 US$, the 

midpoint would be $6.1 million. 

52 Refer to Morrall (1986) and Tengs et al. (1995) for lists of regulations’ cost-effectiveness for the United 

States. Tengs et al. found that for 124 environmental regulations (primarily toxin control), the median cost 

per life-year saved is $3.3 million. About 15 percent of the environmental regulations exceeded $100 

million per life-year saved. Converting these life-year values to statistical life values would result in a 

significant number of regulations with incredibly exorbitant cost-effectiveness measures. Life-saving 

regulations in other sectors of the economy (e.g., health care, transportation) had much lower costs per lifeyear saved. Refer to Ramsberg and Sjoberg (1997) for an evaluation of the cost-effectiveness of lifesaving 

interventions in Sweden. 
69

societal mortality risk. The existing literature varies in terms of a preferred value for the 

amount of induced expenditures associated with an additional mortality. However, some 

regulations would clearly not pass a risk-risk analysis with any plausible value for an 

induced-expenditure mortality. For example, the 1986 OSHA regulation limiting 

asbestos occupational exposure cost about $250 million per normalized life saved, and 

the 1987 OSHA regulation limiting formaldehyde occupational exposure cost about $290 

billion per normalized life saved (Viscusi, Hakes, and Carlin 1997). 

 While the risk-risk literature has focused on mortality risks, many environment 

and safety regulations provide other kinds of benefits, such as reduced morbidity and 

injury as well as non-human health related effects such as improved visibility and 

ecosystem health (Lutter and Morrall 1994, Portney and Stavins 1994). A regulation that 

primarily delivers non-mortality benefits could fail a risk-risk analysis but still pass a 

benefit-cost analysis. One approach could be to convert the morbidity effects into 

mortality risk equivalents. Alternatively one could focus on policies whose primary 

intent is to reduce mortality risks. Recognizing this criticism, Hahn, Lutter, and Viscusi 

(2000) conducted risk-risk analysis on a number of major environment, health, and safety 

regulations over the 1986 – 1998 period whose primary benefits were reduced mortality 

risk (see Table 13).53 Their analysis focuses on 24 regulations promulgated by the U.S. 

Environmental Protection Agency (EPA), U.S. Department of Housing and Urban 

Development (HUD), U.S. Department of Labor (OSHA), and U.S. Consumer Products 

Safety Commission (CPSC). Thirteen of 24 regulations designed primarily to reduce 

mortality risks actually increased mortality risks based on the Lutter et al. work finding 

 

53 Hahn, Lutter, and Viscusi only considered regulations with reduced mortality risk benefits comprising at 

least 90 percent of total monetized benefits. 
70

that $15 million of expenditures induces a fatality. Ten of these thirteen rules failing a 

risk-risk analysis had at least ten times more expenditure-induced fatalities than lives 

saved.54 

10. Conclusion 

 For nearly thirty years, economists have attempted to infer individuals’ 

preferences over mortality and morbidity risk and income in labor and product markets. 

The substantial literature that has developed over that time has confirmed Adam Smith’s 

intuition about compensating differentials for occupational hazards in a significant and 

growing number of countries. In addition to evaluating various international labor 

markets, the literature has expanded to address a variety of econometric issues, morbidity 

risk premiums, and factors influencing mortality risk premiums such as union affiliation 

and age. 

 While the tradeoff estimates may vary significantly across studies, the value of a 

statistical life for prime-aged workers has a median value of about $7 million in the 

United States. Our meta-analysis characterizes some of the uncertainty in estimates of 

the value of a statistical life, and finds that 95 percent confidence interval upper bounds 

can exceed the lower bounds by a factor of two or more. Other developed countries 

appear to have comparable VSLs, although some studies of the United Kingdom have 

found much larger risk premiums. Consistent with the fact that safety is a normal good, 

developing countries labor markets also have significant, but smaller, values of statistical 

life. Overall, our point estimates of the income elasticity of the value of a statistical life 

 

54 Note that the construction of discounted statistical lives saved reflects both an accounting for life-years 

saved and discounting for latency. 
71

range from 0.5 to 0.6. Union members in U.S. labor markets appear to enjoy greater risk 

premiums than non-members, while the evidence in other developed countries is rather 

mixed. The theoretical and empirical literature indicates that the value of a statistical life 

decreases with age. 

 The estimates of the value of a statistical life can continue to serve as a critical 

input in benefit-cost analyses of proposed regulations and policies. Refining VSLs for 

the specific characteristics of the affected population at risk remains an important priority 

for the research community and the government agencies conducting these economic 

analyses. Improving the application of VSLs in this way can result in more informed 

government interventions to address market failures related to environmental, health, and 

safety mortality risks. 
72

References 

Adler, M.D. and E.A. Posner. (2000). “Implementing Cost-Benefit Analysis When 

Preferences Are Distorted,” Journal of Legal Studies 29, 1105-1148. 

Andrews, R.M. and P. McCrea. (1999). “UK Health and Safety Executive Approach to 

Exposure Limit Setting.” In Proceedings of the OECD Workshop on the 

Integration of Socio-Economic Analysis in Chemical Risk Management Decision 

Making, London, January 7-9, 1998. Paris: Organization of Economic 

Cooperation and Development, pp. 193-202. 

Arabsheibani, G.R. and A. Marin. (2000). “Stability of Estimates of the Compensation 

for Danger,” Journal of Risk and Uncertainty 20(3), 247-269. 

Arnould, R.J. and L.M. Nichols. (1983). “Wage-Risk Premiums and Workers' 

Compensation: A Refinement of Estimates of Compensating Wage Differential,” 

Journal of Political Economy 91(2), 332-340. 

Arrow, K.J., W.R. Cline, K.-G. Maler, M. Munasinghe, J.E. Stiglitz, and R. Squitieri. 

(1996). “Intertemporal Equity and Discounting.” In J.P. Bruce, H. Lee, and E. 

Haites (eds.), Climate Change 1995: Economic and Social Dimensions. 

Cambridge, England: Cambridge University Press, pp. 125-144. 

Aten, B., R. Summers, and A. Heston. (2001). The Penn World Table Mark 6. 

Philadelphia, PA: The Center for International Comparisons at the University of 

Pennsylvania. Internet: http://pwt.econ.upenn.edu. 

Atkinson, S.E. and R. Halvorsen. (1990). “The Valuation of Risks to Life: Evidence 

from the Market for Automobiles,” Review of Economics and Statistics 72(1), 

133-136. 

Barro, R.J. and J.W. Lee. (1996). “International Measures of Schooling Years and 

Schooling Quality,” American Economic Review 86(2), 218-223. 

Berger, M.C. and P.E. Gabriel. (1991). “Risk Aversion and the Earnings of U.S. 

Immigrants and Natives,” Applied Economics 23, 311-318. 

Biddle, J.E. and G.A. Zarkin. (1988). “Worker Preference and Market Compensation for 

Job Risk,” Review of Economics and Statistics 70(4), 660-667. 

Blanchard, G. (1996). Highway Benefit-Cost Analysis: A Review of Evidence. Transport 

Canada, Economic Analysis, Special Infrastructure Project. Report no. 

TP12790E. Ottawa, Ontario. 

Blomquist, G. (1979). “Value of Life Saving: Implications of Consumption Activity,” 

Journal of Political Economy 87(3), 540-558. 
73

Bowland, B.J. and J.C. Beghin. (2001). “Robust Estimates of Value of a Statistical Life 

for Developing Economies,” Journal of Policy Modeling 23, 385-396. 

Brown, C. (1980). “Equalizing Differences in the Labor Market,” Quarterly Journal of 

Economics 94(1), 113-134. 

Bureau of Labor Statistics. (n.d.). Census of Fatal Occupational Injuries. Washington, 

DC: Department of Labor. Internet: http://www.bls.gov/iif/oshcfoi1.htm. 

Butler, R.J. (1983). “Wage and Injury Rate Responses to Shifting Levels of Workers' 

Compensation.” In J.D. Worrall (ed.), Safety and the Workforce: Incentives and 

Disincentives in Workers' Compensation. Ithaca, NY: ILR Press, pp. 61-86. 

Carlin, P.S. and R. Sandy. (1991). “Estimating the Implicit Value of a Young Child's 

Life,” Southern Economic Journal 58(1), 186-202. 

Chapman, K.S. and G. Hariharan. (1994). “Controlling for Causality in the Link from 

Income to Mortality,” Journal of Risk and Uncertainty 8(1), 85-94. 

Chilton, S., J. Covey, L. Hopkins, M. Jones-Lee, G. Loomes, N. Pidgeon, and A. 

Spencer. (1999). “Annex: New Research Results on the Valuation of Preventing 

Fatal Road Accident Casualties.” In Health Costs Due to Road Traffic-related 

Air Pollution: Economic Evaluation, Prepared for the WHO Ministerial 

Conference on Environment and Health, London, June 1999. Bern, Switzerland: 

Federal Department of Environment, Transport, Energy and Communications, 

Bureau for Transport Statistics. 

Council of Economic Advisers. (2002). Economic Report of the President. Washington, 

DC: Government Printing Office. 

Cousineau, J-M., R. Lacroix, and A-M. Girard. (1992). “Occupational Hazard and Wage 

Compensating Differentials,” Review of Economics and Statistics 74(1), 166-169. 

Cutler, D.M. and E. Richardson. (1997). “Measuring the Health of the U.S. Population,” 

Brookings Papers on Economic Activity, Microeconomics vol. 1997, 217-271. 

Dardis, R. (1980). “The Value of Life: New Evidence from the Marketplace,” 

American Economic Review 70(5), 1077-1082. 

Desvousges, W.H., F.R. Johnson, H.S. Banzhof, R.R. Russell, E.E. Fries, K.J. Dietz, and 

S.C. Helms. (1995). Assessing the Environmental Externality Costs for 

Electricity Generation. Research Triangle Park, NC: Triangle Economic 

Research. 
74

Dickens, W.T. (1984). “Differences Between Risk Premiums in Union and Nonunion 

Wages and the Case for Occupational Safety Regulation,” American Economic 

Review 74(2), 320-323. 

Dillingham, A.E. (1985). “The Influence of Risk Variable Definition on Value-of-Life 

Estimates,” Economic Inquiry 23(2), 277-294. 

Dillingham, A.E., T. Miller, and D.T. Levy. (1996). “A More General and Unified 

Measure for Valuing Labour Market Risk,” Applied Economics 28, 537-542. 

Dillingham, A.E. and R.S. Smith. (1984). “Union Effects on the Valuation of Fatal 

Risk.” In Dennis, B.O. (ed.), Proceedings of the Industrial Relations Research 

Association 36th Annual Meeting, San Francisco, CA, December 28 – 30, 1983. 

Madison, WI: Industrial Relations Research Association, pp. 270-277. 

Dorman, P. and P. Hagstrom. (1998). “Wage Compensation for Dangerous Work 

Revisited,” Industrial and Labor Relations Review 52(1), 116-135. 

Dorsey, S. (1983). “Employment Hazards and Fringe Benefits: Further Tests for 

Compensating Differentials.” In J.D. Worrall (ed.), Safety and the Workforce: 

Incentives and Disincentives in Workers' Compensation. Ithaca, NY: ILR Press, 

pp. 87-102. 

Dorsey S. and N. Walzer. (1983). “Workers' Compensation, Job Hazards, and Wages,” 

Industrial and Labor Relations Review 36(4), 642-654. 

Dreyfus, M.K. and W.K. Viscusi. (1995). “Rates of Time Preference and Consumer 

Valuations of Automobile Safety and Fuel Efficiency,” Journal of Law and 

Economics 38(1), 79-105. 

Duncan, G.J. and B. Holmlund. (1983). “Was Adam Smith Right After All? Another 

Test of the Theory of Compensating Wage Differentials,” Journal of Labor 

Economics 1(4), 366-379. 

European Commission. (2000). Workshop on the Value of Reducing the Risk of IllHealth or a Fatal Illness. Proceedings, November 13, 2000, Brussels, Belgium. 

Internet: 

http://europa.eu.int/comm/environment/enveco/others/proceedings_of_the_works

hop.pdf. 

Fairris, D. (1989). “Compensating Wage Differentials in the Union and Nonunion 

Sectors,” Industrial Relations 28(3), 356-372. 

Fairris, D. (1992). “Compensating Payments and Hazardous Work in Union and 

Nonunion Settings,” Journal of Labor Research 13(2), 205-221. 
75

Fishback, P.V. and S.E. Kantor. (1992). “`Square Deal’ or Raw Deal? Market 

Compensation for Workplace Disamenities, 1884-1903,” Journal of Economic 

History 52(4), 826-848. 

Freeman, R.B. and J.L. Medoff. (1981). “The Impact of the Percentage Organized on 

Union and Nonunion Wages,” Review of Economics and Statistics 63(4), 561-

572. 

French, M.T. (1990). “Estimating the Full Cost of Workplace Injuries,” American 

Journal of Public Health 80(9), 1118-1119. 

French, M.T. and D.L. Kendall. (1992). “The Value of Job Safety for Railroad 

Workers,” Journal of Risk and Uncertainty 5(2), 175-185. 

Frerichs, R.R., J.M. Chapman, P. Nourjah, and E.F. Maes. (1984). Cardiovascular 

Diseases in Los Angeles, 1979 – 1981. Los Angeles: American Heart Association 

– Greater Los Angeles Affiliate, Inc. 

Gaba, A. and W.K. Viscusi. (1998). “Differences in Subjective Risk Thresholds: 

Worker Groups as an Example,” Management Science 44(6), 801-811. 

Garbacz, C. (1989). “Smoke Detector Effectiveness and the Value of Saving a Life,” 

Economics Letters 31, 281-286. 

Garber, A.M. and C.E. Phelps. (1997). “Economic Foundations of Cost-Effectiveness 

Analysis,” Journal of Health Economics 16, 1-31. 

Garen, J. (1988). “Compensating Wage Differentials and the Endogeneity of Job 

Riskiness,” Review of Economics and Statistics 70(1), 9-16. 

Gayer, T., J.T. Hamilton, and W.K. Viscusi. (2000). “Private Values of Risk Tradeoffs 

at Superfund Sites: Housing Market Evidence on Learning About Risk,” Review 

of Economics and Statistics 82(3), 439-451. 

Gegax, D., S. Gerking, and W. Schulze. (1991). “Perceived Risk and the Marginal 

Value of Safety,” Review of Economics and Statistics 73(4), 589-596. 

Gerdtham, U. and M. Johannesson. (2002). “Do Life-Saving Regulations Save Lives?” 

Journal of Risk and Uncertainty 24(3), 231-249. 

Gerking, S., M. de Haan, and W. Schulze. (1988). “The Marginal Value of Job Safety: 

A Contingent Valuation Study,” Journal of Risk and Uncertainty 1(2), 185-199. 

Ghosh, D., D. Lees, and W. Seal. (1975). “Optimal Motorway Speed and Some 

Valuations of Time and Life,” Manchester School Economics and Social Studies

43(2), 134-143. 
76

Graham, J.D., B.-H. Chang, and J.S. Evans. (1992). “Poorer is Riskier,” Risk Analysis

12(3): 333-337. 

Graham, J.D. and J.B. Wiener. (1995). Risks vs. Risks: Tradeoffs in Protecting Health 

and the Environment. Cambridge, MA: Harvard University Press. 

Griliches, Z. (ed.). (1971). Price Indexes and Quality Change. Cambridge: Harvard 

University Press. 

Hahn, R.W., R.W. Lutter, and W.K. Viscusi. (2000). Do Federal Regulations Reduce 

Mortality? Washington, DC: AEI-Brookings Joint Center for Regulatory Studies. 

Hamermesh, D.S. (1978). “Economic Aspects of Job Satisfaction.” In O. Ashenfelter 

and W. Oates (eds.), Essays in Labor Market Analysis. New York: John Wiley 

and Sons, pp. 53-72. 

Hamermesh, D.S. (1999). “Changing Inequality in Markets for Workplace Amenities,” 

Quarterly Journal of Economics 114(4), 1085-1123. 

Hamermesh, D.S. and J.R. Wolfe. (1990). “Compensating Wage Differentials and the 

Duration of Wage Loss,” Journal of Labor Economics 8(1), S175-S197. 

Hammitt, J.K. (2002). “Commentary on: What Determines the Value of Life? A MetaAnalysis,” Journal of Policy Analysis and Management 21(2), 271-273. 

Hara Associates Inc. (2000). Benefit/Cost Analysis of Proposed Tobacco Products 

Information Regulations. Prepared for Health Canada and Consulting and Audit 

Canada. Ottawa, Ontario. June 5, 2000. 

Heinzerling, L. (1998). “Regulatory Costs of Mythic Proportions,” Yale Law Journal

107, 1981-2070. 

Heinzerling, L. (1999). “Discounting Life,” Yale Law Journal 108, 1911-1915. 

Heinzerling, L. (2000). “The Rights of Statistical People,” Harvard Environmental Law 

Review 24, 189-207. 

Hersch, J. (1998). “Compensating Differentials for Gender-Specific Job Injury Risks,” 

American Economic Review 88(3), 598-627. 

Hersch, J. and T. S. Pickton. (1995). “Risk-Taking Activities and Heterogeneity of JobRisk Tradeoffs,” Journal of Risk and Uncertainty 11(3), 205-217. 

Hersch, J. and W.K. Viscusi. (1990). “Cigarette Smoking, Seatbelt Use, and Differences 

in Wage-Risk Tradeoffs,” Journal of Human Resources 25(2), 202-227. 
77

Herzog Jr., H.W. and A.M. Schlottmann. (1990). “Valuing Risk in the Workplace: 

Market Price, Willingness to Pay, and the Optimal Provision of Safety,” Review 

of Economics and Statistics 72(3), 463-470. 

H.M. Treasury. (1997). “The Green Book”: Appraisal and Evaluation in Central 

Government, Treasury Guidance. London: Her Majesty’s Stationary Office 

Hwang, H.-S., W.R. Reed, and C. Hubbard. (1992). “Compensating Wage Differentials 

and Unobserved Productivity,” Journal of Political Economy 100(4), 835-858. 

Intergovernmental Panel on Climate Change (IPCC) (1996). Climate Change 1995: 

Economic and Social Dimensions of Climate Change. Cambridge: Cambridge 

University Press. 

Intergovernmental Panel on Climate Change (IPCC) (2001). Climate Change 2001: 

Mitigation. Cambridge: Cambridge University Press. 

Ippolito, P.M. and R.A. Ippolito. (1984). “Measuring the Value of Life Saving from 

Consumer Reactions to New Information,” Journal of Public Economics 25, 53-

81. 

Jenkins, R.R., N. Owens, and L.B. Wiggins. (2001). “Valuing Reduced Risks to 

Children: The Case of Bicycle Safety Helmets,” Contemporary Economic Policy

19(4), 397-408. 

Jones-Lee, M.W. (1976). The Value of Life: An Economic Analysis. Chicago: 

University of Chicago Press. 

Jones-Lee, M.W., M. Hammerton, and P.R. Phillips. (1985). “The Value of Safety: 

Results of a National Sample Survey,” Economic Journal 95, 49-72. 

Keeler, E.B. and S. Cretin. (1983). “Discounting of Life-Saving and Other 

Nonmonetary Effects,” Management Science 29(3), 300-306. 

Keeney, R.L. (1990). “Mortality Risks Induced by Economic Expenditures,” Risk 

Analysis 10(1), 147-159. 

Keeney, R.L. (1994). “Mortality Risks Induced by the Costs of Regulation,” Journal of 

Risk and Uncertainty 8(1), 95-110. 

Keeney, R.L. (1997). “Estimating Fatalities Induced by the Economic Costs of 

Regulation.” Journal of Risk and Uncertainty 14(1), 5-24. 
78

Kim, S-W. and P.V. Fishback. (1993). “Institutional Change, Compensating 

Differentials, and Accident Risk in American Railroading, 1892-1945,” Journal 

of Economic History 53(4), 796-823. 

Kim, S-W. and P.V. Fishback. (1999). “The Impact of Institutional Change on 

Compensating Wage Differentials for Accident Risk: South Korea, 1984-1990,” 

Journal of Risk and Uncertainty 18(3), 231-248. 

Kitawaga, E.M. and P.M. Hauser. (1973). Differential Mortality in the United States of 

America: A Study in Socioeconomic Epidemiology. Cambridge, MA: Harvard 

University Press. 

Kniesner, T.J. and J.D. Leeth. (1991). “Compensating Wage Differentials for Fatal 

Injury Risk in Australia, Japan, and the United States,” Journal of Risk and 

Uncertainty 4(1), 75-90. 

Krupnick, A. (2002). “Commentary on: What Determines the Value of Life? A MetaAnalysis,” Journal of Policy Analysis and Management 21(2), 275-282. 

Lang, C., G. Yarwood, F. Lalonde, and R. Bloxam. (1995). Environmental and Health 

Benefits of Cleaner Vehicles and Fuels. Prepared for Canadian Council of 

Ministers of the Environment, Task Force on Cleaner Vehicles and Fuels. 

October 5, 1995. 

Lanoie, P., C. Pedro, and R. Latour. (1995). “The Value of a Statistical Life: A 

Comparison of Two Approaches,” Journal of Risk and Uncertainty 10, 235-257. 

Leigh, J.P. (1981). “Compensating Wages for Occupational Injuries and Diseases,” 

Social Science Quarterly 62(4), 772-778. 

Leigh, J.P. (1987). “Gender, Firm Size, Industry, and Estimates of the Value of Life,” 

Journal of Health Economics 1, 331-344. 

Leigh, J.P. (1991). “No Evidence of Compensating Wages for Occupational Fatalities,” 

Industrial Relations 30(3), 382-395. 

Leigh, J.P. (1995). “Compensating Wages, Value of a Statistical Life, and Inter-Industry 

Differentials,” Journal of Environmental Economics and Management 28(1), 83-

97. 

Leigh, J.P. and R.N. Folsom. (1984). “Estimates of the Value of Accident Avoidance at 

the Job Depend on the Concavity of the Equalizing Differences Curve,” 

Quarterly Review of Economics and Business 24(1), 56-66. 

Liu, J-T., J. Hammitt, and J-L. Liu. (1997). “Estimating Hedonic Wage Function and 

Value of Life in a Developing Country,” Economics Letters 57, 353-358. 
79

Liu, J-T. and J.K. Hammitt. (1999). “Perceived Risk and Value of Workplace Safety in 

a Developing Country,” Journal of Risk Research 2(3), 263-275. 

Lott, J.R. and R.L. Manning. (2000). “Have Changing Liability Rules Compensated 

Workers Twice for Occupational Hazards? Earnings Premiums and Cancer 

Risks,” Journal of Legal Studies 29, 99-130. 

Low, S.A. and L.R. McPheters. (1983). “Wage Differentials and the Risk of Death: An 

Empirical Analysis,” Economic Inquiry 21, 271-280. 

Lutter, R. and J.F. Morrall. (1994). “Health-Health Analysis: A New Way to Evaluate 

Health and Safety Regulation,” Journal of Risk and Uncertainty 8(1), 43-66. 

Lutter, R., J.F. Morrall, and W.K. Viscusi. (1999). “The Cost-Per-Life-Saved Cutoff for 

Safety-Enhancing Regulations,” Economic Inquiry 37(4), 599-608. 

Magat, Wesley, W. Kip Viscusi, and Joel Huber. (1996). “A Reference Lottery Metric 

for Valuing Health,” Management Science 42(8), 1118-1130. 

Marin, A. and H. Psacharopoulos. (1982). “The Reward for Risk in the Labor Market: 

Evidence from the United Kingdom and a Reconciliation with Other Studies,” 

Journal of Political Economy 90(4), 827-853. 

Marsh, Suzanne M. and Larry A. Layne. (2001). Fatal Injuries to Civilian Workers in 

the United States, 1980 - 1995. DHHS/NIOSH pub. no. 2001-129s. July. 

Washington, DC: Department of Health and Human Services. 

Martinello, F. and R. Meng. (1992). “Workplace Risks and the Value of Hazard 

Avoidance,” Canadian Journal of Economics 25(2), 333-345. 

McLean, R.A., W.R. Wendling, and P.R. Neergaard. (1978). “Compensating Wage 

Differentials for Hazardous Work: An Empirical Analysis,” Quarterly Review of 

Economics and Business 18(3), 97-107. 

McNeill, W.H. (1976). Plagues and Peoples. Garden City, NY: Anchor Press. 

Mellow, W. and H. Sider. (1983). “Accuracy of Response in Labor Market Surveys: 

Evidence and Implications,” Journal of Labor Economics 1(4), 331-344. 

Meng, R. (1989). “Compensating Differences in the Canadian Labour Market,” 

Canadian Journal of Economics 22(2), 413-424. 

Meng, R. (1991). “Compensating Wages for Long-Term Job Hazards in Canadian 

Industry,” Economics Letters 36(3), 331-336. 
80

Meng, R.A. and D.A. Smith. (1990). “The Valuation of Risk of Death in Public Sector 

Decision-Making,” Canadian Public Policy - Analyse de Politiques 16(2), 137-

144. 

Meng, R. and D.A. Smith. (1999). “The Impact of Workers' Compensation on Wage 

Premiums for Job Hazards,” Applied Economics 31(9), 1101-1108. 

Miller, P., C. Mulvey, and K. Norris. (1997). “Compensating Differentials for Risk of 

Death in Australia,” Economic Record 73(223), 363-372. 

Miller, T.R. (1990). “The Plausible Range for the Value of Life – Red Herrings Among 

the Mackerel,” Journal of Forensic Economics 3(3), 17-39. 

Miller, T.R. (2000). “Variations between Countries in Values of Statistical Life,” 

Journal of Transport Economics and Policy 34(2), 169-188. 

Moore, M.J. and W.K. Viscusi. (1988a). “Doubling the Estimated Value of Life: Results 

Using New Occupational Fatality Data,” Journal of Policy Analysis and 

Management 7(3), 476-490. 

Moore, M.J. and W.K. Viscusi. (1988b). “The Quantity-Adjusted Value of Life,” 

Economic Inquiry 26, 369-388. 

Moore, M.J. and W.K. Viscusi. (1990a). Compensation Mechanisms for Job Risks. 

Princeton: Princeton University Press. 

Moore, M.J. and W.K. Viscusi. (1990b). “Discounting Environmental Health Risks: 

New Evidence and Policy Implications,” Journal of Environmental Economics 

and Management 18, S51-S62. 

Moore, M.J. and W.K. Viscusi. (1990c). “Models for Estimating Discount Rates for 

Long-Term Health Risks Using Labor Market Data,” Journal of Risk and 

Uncertainty 3, 381-401. 

Morrall, J. (1986). “A Review of the Record,” Regulation (Nov/Dec), 25-34. 

Mrozek, J.R. and L.O. Taylor. (2002). “What Determines the Value of Life? A MetaAnalysis,” Journal of Policy Analysis and Management 21(2), 253-270. 

National Institute for Occupational Safety and Health (NIOSH). (2000). Worker Health 

Chartbook, 2000. Report no. 2000-127. U.S. Department of Health and Human 

Services. Internet: http://www.cdc.gov/niosh/pdfs/2000-127.pdf. 

Organization for Economic Cooperation and Development (OECD). (1995). 

Recommendation of the Council of the OECD on Improving the Quality of 

Government Regulation. Report OCDE/GD(95)95. Paris: OECD. 
81

Organization for Economic Cooperation and Development (OECD). (2001). OECD 

Environmental Outlook. Paris: OECD. 

Olson, C.A. (1981). “An Analysis of Wage Differentials Received by Workers on 

Dangerous Jobs,” Journal of Human Resources 16(2), 167-185. 

Peltzman, S. (1975). “The Effects of Automobile Regulation,” Journal of Political 

Economy 83(4), 677-726. 

Privy Council Office, Canada. (1995). Benefit/Cost Analysis Guide for Regulatory 

Programs. Ottawa, Ontario. Internet: 

http://www.pco-bcp.gc.ca/raoics-srdc/ProcGuides/cbgcntents_e.htm. 

Portney, P.R. (1981). “Housing Prices, Health Effects, and Valuing Reductions in Risk 

of Death,” Journal of Environmental Economics and Management 8, 72-78. 

Portney, P.R. and R.N. Stavins. (1994). “Regulatory Review of Environmental Policy: 

The Potential Role of Health-Health Analysis,” Journal of Risk and Uncertainty

8(1), 111-122. 

Ramsberg, J.A.L. and L. Sjoberg. (1997). “The Cost-Effectiveness of Lifesaving 

Interventions in Sweden,” Risk Analysis 17(4), 467-478. 

Revesz, Richard L. (1999). “Environmental Regulation, Cost-Benefit Analysis, and the 

Discounting of Human Lives,” Columbia Law Review 99, 941-1017. 

Rosen, S. (1974). “Hedonic Prices and Implicit Markets: Product Differentiation in Pure 

Competition,” Journal of Political Economy 82, 34-55. 

Rosen, S. (1986). “The Theory of Equalizing Differences.” In O. Ashenfelter and R. 

Layard (eds.), Handbook of Labor Economics. Amsterdam: North-Holland, pp. 

641-692. 

Rosen, S. (1988). “The Value of Changes in Life Expectancy,” Journal of Risk and 

Uncertainty 1, 285-304. 

Sandy, R. and R.F. Elliott. (1996). “Unions and Risk: Their Impact on the Level of 

Compensation for Fatal Risk,” Economica 63(250), 291-309. 

Sandy, R., R.F. Elliott, W.S. Siebert, and X. Wei. (2001). “Measurement Error and the 

Effects of Unions on the Compensating Differentials for Fatal Workplace Risks,” 

Journal of Risk and Uncertainty 23(1), 33-56. 
82

Schelling, T.C. (1968). “The Life You Save May Be Your Own.” In S.B. Chase (ed.), 

Problems in Public Expenditure and Analysis. Washington, DC: Brookings 

Institution, pp. 127-162. 

Science Advisory Board. (1999a). Advisory Letter to Administrator Browner Regarding 

the Clean Air Act Amendments Section 812 Prospective Study of Costs and 

Benefits. October 29, 1999. Internet: http://www.epa.gov/sab/coua0002.pdf. 

Science Advisory Board. (1999b). Final Advisory Letter to Administrator Browner 

Regarding the Clean Air Act Amendments Section 812 Prospective Study of Costs 

and Benefits. November 19, 1999. Internet: 

http://www.epa.gov/sab/coua0003.pdf. 

Science Advisory Board. (2000). SAB Report on EPA’s White Paper “Valuing the 

Benefits of Fatal Cancer Risk Reduction.” Transmitted to the EPA by letter to 

Administrator Browner. July 27, 2000. Internet: 

http://www.epa.gov/sab/eeacf013.pdf. 

Shanmugam, K.R. (1996/7). “The Value of Life: Estimates from Indian Labour 

Market,” Indian Economic Journal 44(4), 105-114. 

Shanmugam, K.R. (1997). “Value of Life and Injury: Estimating Using Flexible 

Functional Form,” Indian Journal of Applied Economics 6(3), 125-136. 

Shanmugam, K.R. (2000). “Valuations of Life and Injury Risks,” Environmental and 

Resource Economics 16, 379-389. 

Shanmugam, K.R. (2001). “Self Selection Bias in the Estimates of Compensating 

Differentials for Job Risks in India,” Journal of Risk and Uncertainty 22(3), 263-

275. 

Shepard, D.S. and R.J. Zeckhauser. (1982). “Life-Cycle Consumption and Willingnessto-Pay for Increased Survival.” In M.W. Jones-Lee (ed.), The Value of Life and 

Safety. Amsterdam: North Holland. 

Shepard, D.S. and R.J. Zeckhauser. (1984). “Survival Versus Consumption,” 

Management Science 30(4), 423-439. 

Shogren, J.S. and T. Stamland. (2002). “Skill and the Value of Life,” Journal of 

Political Economy 110(5), 1168-1173. 

Siebert, W.S. and X. Wei. (1994). “Compensating Wage Differentials for Workplace 

Accidents: Evidence for Union and Nonunion Workers in the UK,” Journal of 

Risk and Uncertainty 9(1), 61-76. 
83

Siebert, W.S. and X. Wei. (1998). “Wage Compensation for Job Risks: The Case of 

Hong Kong,” Asian Economic Journal 12(2), 171-181. 

Smith, A. (1776). The Wealth of Nations. Chicago: University of Chicago Press. 1976 

edition. 

Smith, R.S. (1974). “The Feasibility of an `Injury Tax’ Approach to Occupational 

Safety,” Law and Contemporary Problems 38(4), 730-744. 

Smith, R.S. (1976). The Occupational Safety and Health Act: Its Goals and Its 

Achievements. Washington, DC: American Enterprise Institute. 

Smith, R.S. (1979). “Compensating Wage Differentials and Public Policy: A Review,” 

Industrial and Labor Relations Review 32(3), 339-352. 

Smith, V.K. (1983). “The Role of Site and Job Characteristics in Hedonic Wage 

Models,” Journal of Urban Economics 13, 296-321. 

Smith, V.K., D.J. Epp, and K.A. Schwabe. (1994). “Cross-Country Analyses Don’t 

Estimate Health-Health Responses,” Journal of Risk and Uncertainty 8(1), 67-84. 

Smith, V.K. and C.C.S. Gilbert. (1984). “The Implicit Valuation of Risks to Life: A 

Comparative Analysis,” Economics Letters 16, 393-399. 

Smith, V.K. and C.C.S. Gilbert. (1985). “The Valuation of Environmental Risks Using 

Hedonic Wage Models.” In M. David and T. Smeeding (eds.), Horizontal Equity, 

Uncertainty, and Economic Well-Being. Chicago: University of Chicago Press. 

pp. 359-385. 

Summers, R. and A. Heston. (1991). “The Penn World Table (Mark 5): An Expanded 

Set of International Comparisons, 1950 – 1988,” Quarterly Journal of Economics

106, 327-368. 

Tengs, T.O., M.E. Adams, J.S. Pliskin, D.G. Safran, J.E. Siegel, M.C. Weinstein, and 

J.D. Graham. (1995). “Five-Hundred Life-Saving Interventions and Their CostEffectiveness,” Risk Analysis 15(3), 369-390. 

 

Thaler, R. and S. Rosen. (1975). “The Value of Saving a Life: Evidence from the Labor 

Market.” In N.E. Terleckyj (ed.), Household Production and Consumption. New 

York: Columbia University Press, pp. 265-300. 

U.K. Cabinet Office. (2000). Good Policy Making: A Guide to Regulatory Impact 

Assessment. London: Cabinet Office Regulatory Impact Unit. Internet: 

http://www.cabinet-office.gov.uk/regulation/2000/riaguide/default.htm. 
84

U.S. Environmental Protection Agency. (1999). The Benefits and Costs of the Clean Air 

Act 1990 to 2010. Report 410-R-99-001. Washington, DC: Office of Air and 

Radiation, USEPA. Internet: http://www.epa.gov/oar/sect812/copy99.html. 

U.S. Environmental Protection Agency. (2000a). Guidelines for Preparing Economic 

Analyses. Report 240-R-00-003. Washington, DC: Office of the Administrator, 

USEPA. Internet: http://yosemite1.epa.gov/ee/epa/eed.nsf/pages/guidelines. 

U.S. Environmental Protection Agency. (2000b). Regulatory Impact Analysis: HeavyDuty Engine and Vehicle Standards and Highway Diesel Fuel Sulfur Control 

Requirements. Report 420-R-00-026. Washington, DC: Office of Air and 

Radiation, USEPA. 

U.S. Federal Aviation Administration. (1998). Economic Values for Evaluation of 

Federal Aviation Administration Investment and Regulatory Program. Report no. 

FAA-APO-98-8. Washington, DC: Department of Transportation. June. 

Internet: http://www.api.faa.gov/economic/TOC.PDF. 

U.S. Office of Management and Budget. (1992). Guidelines and Discount Rates for 

Benefit-Cost Analysis of Federal Programs. Circular A-94. Washington, DC: 

The White House. Internet: 

http://www.whitehouse.gov/omb/circulars/a094/a094.pdf. 

U.S. Office of Management and Budget. (1996). Economic Analysis of Federal 

Regulations Under Executive Order 12866. Washington, DC: The White House. 

Internet: http://www.whitehouse.gov/omb/inforeg/riaguide.html. 

U.S. Office of Management and Budget. (2001). Making Sense of Regulation: 2001 

Report to Congress on the Costs and Benefits of Regulations and Unfunded 

Mandates on State, Local, and Tribal Entities. Washington, DC: The White 

House. Internet: http://www.whitehouse.gov/omb/inforeg/costbenefitreport.pdf. 

Viscusi, W.K. (1978a). “Labor Market Valuations of Life and Limb: Empirical 

Evidence and Policy Implications,” Public Policy 26(3), 359-386. 

Viscusi, W.K. (1978b). “Wealth Effects and Earnings Premiums for Job Hazards,” 

Review of Economics and Statistics 60(3), 408-416. 

Viscusi, W.K. (1979). Employment Hazards: An Investigation of Market Performance. 

Cambridge, MA: Harvard University Press. 

Viscusi, W.K. (1980). “Union, Labor Market Structure, and the Welfare Implications of 

the Quality of Work,” Journal of Labor Research 1(1), 175-192. 
85

Viscusi, W.K. (1981). “Occupational Safety and Health Regulation: Its Impact and 

Policy Alternatives.” In J.P. Crecine (ed.), Research in Public Policy Analysis 

and Management. Greenwich, CT: JAI Press, vol. 2, 281-299. 

Viscusi, W.K. (1983). Risk by Choice: Regulating Health and Safety in the Workplace. 

Cambridge, MA: Harvard University Press. 

Viscusi, W.K. (1992a). Fatal Tradeoffs: Public and Private Responsibilities for Risk. 

New York: Oxford University Press. 

Viscusi, W.K. (1992b). Wealth, Health Investments, and the Value of Life. Report 

prepared for the U.S. Office of Management and Budget, Washington, DC. 

Viscusi, W.K. (1993). “The Value of Risks to Life and Health,” Journal of Economic 

Literature 31, 1912-1946. 

Viscusi, W.K. (1994a). “Mortality Effects of Regulatory Costs and Policy Evaluation 

Criteria,” RAND Journal of Economics 25(1), 94-109. 

Viscusi, W.K. (1994b). “Risk-Risk Analysis,” Journal of Risk and Uncertainty 8(1), 5-

17. 

Viscusi, W.K. (1998). Rational Risk Policy. New York: Oxford University Press. 

Viscusi, W.K. (2000). “The Value of Life in Legal Contexts: Survey and Critique,” 

American Law and Economics Review 2(1), 195-222. 

Viscusi, W.K. and W. Evans. (1990). “Utility Functions that are Dependent on One’s 

Health Status: Estimates and Economic Implications,” American Economic 

Review 80, 353-374. 

Viscusi, W.K., J.K. Hakes, and A. Carlin. (1997). “Measures of Mortality Risks,” 

Journal of Risk and Uncertainty 14(3), 213-233. 

Viscusi, W.K. and J. Hersch. (2001). “Cigarette Smokers as Job Risk Takers,” Review 

of Economics and Statistics 83(2), 269-280. 

Viscusi, W. K. and M.J. Moore. (1989). “Rates of Time Preference and Valuations of 

the Duration of Life,” Journal of Public Economics 38, 297-317. 

Viscusi, W.K. and C.J. O'Connor. (1984). “Adaptive Responses to Chemical Labeling: 

Are Workers Bayesian Decision Makers?” American Economic Review 74(5), 

942-956. 

Viscusi, W.K. and R.J. Zeckhauser. (1994). “The Fatality and Injury Costs of 

Expenditures,” Journal of Risk and Uncertainty 8(1), 19-41. 
86

Weiss, P., G. Maier, and S. Gerking. (1986). “The Economic Evaluation of Job Safety: 

A Methodological Survey and Some Estimates for Austria,” Empirica 13(1), 53-

67. 

White, H. (1980). “A Heteroskedasticity-Consistent Covariance Matrix Estimator and a 

Direct Test for Heteroskedasticity,” Econometrica 48(3), 817-830. 

Wildavsky, A. (1980). “Richer is Safer,” The Public Interest 60, 23-39. 

Wildavsky, A. (1988). Searching for Safety. New Brunswick, NJ: Transaction Books.
Figure 1 Market Process for Determining Compensating Differentials 

w1(p2) 

w2(p2) 

w1(p1) 

p1 p2 

Wage 

EU1 

w(p) 

OC1 

OC2 

O

OC1 

w(p) 

EU2 

EU1

EU2 
Figure 2 

The Value of Statistical Life as a Function of Mortality Risk 

05

10152025

0.01 0.05 0.09 0.13 0.17 0.21 0.25 0.29 0.33 0.37

Annual Occupational Mortality Risk per 1,000 Workers 

Value of 

Statistical 

Life 

(millions, 

2000 US$)

Viscusi (1981; linear) Viscusi (1981; log) L&F (1984; lineaL&F (1984; log) Olson (1981; log) D&W (1983; log)
Table 1. U.S. Occupational Fatality Rates by Industry, 1992 – 1995 National 

 Fatality Rate per 100,000 

Workers 

Industry NIOSH (NTOF) BLS (CFOI) 

Agriculture, Forestry, & 

Fisheries 

17.0 23.9 

Mining 24.5 26.3 

Construction 12.8 13.4 

Manufacturing 3.6 3.8 

Transportation & Utilities 10.4 10.6 

Wholesale Trade 3.5 5.4 

Retail Trade 2.8 3.6 

Finance, Insurance, & 

Real Estate 

1.1 1.5 

Services 1.5 1.8 

Sources: Rates constructed by authors based on Marsh and Layne (2001) and B
 Table 2. Summary of Labor Market Studies of the Value of a Statistical Life, United States 

Author (Year) Sample Risk Variable Mean Risk Nonfatal Risk Included? Workers' Comp Included? Avera

Income 

(2000 U

Smith (1974) Current Population Survey

(CPS) 1967, Census of Manufactures 1963, U.S. Census 1960, Employment and Earnings 1963 

Bureau of Labor Statistics (BLS) 1966, 1967 

0.000125 Yes, 

significant 

No $29,0

Thaler and Rosen (1975) Survey of Economic 

Opportunity 1967 

Society of Actuaries 1967 0.001 No No $34,6

Smith (1976) CPS 1967, 1973 BLS 1966, 1967, 1970 0.0001 Yes, not 

significant 

No $31,0

Viscusi (1978a, 1979) Survey of Working 

Conditions, 1969-1970 (SWC) 

BLS 1969, subjective risk of job (SWC) 

0.0001 Yes, 

significant 

No $31,8

Brown (1980) National Longitudinal 

Survey of Young Men 1966-71, 1973 

Society of Actuaries 1967 0.002 No No $49,0

Viscusi (1981) Panel Study of Income 

Dynamics (PSID) 1976 

BLS 1973-1976 0.0001 Yes, 

significant 

No $22,6

Olson (1981) CPS 1978 BLS 1973 0.0001 Yes, 

significant 

No $36,1

Arnould and Nichols (1983) 

U.S. Census 1970 Society of Actuaries 1967 0.001 No Yes NA 

Butler (1983) S.C. Workers' 

Compensation Data 1940-69 

S.C. Workers' Compensation Claims Data 

0.00005 No Yes $22,7
Author (Year) Sample Risk Variable Mean Risk Nonfatal Risk Included? Workers' Comp Included? Avera

Income 

(2000 U

Low and McPheters (1983) 

International City Management Association 1976 (police officer wages) 

Constructed a risk measure from DOJ/FBI police officers killed data 1972-75 for 72 cities 

0.0003 No No $33,1

Dorsey and Walzer (1983) CPS May 1978 BLS 1976 0.000052 Yes, 

significant 

Yes $21,6

Leigh and Folsom (1984) PSID 1974; Quality of 

Employment Survey (QES) 1977 

BLS 0.0001 Yes, 

significant 

No $29,0

$36,9

Smith and Gilbert (1984, 1985) 

CPS 1978 BLS 1975 NA No No NA 

Dillingham and Smith (1984) 

CPS May 1979 BLS industry data 1976, 

1979; NY Workers' Comp Data 1970 

0.000082 Yes, 

significant in 

some 

specifications

No $29,7

Dillingham (1985) QES 1977 BLS 1976; NY Workers' 

Compensation data 1970 

0.000008, 0.00014 

No No $26,7

Leigh (1987) QES 1977; CPS 1977 BLS NA No No NA Moore and Viscusi (1988a) 

PSID 1982 BLS 1972-1982, NIOSH 

National Traumatic Occupational Fatality (NTOF) Survey 1980-1985 

0.00005, 0.00008 

No Yes $24,9

Moore and Viscusi (1988b) 

QES 1977 BLS, discounted expected 

life years lost; subjective risk of job (QES) 

0.00006 No Yes $31,0
Author (Year) Sample Risk Variable Mean Risk Nonfatal Risk Included? Workers' Comp Included? Avera

Income 

(2000 U

Garen (1988) PSID 1981-1982 BLS 1980, 1981 0.000108 Yes, 

significant 

No $29,8

Viscusi and Moore (1989) PSID 1982 NIOSH NTOF Survey, 

Structural Markov Model 

0.0001 No No $24,6

Herzog and Schlottman (1990) 

U.S. Census 1970 BLS 1969 0.000097 No No $48,3

Moore and Viscusi (1990b) 

PSID 1982 NIOSH NTOF Survey, 

Structural Life Cycle Model 

0.0001 No No $24,6

Moore and Viscusi (1990c) 

PSID 1982 NIOSH NTOF Survey, 

Structural Integrated Life Cycle Model 

0.0001 Yes Yes $24,6

Kniesner and Leeth (1991) CPS 1978 NIOSH NTOF Survey 

1980-1985 

0.0004 Yes, 

significant in 

some 

specifications

Yes $33,6

Gegax, Gerking, and Schulze (1991) 

Authors' mail survey 1984 Workers' assessed fatality 

risk at work 1984 

0.0009 No No $41,3

Leigh (1991) QES 1972-3, QES 1977, 

PSID 1974, 1981, Longitudinal QES 1973-1977, CPS January 1977 

BLS 1979, Workers' Compensation data from 11 states 1977-1980 

0.000134 No No $32,9

Berger and Gabriel (1991) US Census 1980 BLS 1979 0.00008-

0.000097 

No No $46,8

$48,0

Leigh (1995) PSID 1981, CPS January 

1977, QES 1977 

BLS 1976, 79-81 and NIOSH 1980-85 

0.00011-0.00013 

No No $29,5
Author (Year) Sample Risk Variable Mean Risk Nonfatal Risk Included? Workers' Comp Included? Avera

Income 

(2000 U

Dorman and Hagstrom (1998) 

PSID 1982 BLS 1979-1981, 1983, 

1985, 1986; NIOSH NTOF 1980-1988 

0.000123-0.0001639 

Yes Yes $32,2

Lott and Manning (2000) CPS March 1971 and 

March 1985 

Hickey-Kearney carcinogenic exposure 1972-1974, NIOSH National Occupational Exposure Survey 1981-1983 

NA No No $30,2

† Lott and Manning (2000) estimate represents the value of avoiding a statistical fatal cancer case with an assumed latency period of 10 percent). The reported values from their paper without discounting of this latency period are presented in parentheses. 
Table 3. Summary of Value of a Statistical Life Studies Based on Tradeoffs Outside the Labor Market, United St

Author (Year) Nature of Risk, Year Component of the Monetary Tradeoff Average Income Level (2000 US$) Imp

Blomquist (1979) Automobile death risks, 1972 Estimated disutility of seat belts $38,395 Dardis (1980) Fire fatality risks without smoke 

detectors, 1974-1979 

Purchase price and maintenance costs of smoke detectors 

NA 

Portney (1981) Mortality effects of air pollution, 

1978 

Property values in Allegheny County, PA 

NA 

Ippolito and Ippolito (1984) Cigarette smoking risks, 1980 Estimates monetary equivalent of 

effect of risk information 

NA 

Garbacz (1989) Fire fatality risks without smoke 

detectors, 1968-1985 

Purchase price of smoke detectors NA 

Atkinson and Halvorson (1990) Automobile accident risks, 1989 Prices of new automobiles NA 

Carlin and Sandy (1991) Fatality risks with use of 

children's car seats, 1985 

Purchase price of car seats plus time to buckle children, 10 Indiana cities 

$24,737 

Dreyfus and Viscusi (1995) Automobile safety, 1988 Prices of automobiles NA Gayer, Hamilton, and Viscusi (2000) Superfund sites' cancer risks, 

1988 - 1993 

Property values in Greater Grand Rapids, MI 

NA $3.2

Jenkins, Owens, and Wiggins (2001) Bicycle-related fatal head injury 

risks, 1997 

Purchase price of bicycle helmets NA $1.4-

$1.2-$$2.1-$

† Gayer et al. (2000) estimate represents the value of avoiding a statistical cancer case with an assumed latency period of 10 years (disc

reported values from their paper without discounting of this latency period are presented in parentheses.
Table 4. Summary of Labor Market Studies of the Value of a Statistical Life, International 

Author (Year) Country Sample Risk Variable Mean Risk 

Nonfatal 

Risk 

Included?

Workers' 

Comp 

Included?

Ave

(2

Marin and Psacharopoulos (1982) 

UK General Household 

Survey 1975 

OPCS Occupational Mortality Decennial Survey 1970-72 

0.0001 No No 

Weiss, Maier, and Gerking (1986) 

Austria Austrian Microcensus 

File of Central Bureau of Statistics 1981 

Austrian Social Insurance Data on job-related accidents 1977 - 1984 

NA Yes No 

Meng (1989) Canada National Survey of Class 

Structure and Labour Process 1981 

Labour Canada and Quebec Occupational Health and Safety Board 1981 

0.00019 No No 

Meng and Smith (1990) 

Canada National Election Study 

1984 

Labour Canada and Quebec Occupational Health and Safety Board 1981-83 

0.00012 No No 

Kniesner and Leeth (1991) 

Japan Two-digit manufacturing 

data 1986 (Japan) 

Yearbook of Labor Statistics (Japan) 

0.00003 Yes No 

Kniesner and Leeth (1991) 

Australia Two-digit manufacturing 

data 1984-85 (Australia, by state) 

Industrial Accidents, Australia Bureau of Statistics 1984 - 1986 

0.0001 Yes Yes 

Cousineau, Lacroix, and Girard (1992) 

Canada Labor, Canada Survey 

1979 

Quebec Compensation Board 

0.00001 Yes No 

Martinello and Meng (1992) 

Canada Labour Market Activity 

Survey 1986 

Labour Canada and Statistics Canada 1986 

0.00025 Yes No 
Author (Year) Country Sample Risk Variable Mean Risk 

Nonfatal 

Risk 

Included?

Workers' 

Comp 

Included?

Ave

(2

Kim and Fishback (1993) 

South Korea 

Ministry of Labor's Report on Monthly Labor Survey and Survey on Basic Statistics for the Wage Structures 

Ministry of Labor's Analysis for Industrial Accidents 

0.000485 Yes Yes 

Siebert and Wei (1994) 

UK General Household 

Survey 1983 

Health and Safety Executive (HSE) 1986-88 

0.000038 Yes No 

Lanoie, Pedro, and Latour (1995) 

Canada Authors' in-person 

survey 1990 

Quebec Workers' Compensation Board 1981-1985 

0.000126 Yes No 

Sandy and Elliott (1996) 

UK Social Change and 

Economic Life Initiative Survey (SCELI) 1986 

OPCS Occupational Mortality Tables Decennial Supplement 1979/80-1982/3 

0.000045 No No 

Shanmugam (1996/7) 

India Author's survey of blue 

collar manufacturing workers, Madras, India 1990 

Administrative Report of Factories Act 1987-1990 

0.000104 No No 

Liu, Hammitt, and Liu (1997) 

Taiwan Taiwan Labor Force 

Survey 1982-1986 

Taiwan Labor Insurance Agency 1982-1986 

0.000225- 0.000382 

No No $5,0

Miller, Mulvey, and Norris (1997) 

Australia Australian Census of 

Population and Housing 1991 

Worksafe Australia, National Occupational Health and Safety Commission 1992-93 

0.000068 No No 

Siebert and Wei (1998) 

Hong Kong Hong Kong Census 1991 Labour Department 0.000139 No No 

Liu and Hammitt (1999) 

Taiwan Authors' survey of 

petrochemical workers 1995 

Workers' assessed fatality risk at work 1995 

0.000513 Yes No 
Author (Year) Country Sample Risk Variable Mean Risk 

Nonfatal 

Risk 

Included?

Workers' 

Comp 

Included?

Ave

(2

Meng and Smith (1999) 

Canada Labour Market Activity 

Survey 1986 

Ontario Workers' Compensation Board 

0.00018 Yes Yes 

Arabsheibani and Marin (2000) 

UK General Household 

Survey (1980s) 

OPCS Occupational Mortality Decennial Survey 1979-83 

0.00005 Yes No 

Shanmugam (2000) India Author's survey of blue 

collar manufacturing workers, Madras, India 1990 

Administrative Report of Factories Act 1987-1990 

0.000104 Yes No 

Shanmugam (2001) India Author's survey of blue 

collar manufacturing workers, Madras, India 1990 

Administrative Report of Factories Act 1987-1990 

0.000104 Yes No 

Sandy, Elliott, Siebert, and Wei (2001) 

UK SCELI 1986 OPCS 79/80 - 82/3, HSE 

1986 - 88 

0.000038, 0.000045 No No 
Table 5a. Summary of Labor Market Studies of the Value of Statistical Injury, United States 

Author 

(Year) Sample Risk Variable Mean Injury Risk Fatal Risk Included? Workers' Comp Included? Average Income Level (2000 US$) Imp

Smith (1974) Current Population Survey 

(CPS) 1967, Census of Manufactures 1963, U.S. Census 1960, Employment and Earnings 1963 

Bureau of Labor Statistics (BLS) 1966, 1967 

NA Yes, 

significant 

No $29,029 -$30,93

Smith (1976) CPS 1967, 1973 BLS 1966, 1967, 

1970 

NA Yes, 

significant 

No $31,027 nonfata

Viscusi (1978a, 1979) 

Survey of Working Conditions, 1969-1970 (SWC) 

BLS non-fatal injury rate 1969 (preOSHA) 

0.032 Yes, 

significant 

No $31,842 $25,69

Viscusi (1978b) 

SWC 1969-1970 BLS non-fatal injury 

rate 1969 (preOSHA) 

0.032 No No $32,675 $61,53

McLean, Wendling, and Neergaard (1978) 

Wisconsin Census 1970 Wisconsin Workers' 

Compensation accident data 1970 

0.05 No No $34,414 $141,6

Viscusi (1981) 

Panel Study of Income Dynamics (PSID) 1976 

BLS non-fatal injury rate 1976 

0.032 Yes, 

significant 

No $22,618 $59,23

Olson (1981) CPS 1978 BLS total lost 

workday accident rate 1973 

0.035 Yes, 

significant 

No $36,151 $24,00
Author 

(Year) Sample Risk Variable Mean Injury Risk Fatal Risk Included? Workers' Comp Included? Average Income Level (2000 US$) Imp

Freeman and Medoff (1981) 

CPS May 1973 - 1975 BLS mean lost 

workdays per worker 1972 - 1974 

0.701 No No NA Not rep

and sta

Leigh (1981) PSID 1974, QES 1977 BLS injury rates 

1974 

NA Yes, not 

significant 

No NA Not rep

and sta

Butler (1983) S.C. Workers' 

Compensation Data 1940-69 

S.C. Workers' Compensation Claims Data 

.061 

(claims 

rate) 

No Yes $22,713 $936/d

Dorsey and Walzer (1983) 

CPS May 1978 BLS nonfatal lost 

workday injury incidence rate 1976 

0.03 Yes, some 

specifications 

Yes $21,636 $60,58

Smith (1983) CPS 1978 BLS Work Injury 

Rate 

0.078 No No $32,488 $35,48

Dorsey (1983) 

Employers' Expenditures for Employee Compensation survey 1977, BLS May 1978, BLS May 1979 

BLS 0.036 Yes, not 

significant 

Yes, in some specifications 

$33,019 $102,3

Leigh and Folsom (1984) 

PSID 1974; Quality of Employment Survey (QES) 1977 

BLS nonfatal injury rate 

0.074, 0.066 

Yes No $29,038, $36,946 $99,43

Viscusi and O'Connor (1984) 

Author's chemical worker survey, 1982 

Workers' assessed injury and illness rate 

0.1 No No $37,642 $17,70
Author 

(Year) Sample Risk Variable Mean Injury Risk Fatal Risk Included? Workers' Comp Included? Average Income Level (2000 US$) Imp

Dickens (1984) 

CPS May 1977 BLS industry data 

1977 

NA Yes No NA Not rep

Viscusi and Moore (1987) 

QES 1977 BLS lost workday 

injury rate, BLS total injury rate 

0.038, 0.097 

No Yes $43,503 $70,65

for nonacciden

Biddle and Zarkin (1988) 

QES 1977 BLS nonfatal lost 

workday injury incident rate, 1977 

0.037 No No $42,170 $168,6

$155,5

Garen (1988) PSID 1981-1982 BLS nonfatal injury 

rate, 1980-1981 

NA Yes No $29,865 $26,95

Moore and Viscusi (1988b) 

QES 1977 BLS annual 

incidence rate of lost workday cases 1973-1976 

0.047 Yes, 

significant 

Yes $31,092 $36,81

Hersch and Viscusi (1990) 

Authors' survey in Eugene, OR, 1987 

Workers' assessed injury rate using BLS lost workday incidence rate scale 

0.059 No No (one state) $21,897 $72,42

(smoke

Viscusi and Evans (1990) 

Viscusi and O'Connor chemical worker survey 

Utility function estimates using assessed injury and illness rate 

0.1 No No $37,802 $23,78

(certain 

Hamermesh and Wolfe (1990) 

PSID 1981 BLS 1980, 1981 incidence: 

0.0476; 

duration: 

0.1571 

No Yes NA Not rep

and sta
Author 

(Year) Sample Risk Variable Mean Injury Risk Fatal Risk Included? Workers' Comp Included? Average Income Level (2000 US$) Imp

Kniesner and Leeth (1991) 

CPS 1978 BLS lost workday 

injury rate 

0.055 Yes Yes $33,627 $60,62

French and Kendall (1992) 

CPS 1980 (railroad industry only) 

Federal Railroad Administration Injury Rate 

0.048 No No $46,284 $48,92

Fairris (1992) SWC 1969-1970 BLS 1969 industry 

injury frequency rate

0.032 No No $33,850 $49,29

Hersch and Pickton (1995) 

National Medical Expenditure Survey 1987 

BLS total lost workdays per worker per year 1987 

0.702† No No $26,345 $120,7

(nonsm(smoke

Dillingham, Miller, and Levy (1996) 

QES 1977 constructed a risk 

measure 

NA Yes No $24,267 $155,4

year of 

Dorman and Hagstrom (1998) 

PSID 1982 BLS 1981 0.052 Yes Yes $32,243 Nonfat

Hersch (1998) 

CPS March 1994 BLS 1993 (number 

of cases of days away from work) 

0.029 

whole 

sample; 

0.022 

females 

No No $28,004 $22,81

$36,19
Author 

(Year) Sample Risk Variable Mean Injury Risk Fatal Risk Included? Workers' Comp Included? Average Income Level (2000 US$) Imp

Viscusi and Hersch (2001) 

National Medical Expenditure Survey 1987 

BLS 1987 injury rate and lost workdays rate 

injury rate: 

0.042 

nonsmoker; 

0.49 

smoker 

No No $31,651 

(nonsmokers); $28,316 (smokers) 

$47,47$31,02

† Note that the measure used in the Hersch and Pickton study – probability of losing 1 workday due to injury per worker per year – diffin most other studies – probability of a lost workday injury per worker per year. 
Table 5b. Summary of Labor Market Studies of the Value of Statistical Injury, International 

Author (Year) Country Sample Risk Variable Mean Injury Risk Fatal Risk Included? Workers' Comp Included? Average Income Level (2000 US$) 

Cousineau, Lacroix, and Girard (1992) 

Canada Labor, Canada Survey 

1979 

Quebec Compensation Board 

NA Yes No $29,665 

Martinello and Meng (1992) 

Canada Labour Market Activity 

Survey 1986 

Labour Canada and Statistics Canada 1986 

0.063 Yes No $25,387 $1

$1sev

Siebert and Wei (1994) 

UK General Household 

Survey 1983 

Health and Safety Executive (HSE) 1986-88 data 

0.0143 Yes No $12,810 co

sigsp

Lanoie, Pedro, and Latour (1995) 

Canada Authors' in-person 

survey 1990 

Quebec Workers' Compensation Board 1981-1985 

0.099 Yes No $40,739 

Liu and Hammitt (1999) 

Taiwan Authors’ survey of 

petrochemical workers 1999 

Workers’ assessed injury risk at work 1995 

0.0109 Yes No $18,483 

Meng and Smith (1999) 

Canada Labour Market Activity 

Survey 1986 

Ontario Workers' Compensation Board 

3.01 -- 

workdays 

lost per 

worker 

Yes Yes $19,962 

Shanmugam (2000) 

India Author's survey of blue 

collar manufacturing workers, Madras, India 1990 

Administrative Report of Factories Act 1987-1990 

0.0729 Yes No $778 

Shanmugam (2001) 

India Author's survey of blue 

collar manufacturing workers, Madras, India 1990 

Administrative Report of Factories Act 1987-1990 

0.0729 Yes No $778 
104

Table 6. Replication of Published Meta-Analyses with Viscusi-Aldy Data 

Variable 

Viscusi-Aldy 

Version of Liu 

et al. (1997) 

Equation 2 

(1) 

Viscusi-Aldy 

Version of Miller 

(2000) Model 3 

(2) 

Viscusi-Aldy 

Version of BowlandBeghin (2001) 

Linear Model 

(3) 

Viscusi-Aldy Version 

of Mrozek-Taylor 

(2002) Model 2 

(4) 

Dependent 

Variable 

log(VSL) log(VSL) VSL (Expressed in 

Per Hour Terms) 

log(VSL) 

log(Income) 0.51* 

(0.15) 

0.53* 

(0.17) - - 

 

Income - - 0.078** 

(0.031) - 

 

Hourly Wage - - - 0.040 

(0.026) 

 

Mean Risk -0.015* 

(0.0057) - -19.34** 

(8.56) 

-0.16*** 

(0.075) 

 

Mean Risk 

Squared - - - 0.0019 

(0.0015) 

 

Union x 

Mean Risk - - - 0.22*** 

(0.11) 

 

Dillingham 

Risk - - - -0.32 (0.58) 

 

Society of 

Actuaries Risk - -1.29* 

(0.28) - - 

 

BLS Risk - - 1445.18** 

(591.47) - 

 

NIOSH Risk - - - 0.27 

(0.40) 

 

Education 

Level - - -157.14 (192.32) - 

 

Unemployment 

Rate - - - 0.045 (0.048) 

 

U.S. National 

Data - - - 0.31 (0.82) 

 

Non-U.S. 

Study - - - -0.0048 (0.81) 

 

Union VSL - - 2995.05* 

(1086.09) 

-0.42 

(0.90) 

 

Union Dummy 

Variable - - 1000.76 (638.37) 

0.43 

(0.40) 
105

Variable 

Viscusi-Aldy 

Version of Liu 

et al. (1997) 

Equation 2 

(1) 

Viscusi-Aldy 

Version of Miller 

(2000) Model 3 

(2) 

Viscusi-Aldy 

Version of BowlandBeghin (2001) 

Linear Model 

(3) 

Viscusi-Aldy Version 

of Mrozek-Taylor 

(2002) Model 2 

(4) 

Dependent 

Variable 

log(VSL) log(VSL) VSL (Expressed in 

Per Hour Terms) 

log(VSL) 

 

Male Only 

Sample - - -588.20 

(600.82) - 

 

Blue Collar 

Sample - - -812.78 (644.54) 

-0.68 

(0.49) 

 

Quadratic Risk - - - 0.54 

(0.33) 

 

Morbidity 

Variable 

Included 

- - - 

0.11 

(0.32) 

 

log(Dependent 

Variable) - - - -0.24 (0.36) 

 

Regional 

Dummy 

Variable 

- - 

-757.70 

(601.16) 

0.16 

(0.35) 

 

Urban Dummy 

Variable - - -65.91 

(804.50) 

0.12 

(0.41) 

 

Workers’ 

Compensation - - - 0.10 (0.45) 

 

Wage in After 

Tax Terms - - - -0.29 

(0.50) 

 

Industry 

Dummy 

Variable 

- - - 

0.081 

(0.27) 

 

Occupation 

Dummy 

Variable 

- - - 

0.0039 

(0.20) 

 

No Occupation 

Dummy 

Variable 

- 

0.48 

(0.33) - - 

 

Job 

Characteristics 

Dummy 

Variable 

- - - 

-0.021 

(0.51) 

 

Constant 10.56* 

(1.49) 

9.80* 

(1.78) 

1935.54 

(1506.92) 

15.68* 

(1.39) 
106

Variable 

Viscusi-Aldy 

Version of Liu 

et al. (1997) 

Equation 2 

(1) 

Viscusi-Aldy 

Version of Miller 

(2000) Model 3 

(2) 

Viscusi-Aldy 

Version of BowlandBeghin (2001) 

Linear Model 

(3) 

Viscusi-Aldy Version 

of Mrozek-Taylor 

(2002) Model 2 

(4) 

Dependent 

Variable 

log(VSL) log(VSL) VSL (Expressed in 

Per Hour Terms) 

log(VSL) 

 

R2 0.37 0.27 - 0.83 

 

n 46 49 45 41 

Specifications (1), (2), and (4) estimated with ordinary least squares. 

Specification (3) estimated with robust regression with Huber weights. 

Robust (White) standard errors are presented in parentheses for specifications (1), (2), and (4). 

Asymptotic standard errors presented in parentheses for specification (3). 

* Indicates statistical significance at 1 percent level. 

** Indicates statistical significance at 5 percent level. 

*** Indicates statistical significance at 10 percent level. 
107

Table 7. Income Elasticity of Willingness-to-Pay to Reduce Mortality Risk 

Paper (Model) Reported Elasticity in 

Authors’ Papers Viscusi-Aldy Version 

Liu et al. (1997) 

(Equation 2) 

0.53 0.51* 

(0.21 – 0.80) 

 

Miller (2000) 

(Model 3) 

0.89* 0.53* 

(0.20 – 0.86) 

Mrozek and Taylor (2002) 

(Model 2) 

0.46** 0.52 

(-0.18 – 1.22) 

 

Bowland and Beghin (2001) 

(Linear Model) 

1.66* 0.61** 

(0.11 – 1.10) 

* Indicates elasticity is based on coefficient that is statistically significant at 1 percent 

level. 

** Indicates elasticity is based on coefficient that is statistically significant at 5 percent 

level. 

Ranges in parentheses represent the 95 percent confidence interval around the point 

estimate for the income elasticity. 
108

Table 8. Regression Models for Viscusi-Aldy Meta-Analysis 

Variable (1) (2) (3) (4) (5) (6) 

Regression 

Technique OLS OLS OLS Robust with 

Huber weights 

Robust with 

Huber weights 

Robust with 

Huber weights 

log(Income) 0.51* 

(0.15) 

0.49* 

(0.13) 

0.60* 

(0.16) 

0.48* 

(0.12) 

0.46* 

(0.11) 

0.47* 

(0.15) 

Mean Risk -0.015** 

(0.0057) 

-0.053* 

(0.011) 

-0.045* 

(0.015) 

-0.029* 

(0.0061) 

-0.090* 

(0.018) 

-0.11* 

(0.029) 

Mean Risk Squared - 0.00022* 

(0.000052) 

0.00016* 

(0.000055) 

- 0.00065* 

(0.00019) 

0.0010** 

(0.00035) 

Society of Actuaries 

Risk 

- - 0.50 

(0.87) 

- - dropped 

 

NIOSH Risk - - 0.50 

(0.41) 

- - 0.56 

(0.41) 

Subjective - - -0.69 

(0.78) 

- - -0.16 

(0.98) 

Union Dummy 

Variable 

- - 0.44 

(0.32) 

- - 0.50 

(0.30) 

 

Male Only Sample - - 0.24 

(0.36) 

- - 0.36 

(0.34) 

 

Blue Collar Sample - - -0.016 

(0.31) 

- - -0.23 

(0.33) 

 

Quadratic Risk - - 0.092 

(0.27) 

- - 0.20 

(0.34) 

 

Morbidity Variable 

Included 

- - 0.55*** 

(0.30) 

- - 0.62** 

(0.30) 

 

log(Dependent 

Variable) 

- - 0.17 

(0.31) 

- - 0.13 

(0.41) 

Regional Dummy 

Variable 

- - -0.16 

(0.25) 

- - 0.16 

(0.31) 

Urban Dummy 

Variable 

- - 0.38 

(0.29) 

- - 0.087 

(0.39) 

Workers’ 

Compensation 

- - -0.57 

(0.33) 

- - -0.28 

(0.38) 

Industry Dummy 

Variable 

- - -0.46 

(0.27) 

- - -0.37 

(0.28) 

Occupation Dummy 

Variable 

- - -0.45 

(0.31) 

- - -0.24 

(0.33) 

Constant 10.56* 

(1.49) 

11.22* 

(1.30) 

9.58* 

(1.82) 

11.05* 

(1.23) 

11.83* 

(1.12) 

11.22* 

(1.68) 

R2

 0.37 0.55 0.72 - - - 

 

n 46 46 46 45 45 44 

Income Elasticity 

(95% Confidence 

Interval)

0.51 

(0.21 – 0.80) 

0.49 

(0.23 – 0.75) 

0.60 

(0.27 – 0.94) 

0.48 

(0.23 – 0.73) 

0.46 

(0.24 – 0.69) 

0.47 

(0.15 – 0.78) 

Mean Predicted VSL, 

Full Sample (millions 

2000 US$) (95% 

Confidence Interval) 

5.0 

(3.4 – 7.4) 

5.4 

(3.7 – 7.8) 

5.9 

(2.7 – 13.9) 

5.5 

(4.1 – 7.5) 

5.9 

(4.4 – 8.1) 

6.2 

(2.5 – 15.7) 

Mean Predicted VSL, 

U.S. Sample (millions 

2000 US$) (95% 

5.5 

(3.8 – 8.1) 

5.8 

(4.1 – 8.3) 

6.9 

(3.1 – 16.2) 

6.1 

(4.6 – 8.2) 

6.3 

(4.8 – 8.4) 

7.6 

(3.0 – 19.4) 
109

Variable (1) (2) (3) (4) (5) (6) 

Regression 

Technique OLS OLS OLS Robust with 

Huber weights 

Robust with 

Huber weights 

Robust with 

Huber weights 

Confidence Interval) 

Dependent variable: log(VSL) 

Robust (White) standard errors presented in parentheses for specifications 1 – 3. 

Asymptotic standard errors presented in parentheses for specifications 4 – 6. 

* Indicates statistical significance at 1 percent level. 

** Indicates statistical significance at 5 percent level. 

*** Indicates statistical significance at 10 percent level. 

Note: Estimation with robust standard errors clustered by wage data source yields same significance levels, with the 

exception of Mean Risk in (1) and (3) and Mean Risk Squared in (3), which are all significant at the 2 percent level, and the 

industry dummy variable in (3) which is significant at the 8 percent level. 
Table 9a. Summary of Union Effects in Value of a Statistical Life Studies, United States 

Author (Year) Sample Risk Variable Mean Risk 

Nonfatal 

Risk 

Included? 

Workers' 

Comp 

Included? 

Average 

Income Level 

(2000 US$) 

Union Implicit 

VSL 

(millions, 2000 

US$) 

I

(m

Thaler and Rosen (1975) 

Survey of Economic Opportunity 1967 

Society of Actuaries 1967 

0.001 No No $34,663 $1.7-$1.9 

Viscusi (1980) SWC 1969-1970 BLS 3-digit industry 

data for 1969 

0.000118 Yes No $31,842 $5.5-$15.2 N

Olson (1981) CPS 1978 BLS 1973 0.0001 Yes, 

significant 

No $36,151 $30.6-$44.2 

Dorsey (1983) Employers' Expenditures 

for Employee Compensation survey 1977, BLS May 1978, BLS May 1979 

BLS 0.000071 Yes, 

significant 

Yes, in some 

specifications 

$33,019 $9.6 

Dorsey and Walzer (1983) 

CPS May 1978 BLS 1976 0.000052 Yes, 

significant 

Yes $21,636 $11.8, $12.3 

Dillingham and Smith (1984) 

CPS May 1979 BLS industry data 1976, 

1979; NY Workers' Comp Data 1970 

0.000082 Yes, 

significant in 

some 

specifications

No $29,707 $6.1, $6.6 

Garen (1988) PSID 1981-1982 BLS 1980, 1981 0.000108 Yes, 

significant 

No $29,865 Insufficient 

information 

provided to 

calculate 

Gegax, Gerking, and Schulze (1991) 

Authors' mail survey 1984 

Workers' assessed fatality risk at work 1984 

0.0009 No No $41,391 $2.7 N
Author (Year) Sample Risk Variable Mean Risk 

Nonfatal 

Risk 

Included? 

Workers' 

Comp 

Included? 

Average 

Income Level 

(2000 US$) 

Union Implicit 

VSL 

(millions, 2000 

US$) 

I

(m

Kim and Fishback (1993) 

Interstate Commerce Commission data 1893 - 1909, 1926 - 32, 1934 – 45 

ICC Fatality Data 1890 - 1945 

0.0006 - 0.0018 

Yes No NA No statistical 

difference 

between union 

and nonunion 

members 

Dorman and Hagstrom (1998) 

PSID 1982 BLS 1979-1981, 1983, 

1985, 1986; NIOSH NTOF 1980-1988 

0.000123 - 0.0001639 

Yes Yes $32,243 $18.1 
Table 9b. Summary of Union Effects in Value of Statistical Injury Studies, United States 

Author (Year) Sample Risk Variable Mean Risk Fatal Risk Included? Workers' Comp Included? Average Income Level (2000 US$) Union Implicit Value of a Statistical Injury (2000 US$) Nonunion Imp

Value of a Statistical Inju

(2000 US$

Biddle and Zarkin (1988) 

QES 1977 BLS nonfatal lost 

workday injury incident rate, 1977 

0.037 No No $42,170 $319,678 $56,832 

Freeman and Medoff (1981) 

CPS May 1973 – 1975 

BLS mean lost workdays per worker 1972 - 1974

0.701* No No NA No statistical 

difference between 

union and 

nonunion members

 

Smith (1983) CPS 1978 BLS Work Injury 

Rate 

0.078 No No $38,138 (males); 

$25,672 (females) 

$74,368 (males); 

$59,815 (females)

$46,528 (male

$25,672 (fema

Fairris (1992) SWC 1969-1970 BLS 1969 industry 

injury frequency rate 

0.032 No No $33,850 $246,449 Not significa

Hamermesh and Wolfe (1990) 

PSID 1981 BLS 1980, 1981 incidence: 

0.0476; 

duration: 

0.1571 

No Yes NA Insufficient 

information 

provided to 

calculate 

Positive, bu

smaller than un

value of inju

Hersch and Pickton (1995) 

National Medical Expenditure Survey 1987 

BLS total lost workdays per worker per year 1987 

0.702* No No $26,345 $222,448 $99,478 

*Lost workdays per 100 full-time workers. 
Table 9c. Summary of Union Effects in Value of a Statistical Life Studies, International 

Author (Year) Country Sample Risk Variable Mean Risk Nonfatal Risk Included? Workers' Comp Included? Average Income Level (2000 US$) Union Implicit VSL (millions, 2000 US$) Nonu

Implici

(mill

2000 

Marin and Psacharopoulos (1982) 

UK General Household 

Survey 1975 

OPCS Occupational Mortality Decennial Survey 1970-72 

0.0001 No No $14,472 No statistical 

difference 

between union 

and nonunion 

members 

 

Meng (1989) Canada National Survey of 

Class Structure and Labour Process 1981 

Labour Canada and Quebec Occupational Health and Safety Board 1981 

0.00019 No No $43,840 No statistical 

difference 

between union 

and nonunion 

members 

 

Meng and Smith (1990) 

Canada National Election 

Study 1984 

Labour Canada and Quebec Occupational Health and Safety Board 1981-83 

0.00012 No No $29,646 No statistical 

difference 

between union 

and nonunion 

members 

 

Meng (1991) Canada Labour Market 

Activity Survey 1986 

Labour Canada and Workers' Compensation Boards 

0.00023 for 

accidents, 

0.00012 for 

disease 

No No $25,387 $1.9 $4

Cousineau, Lacroix, and Girard (1992) 

Canada Labor, Canada 

Survey 1979 

Quebec Compensation Board 

0.00001 Yes No $29,665 $4.4 $2

Martinello and Meng (1992) 

Canada Labour Market 

Activity Survey 1986 

Labour Canada and Statistics Canada 1986 

0.00025 Yes No $25,387 No statistical 

difference 

between union 

and nonunion 

members 

 
Author (Year) Country Sample Risk Variable Mean Risk Nonfatal Risk Included? Workers' Comp Included? Average Income Level (2000 US$) Union Implicit VSL (millions, 2000 US$) Nonu

Implici

(mill

2000 

Siebert and Wei (1994) 

UK General Household 

Survey 1983 

Health and Safety Executive (HSE) 1986-88 

0.0000379 Yes No $12,810 $11.5 $9

Lanoie, Pedro, and Latour (1995) 

Canada Authors' in-person 

survey 1990 

Quebec Workers' Compensation Board 1981-1985 

0.000126 Yes No $40,739 $19.6, $21.7 No

signif

Sandy and Elliott (1996) 

UK Social Change and 

Economic Life Initiative Survey (SCELI) 1986 

OPCS Occupational Mortality Tables Decennial Supplement 1979/80-1982/3 

4.4831E-05 No No $16,143 $69.0 $69

Shanmugam (1996) 

India Author's survey of 

blue collar manufacturing workers, Madras, India 1990 

Administrative Report of Factories Act 1987-1990 

0.000104 No No $778 $0.6 No

signif

Kim and Fishback (1999) 

South Korea 

Ministry of Labor's Report on Monthly Labor Survey and Survey on Basic Statistics for the Wage Structures 

Ministry of Labor's Analysis for Industrial Accidents 

0.000485 Yes Yes $8,125 No statistical 

difference 

between union 

and nonunion 

members 

 
Author (Year) Country Sample Risk Variable Mean Risk Nonfatal Risk Included? Workers' Comp Included? Average Income Level (2000 US$) Union Implicit VSL (millions, 2000 US$) Nonu

Implici

(mill

2000 

Arabsheibani and Marin (2000) 

UK General Household 

Survey (1980s) 

OPCS Occupational Mortality Decennial Survey 1979-83 

reported as 

-0.006 

Yes No $20,163 No statistical 

difference 

between union 

and nonunion 

members 

 

Sandy, Elliott, Siebert, and Wei (2001) 

UK SCELI 1986 OPCS 79/80 - 

82/3, HSE 1986 - 88 

0.000038, 0.000045 

No No $16,143 $5.7, $44.8 $36.1, 
 Table 10. Summary of Age-Risk Interaction Effects in Value of a Statistical Life Studies, United States 

Author (Year) Sample Average Age of Sample Risk Variable Age x Risk 

Thaler and Rosen (1975) 

Survey of Economic Opportunity 1967 

41.8 Society of Actuaries 1967 Significant 

Viscusi (1979) Survey of Working Conditions, 

1969-1970 (SWC) 

39.7 BLS 1969, subjective risk of 

job (SWC) 

Significant 

Portney (1981) Property values in Allegheny 

County, PA 1978 

NA Air pollution annual mortality 

rate data, EPA 

VSL by age than 45: $164: $0.3 (0.$0.05 (0.05

Arnould and Nichols (1983) 

U.S. Census 1970 NA Society of Actuaries 1967 Significant 

Moore and Viscusi (1988b) 

QES 1977 38.1 Expected life years lost (=BLS 

fatality risk x discounted remaining life) 

Significant (implies riswith life exwith age, ce

Meng (1989) National Survey of Class 

Structure and Labour Process 1981 

39.9 Labour Canada and Quebec 

Occupational Health and Safety Board 1981 

Significant 

Meng and Smith (1990) 

National Election Study 1984 NA Labour Canada and Quebec 

Occupational Health and Safety Board 1981-83 

Insignifican

Dillingham, Miller, and Levy (1996) 

QES 1977 36.5 Constructed a risk measure Declining s

impaired wage 
Author (Year) Sample Average Age of Sample Risk Variable Age x Risk 

Shanmugam (1996/7) Author's survey of blue collar 

manufacturing workers, Madras, India 1990 

34.1 Administrative Report of 

Factories Act 1987-1990 

Insignifican

Shanmugam (2001) Author's survey of blue collar 

manufacturing workers, Madras, India 1990 

34.1 Administrative Report of 

Factories Act 1987-1990 

Insignifican
118

Table 11. Summary of Imputed Discount Rate Studies, United States 

Year Author (Year) Type of Study Sample Implicit 

Discount Rate

1988 Moore and 

Viscusi 

(1988b) 

Labor Hedonic with 

Reduced Form 

Discounting Model 

QES 1977 9.6%-12.2% 

1989 Viscusi and 

Moore (1989) 

Labor Hedonic with 

Structural Markov 

Model 

PSID 1982 10.7% 

1990 Moore and 

Viscusi 

(1990b) 

Labor Hedonic with 

Structural Life Cycle 

Model 

PSID 1982 2% 

1990 Moore and 

Viscusi 

(1990c) 

Labor Hedonic with 

Structural Integrated 

Life Cycle Model 

PSID 1982 1.0%-14.2% 

1995 Dreyfus and 

Viscusi (1995) 

Automobile Hedonic 1988 Residential 

Transportation Energy 

Consumption Survey 

11%-17% 
119

Table 12. Values of Statistical Life Used by U.S. Regulatory Agencies, 1985 – 2000* 

Year Agency Regulation 

Value of a 

Statistical Life 

(millions, 2000 $) 

1985 Federal Aviation 

Administration 

Protective Breathing Equipment (50 Federal Register 

41452) 

$1.0** 

1985 Environmental 

Protection Agency 

Regulation of Fuels and Fuel Additives; Gasoline Lead 

Content (50 FR 9400) 

$1.7 

1988 Federal Aviation 

Administration 

Improved Survival Equipment for Inadvertent Water 

Landings (53 FR 24890) 

$1.5** 

1988 Environmental 

Protection Agency 

Protection of Stratospheric Ozone (53 FR 30566) $4.8 

1990 Federal Aviation 

Administration 

Proposed Establishment of the Harlingen Airport Radar 

Service Area, TX (55 FR 32064) 

$2.0** 

1994 Food and Nutrition 

Service (USDA) 

National School Lunch Program and School Breakfast 

Program (59 FR 30218) 

$1.7, $3.5** 

1995 Consumer Product 

Safety Commission 

Multiple Tube Mine and Shell Fireworks Devices (60 

FR 34922) 

$5.6** 

1996 Food Safety 

Inspection Service 

(USDA) 

Pathogen Reduction; Hazard Analysis and Critical 

Control Point Systems (61 FR 38806) 

$1.9 

1996 Food and Drug 

Administration 

Regulations Restricting the Sale and Distribution of 

Cigarettes and Smokeless Tobacco to Protect Children 

and Adolescents (61 FR 44396) 

$2.7** 

1996 Federal Aviation 

Administration 

Aircraft Flight Simulator Use in Pilot Training, 

Testing, and Checking and at Training Centers (61 FR 

34508) 

$3.0** 

1996 Environmental 

Protection Agency 

Requirements for Lead-Based Paint Activities in 

Target Housing and Child-Occupied Facilities (61 FR 

45778) 

$6.3 

1996 Food and Drug 

Administration 

Medical Devices; Current Good Manufacturing 

Practice Final Rule; Quality System Regulation (61 FR 

52602) 

$5.5** 

1997 Environmental 

Protection Agency 

National Ambient Air Quality Standards for Ozone (62 

FR 38856) 

$6.3 

1999 Environmental 

Protection Agency 

Radon in Drinking Water Health Risk Reduction and 

Cost Analysis (64 FR 9560) 

$6.3 

1999 Environmental 

Protection Agency 

Control of Air Pollution from New Motor Vehicles: 

Tier 2 Motor Vehicle Emissions Standards and 

Gasoline Sulfur Control Requirements (65 FR 6698) 

$3.9, $6.3 

2000 Consumer Product 

Safety Commission 

Portable Bed Rails; Advance Notice of Proposed 

Rulemaking (65 FR 58968) 

$5.0** 

* This table augments a similar presentation of values of a statistical life used in U.S. regulatory analyses in 

Adler and Posner (2000) by including more regulations and presenting VSLs in constant year dollars. 

** The published summaries of the regulatory impact analyses for these rules do not specify the year in 

which the reported dollars are denominated. We have assumed that the dollar year corresponds to the date 

of rule publication for purposes of converting all values into 2000 dollars. Note that the CPSC reported a 

VSL of $5 million in both its 1995 and 2000 regulations; the difference in values reflects our deflating to 

2000 dollars. 
120

Table 13. Evaluation of Risk-Risk Tradeoff for 24 U.S. Regulations, 1986 – 1998 

Regulation Year Agency 

Discounted 

Statistical Lives 

Saved 

Fatalities 

Induced by Cost 

of Regulations 

Net Lives 

Saved by 

Regulations 

Toxicity characteristics to determine 

hazardous wastes 

1990 EPA 0.048 -23 23 

Underground storage tanks: technical 

requirements 

1988 EPA 1.1 -22 24 

Manufactured home construction and 

safety standards on wind standards 

1994 HUD 1.5 -3.2 4.7 

Process safety management of highly 

hazardous chemicals 

1992 DOL 220 -42 260 

Regulations restricting the sale and 

distribution of cigarettes and 

smokeless tobacco to protect children 

and adolescents 

1996 HHS 4,700 -140 4,900 

Medicare and Medicaid programs: 

hospital conditions of participation; 

identification of potential organ, 

tissue, and eye donors; and transplant 

hospitals' provision of transplantrelated data 

1998 HHS 710 9.2 700 

Quality mammography standards 1997 HHS 75 1.4 74 

Food labeling regulations 1993 HHS 520 10 510 

Childproof lighters 1993 CPSC 95 2.9 92 

Standard for occupational exposure to 

benzene 

1987 DOL 4.4 1.8 2.6 

Occupational exposure to methylene 

chloride 

1997 DOL 12 5.9 6.2 

Occupational exposure to 4,4' 

methylenedianiline 

1992 DOL 0.7 0.71 -0.01 

Asbestos: manufacture, importation, 

processing, and distribution in 

commerce -- prohibitions (total) 

1989 EPA 3.9 4.3 -0.41 

National primary and secondary 

water regulations -- phase II: 

maximum contaminant levels for 38 

contaminants 

1991 EPA 44 63 -19 

Occupational exposure to asbestos 1994 DOL 13 20 -7.1 
121

Regulation Year Agency 

Discounted 

Statistical Lives 

Saved 

Fatalities 

Induced by Cost 

of Regulations 

Net Lives 

Saved by 

Regulations 

Hazardous waste management system 

-- wood preservatives 

1990 EPA 0.29 0.83 -0.55 

Sewage sludge use and disposal 

regulations, 40 CFR pt. 503 

1993 EPA 0.24 2.6 -2.3 

Land disposal restrictions for "third 

third" scheduled wastes 

1990 EPA 2.8 30 -27 

Hazardous waste management 

system: final solvents and dioxins 

land disposal restrictions rule 

1986 EPA 1 12 -11 

Occupational exposure to 

formaldehyde 

1987 DOL 0.21 4.8 -4.5 

Prohibit the land disposal of the first 

third of scheduled wastes ("second 

sixth" proposal) 

1988 EPA 2.9 66 -63 

Land disposal restrictions -- phase II: 

universal treatment standards and 

treatment standards for organic 

toxicity, characteristic wastes, and 

newly listed wastes 

1994 EPA 0.16 8.3 -8.2 

Drinking water regulations, synthetic 

organic chemicals -- phase V 

1992 EPA 0.0061 3.4 -3.4 

Solid waste disposal facility criteria, 

40 CFR pt. 257 and pt. 258 

1991 EPA 0.0049 10 -10 

Source: Hahn, Lutter, and Viscusi (2000).
122

Appendix 

Table A. Description of Variables Used in Viscusi-Aldy Meta-Analyses 

Variable Description Summary Statistic, 

Viscusi-Aldy data 

VSL Value of a statistical life (millions, 2000 US$) $6.7 ($5.6) 

Income Annual labor income (2000 US$) $26,006 ($12,002) 

Mean Risk Average mortality risk of sample 0.0002 (0.0003) 

Hourly Wage Hourly wage or hourly equivalent of weekly income (2000 

US$) 

$13.00 ($6.00) 

Union VSL VSL for union members only (dummy variable) 4/49 

Dillingham Risk VSL based on Dillingham (1985) constructed New York 

workers compensation-based fatality risk measure (d.v.) 

1/49 

Society of Actuaries 

Risk 

VSL based on Society of Actuaries 1967 mortality risk data 

(d.v.) 

2/49 

BLS Risk VSL based on BLS mortality risk measure (d.v.) 16/49 

NIOSH Risk VSL based on NIOSH mortality risk measure (d.v.) 5/49 

Subjective VSL based on self-reported measure of mortality risk (d.v.) 1/49 

Education Level Barro-Lee average educational attainment for population > 

25 by country (in years) 

9.6 (2.0) 

Unemployment Rate Annual unemployment rate by year of wage data 7.6 (4.6) 

U.S. National Data VSL based on national U.S. worker sample (d.v.) 24/49 

Non-U.S. Study VSL based on non-US wage-risk study (d.v.) 22/49 

Male Only Sample VSL based on male only sample (d.v.) 21/49 

Blue Collar Sample VSL based on blue collar only sample (d.v.) 15/49 

Quadratic Risk VSL based on econometric specification quadratic in 

mortality risk (d.v.) 

7/49 

Morbidity Variable 

Included 

VSL based on study that included an injury risk measure 

(d.v.) 

25/49 

log(Dependent 

Variable) 

VSL derived from specification with natural logarithm of 

dependent variable (d.v.) 

44/49 

Union D.V. VSL based on specification that included dummy variable 

for union affiliation (d.v.) 

32/49 

Regional D.V. VSL based on specification with regional dummy variables 

(d.v.) 

24/49 

Urban D.V. VSL based on specification with urban/MSA dummy 

variable (d.v.) 

13/49 

Workers’ 

Compensation 

VSL based on specification with workers’ compensation 

variable (d.v.) 

11/49 

Wage in After Tax 

Terms 

VSL based on specification with income expressed in aftertax terms (d.v.) 10/49 

Industry D.V. VSL based on specification with industry dummy variables 

(d.v.) 

21/49 

Occupation D.V. VSL based on specification with occupational dummy 

variables (d.v.) 

20/49 

No Occupation D.V. Study does not include occupational dummy variables 29/49 
123

Variable Description Summary Statistic, 

Viscusi-Aldy data 

(dummy variable) 

Job Characteristics 

D.V. 

VSL based on specification with variables describing job 

characteristics (d.v.) 

10/49 

Means (standard deviations) reported for continuous variables. 

Share of studies in which variable equals 1 reported for dummy variables. 

n =49